MIAMI UNIVERSITY The Graduate School

Certificate for Approving the Dissertation

We hereby approve the Dissertation

of

Julia Simone Kaufman

Candidate for the Degree

DOCTOR OF PHILOSOPHY

______________________________________ Terri Messman-Moore, PhD, Director

______________________________________ Elise Clerkin, PhD, Reader

______________________________________ Aaron Luebbe, PhD, Reader

______________________________________ Kevin Bush, PhD, Graduate School Representative

ABSTRACT

PSYCHOLOGICAL MALTREATMENT SUBTYPES AND ASSOCIATED LONG-

TERM EFFECTS: A PERSON-CENTERED ANALYSIS

by

Julia S. Kaufman

Despite evidence indicating that psychological maltreatment (PM) has pernicious, long-

lasting effects, research on this form of child maltreatment has been slow to progress. PM

in childhood has been found to be a predictor of adult symptoms of anxiety and

depression, substance use, and substance-related problems. Although a range of abusive

and neglectful parenting behaviors can be considered psychological maltreatment,

different subtypes of PM are rarely assessed. The available research examining subtypes

of PM suggests that children experience distinct, and perhaps predictable, combinations

of PM subtypes. Yet, research has not explored how subtypes of PM naturally co-occur

or how these subtypes may differentially affect adult psychological functioning. Using

latent profile analysis, the present study explored the natural co-occurrence of PM

subtypes (i.e., terrorizing, spurning, exploiting/corrupting, isolating, and denying

emotional responsiveness) and differences in adult psychological functioning in a

community sample of 491 young women. Results indicated the best fit was a three-class

model reflecting exposure to low, moderate, and high PM, across PM subtypes. Distinct

PM groups were characterized by severity, but not subtype. Results also revealed

statistically significant differences between the three PM groups on symptoms of anxiety

and depression, substance use, and substance-related problems such that exposure to

more severe PM tended to be associated with greater symptom severity. Importantly, PM

did not occur in isolation as participants in the moderate and high PM groups reported

high rates of child physical and sexual abuse. Research and clinical implications are

discussed.

PSYCHOLOGICAL MALTREATMENT SUBTYPES AND ASSOCIATED LONG-

TERM EFFECTS: A PERSON-CENTERED ANALYSIS

A DISSERTATION

Presented to the Faculty of

Miami University in partial

fulfillment of the requirements

for the degree of

Doctor of Philosophy

Department of Department of Psychology

by

Julia S. Kaufman

The Graduate School

Miami University

Oxford, Ohio

2020

Dissertation Director: Terri L. Messman-Moore, Ph.D.

©

Julia Simone Kaufman

2020

iii

TABLE OF CONTENTS

List of Tables.......................................................................................................................v

List of Figures.....................................................................................................................vi

Dedication..........................................................................................................................vii

Acknowledgements..........................................................................................................viii

Introduction..........................................................................................................................1

Defining Psychological Maltreatment......................................................................1

Measurement and incidence of psychological maltreatment........................4

Evidence for subtypes of psychological maltreatment..................................6

Effects of Psychological Maltreatment.....................................................................7

Anxiety and depression................................................................................8

Substance use...............................................................................................9

Differential effects for different types of psychological maltreatment.......10

Purpose...................................................................................................................12

Study Aims.................................................................................................12

Hypotheses.................................................................................................13

Method...............................................................................................................................13

Participants.............................................................................................................13

Procedures..............................................................................................................14

Measures................................................................................................................14

Psychological maltreatment.......................................................................14

Physical abuse, sexual abuse, and neglect.................................................15

Anxiety and depression..............................................................................16

Substance use.............................................................................................16

Results................................................................................................................................18

Confirmatory Factor Analysis................................................................................18

Person-Centered Analysis of Childhood Psychological Maltreatment..................19

Examination of Characteristics of the Three PM Groups.......................................21

Demographic characteristics......................................................................22

Co-occurrence of other types of child maltreatment..................................22

Intercorrelation of Variables..................................................................................24

iv

Depression and Anxiety.........................................................................................25

Substance Use........................................................................................................26

Alcohol use................................................................................................26

Drug use.....................................................................................................27

Controlling for Other Types of Child Maltreatment..............................................28

Discussion..........................................................................................................................30

Adult Psychological Functioning...........................................................................31

Depression and anxiety..............................................................................31

Substance use.............................................................................................33

Alcohol use.....................................................................................33

Cannabis and nicotine use..............................................................33

Drug use.........................................................................................34

Co-Occurrence of Other Types of Child Maltreatment..........................................35

Child maltreatment type versus severity.....................................................36

Isolating effects of PM...............................................................................37

Strengths and Limitations.......................................................................................42

Implications............................................................................................................44

Conclusion.........................................................................................................................47

References..........................................................................................................................48

Tables.................................................................................................................................67

Figures................................................................................................................................86

v

LIST OF TABLES

1. Confirmatory Factor Analysis of the CAMI Psychological Maltreatment Scale..........66

2. Fit Indices for Latent Profile Models 1 – 4....................................................................67

3. Characteristics of Psychological Maltreatment Subtypes.............................................68

4. Demographic Characteristics.......................................................................................69

5. Co-occurrence of Other Types of Child Maltreatment.................................................70

6. Binomial Logistic Regression Predicting Sexual and Physical Abuse..........................71

7. Intercorrelations, Means, and Standard Deviations for Outcome Variables.................72

8. Bivariate Correlations for Outcomes Variables and Demographic Characteristics......73

9. Depression and Anxiety Between the Psychological Maltreatment Groups.................74

10. Crosstabulation of Psychological Maltreatment Groups and Depression Severity.......75

11. Crosstabulation of Psychological Maltreatment Groups and Anxiety Severity............76

12. Alcohol Use Between the Psychological Maltreatment Groups...................................77

13. Crosstabulation of Psychological Maltreatment Group and Alcohol Use.....................78

14. Cannabis and Nicotine Use Between Psychological Maltreatment Groups..................79

15. Crosstabulation of Psychological Maltreatment Groups and Drug Use Frequency......80

16. Crosstabulation of Psychological Maltreatment Groups and Drug Use Problems.......81

17. Depression and Anxiety Between Groups Controlling for CSA and CPA...................82

18. Alcohol Use Between Groups Controlling for CSA and CPA Victim Status..............83

19. Cannabis and Nicotine Use Between Groups Controlling for CSA and CPA..............84

vi

LIST OF FIGURES

1. Subtypes of Psychological Maltreatment.....................................................................85

2. Severity of Other Types of Child Maltreatment ..........................................................86

3. Percent of Physical and Sexual Abuse Victims.............................................................87

4. Percent of Physical and/or Sexual Abuse Victims.. .....................................................88

5. Characteristics of Child Sexual Abuse Experiences.....................................................89

6. Characteristics of Child Physical Abuse Experiences..................................................90

vii

DEDICATION

To my parents, without whom none of this would have been possible. Thank you for your

endless guidance and support.

viii

ACKNOWLEDGEMENTS

I would like to express my gratitude for the support and guidance I received throughout

this project. I want to thank my advisor, Dr. Terri Messman-Moore, for welcoming me

into her research lab. The time we spent thinking out loud together is something I will

truly miss. I also want to thank my dissertation committee members, Drs. Aaron Luebbe,

Elise Clerkin, and Kevin Bush, for their enthusiasm and guidance on this project.

To my peers, Alex McConnell, Lee Eshelman, Kathryn Mancini, Prachi Bhuptani, Selime

Salim, Bethany Walker, who provided endless support over the years, thank you for

listening to my ideas, reading my drafts, and for your friendship. To my partner, Dave,

thank you for incredible support and patience. I do not know how I could have done it

without you.

Lastly, I thank the women who participated in this study for sharing their stories.

1

A Person-Centered Approach to Identifying Common and Unique Impacts of Different

Psychological Maltreatment Experiences

Psychological maltreatment (PM) has received the least research attention of all forms of

child maltreatment (Barnett et al., 2005; Finkelhor, Ormrod, Turner, & Hamby, 2005; Hart &

Glaser, 2011) despite the fact that it may be the most commonly occurring (Armour, Elklit, &

Christoffersen, 2014; Barnett, Miller-Perrin, & Perrin, 2005; Hart, Binggeli, & Brassard, 1998).

PM refers to patterns of caregiver behavior that communicate to children that they are worthless,

unloved, or endangered (American Professional Society on the Abuse of Children [APSAC],

1995). PM is associated with negative outcomes such as anxiety, depression, emotion

dysregulation, and substance use (Banducci, Hoffman, Lejuez, & Koenen, 2014; Binggeli, Hart,

& Brassard, 2001; Hart et al., 1998; Norman et al., 2012; Spinazzola et al., 2014; Taillieu,

Brownridge, Sareen, & Afifi, 2016). In empirical work, PM is frequently examined as a unitary

construct despite evidence demonstrating its multidimensional nature (Paul & Eckenrode, 2015;

White, English, Thompson, & Roberts, 2016). Although PM can be conceptualized as an

umbrella term for a range of abusive and neglectful parenting behaviors, different subtypes of

PM are rarely assessed. In fact, researchers have yet to apply a person-centered approach to

examine how subtypes of PM co-occur and few studies have assessed differential effects of PM

subtypes (Allen, 2008; Shin, Lee, Jeon, & Wills, 2015; Taussig & Culhane, 2010). The available

research suggests differences in rates of PM across PM subtypes (Baker & Ben-Ami, 2011;

Taussig & Culhane, 2010) as well as differential effects of PM subtypes (e.g., Allen, 2008;

Egeland, Sroufe, & Erickson, 1983; English, Thompson, White, & Wilson, 2015; Taillieu et al.,

2016). Therefore, there is a need to investigate specific experiences of PM and associated effects

on adult functioning (Hart et al., 1998; Taussig & Culhane, 2010). The present study addresses

this gap in the literature by exploring the natural co-occurrence of PM subtypes and associated

differences in adult psychological functioning in a sample of young women.

Defining Psychological Maltreatment

Since the first national law on child abuse was enacted in the United States (Child Abuse

Prevention and Treatment Act [CAPTA], 1974) and PM received vague recognition (as “mental

injury”) in the definition of child maltreatment, a range of terms to describe PM has been used

interchangeably (Navarre, 1987; Taussig & Culhane, 2010). Today, the term PM is often

preferred because it encompasses the cognitive, affective, and interpersonal dimensions of

2

maltreatment as well as acts of omission (i.e., neglect) and acts of commission (i.e., abuse;

APSAC, 1995; Glaser, 2011; Hart & Brassard, 1987). Many definitions of PM, including the

APSAC (1995) definition, imply that emotional abuse is subsumed by PM (e.g., Hart et al.,

1998; Hart, Germain, & Brassard, 1987), although there are exceptions to this perspective (e.g.,

O’Hagan, 1995). Others have stated that the decision to use the term emotional or psychological

is a matter of convention as distinctions between these terms is often not clear (English et al.,

2015).

In a review of the literature, Brassard and Donovan (2006) concluded that there is

considerable agreement among researchers regarding what parenting behaviors constitute this

type of maltreatment. The disagreement largely concerns the following: (1) whether a behavior

should be labeled as PM or another form of abuse/neglect, (2) which subtype of PM a specific

behavior falls under, and (3) which behaviors are severe enough to be called PM as opposed to

poor parenting (Brassard & Donovan, 2006). As noted by Taussig and Culhane (2010), PM

might be defined as harsh parenting and witnessing marital violence in one study, verbal abuse or

aggression (i.e., spurning) in another, and a third study might include exploitation and

abandonment. The lack of definitional clarity impedes progress in the study of PM and limits

comparisons that can be made across studies.

PM constitutes a unique form of child maltreatment, which can be experienced in

isolation (e.g., Armour et al., 2014; Egeland et al., 1983; Garbarino & Vondra, 1987), but

frequently co-occurs with other types of child maltreatment (Bifulco, Moran, Baines, Bunn, &

Stanford, 2002; Cicchetti & Rogosch, 2001; McGee, Wolfe, & Wilson, 1997; Taillieu et al.,

2016; Trickett, Mennen, Kim, & Sang, 2009). The rates of co-occurrence are especially high for

PM and physical abuse (Clausen & Crittenden, 1991; Crittenden, Claussen, & Sugarman, 1994;

Dong et al., 2004; Hodgdon et al., 2018; McGee et al., 1997; Rice et al., 2001). Some researchers

suggest that PM may be the “core component” of child maltreatment (e.g., Hart et al., 1998; Hart

et al., 2011), “embedded in and interact[ing] with” all types of abuse and neglect (Binggeli et al.,

2001, p. xi). In fact, PM in combination with other types of child maltreatment may amplify the

effects of maltreatment (Edwards, Holden, Felitti, & Anda, 2003; McGee et al., 1997; Spinazzola

et al., 2014).

At this time, the APSAC (1995) definition of PM is the most widely utilized and agreed

upon. The APSAC (1995) guidelines define PM as “a repeated pattern of caregiver behavior or

3

extreme incident(s) that convey to children that they are worthless, flawed, unloved, unwanted,

endangered, or only of value in meeting another’s needs” (p. 2). There is general agreement

among researchers that PM reflects a wide range of parenting behaviors including acts of

commission and acts of omission. It has been suggested that, “No two cases of psychological

maltreatment will contain exactly the same elements, and many will be quite different from one

another” (Binggeli et al., 2001, p. 17). The APSAC (1995) guidelines identified and defined five

categories of PM which included spurning, terrorizing, isolating, exploiting/corrupting, and

denying emotional responsiveness. Spurning includes both verbal and non-verbal acts that reject

and degrade a child, such as demeaning, shaming, humiliating, and excessively criticizing a child

(e.g., swearing at or calling a child names; APSAC, 1995; Barnett, Miller-Perrin, & Perrin, 2011;

Hart et al., 1998). Terrorizing is defined as caregiver behavior that threatens or is likely to

physically hurt, kill, or abandon the child or the child’s loved ones or objects (e.g., threatening

suicide, threatening to physically hurt a child). In addition, behaviors that place a child or child’s

loved ones or objects in unpredictable or dangerous situations, exploit a child’s vulnerabilities

with threats, and set unrealistic expectations with threats of loss or harm if not met are

conceptualized as terrorizing. Isolating includes acts that consistently deny the child

opportunities to meet needs for interacting or communicating with peers or adults, outside or

inside the home. This can include behaviors that confine or place unreasonable limitations on the

child’s movement inside the home (e.g., locking a child in a room) and behaviors that limit social

interactions in the home or community (Barnett et al., 2011; Hart et al., 1998).

Exploiting/corrupting includes acts that encourage inappropriate, self-destructive, antisocial,

deviant or other maladaptive behaviors (e.g., encouraging alcohol or drug use, allowing a child to

watch adults have sex, and encouraging prostitution; Barnett et al., 2011; Hart et al., 1998).

Denying emotional responsiveness occurs when caretakers display no emotion in interactions

with the child and ignore the child’s attempts to interact by failing to express affection (e.g., only

interacting with a child if absolutely necessary; Barnett et al., 2011; Hart et al., 1998). While

rejection does not constitute its own category of PM, it is a relevant aspect of all categories

(Hart, Brassard, Binggeli, & Davidson, 2002; Rohner & Rohner, 1980). Rejection is most clearly

reflected in spurning and denying emotional responsiveness. With regard to spurning, rejection is

expressed through behavioral acts of hostility and aggression (i.e., acts of commission).

Rejection is conveyed in denying emotional responsiveness as “the mental attitude of

4

indifference is manifested behaviorally in a caretaker’s neglect of a child’s emotional needs”

(i.e., acts of omission; Hart et al., 2002, p. 81). The APSAC definition of PM included a sixth

category, mental health, medical, and educational neglect; however, this category was derived

from the first five and is not typically included as a separate area of research (APSAC, 1995;

Binggeli et al., 2001; Hart et al., 1998).

Measurement and incidence of psychological maltreatment. Efforts to research and

measure the incidence of PM have been challenging for several reasons. Child maltreatment has

frequently been treated as a dichotomous variable based on the presence or absence of a

maltreatment history. For example, studies that use Child Protective Service (CPS) reports as an

indicator of maltreatment typically dichotomize the variable, using CPS records to assess

whether or not maltreatment occurred. According to Newcomb and Locke (2001), treating child

maltreatment as a dichotomous variable is “blindly avoiding the reality that childhood

maltreatment exists to degree, along various continua” (p. 1221). Therefore, many researchers

advocate for measuring the severity of maltreatment while also accounting for the variety of

maltreatment types experienced (Clemmons, Walsh, DiLillo, & Messman-Moore, 2007;

Litrownik et al., 2005). Another reason to avoid the use of a dichotomous indicator of PM is that

it may be the most difficult form of child maltreatment to dichotomize by defining a cut-off or

threshold level (Wekerle, 2011).

Self-report measures of PM have presented similar challenges. Given the lack of

consistency in conceptual definitions of PM, it is not surprising that there is variability in

operational definitions applied to self-report measures. Available research in the domain of PM

has tended to focus on some subtypes of PM (e.g., terrorizing and spurning), while overlooking

others. For example, the Childhood Trauma Questionnaire (CTQ; Bernstein & Fink, 1998;

Bernstein et al., 2003) is a widely used measure that assesses different subtypes of child

maltreatment. Some researchers use the Emotional Abuse subscale of the CTQ to assess PM

(e.g., Shin et al., 2015) while others combine the Emotional Abuse and Emotional Neglect

subscales (e.g., Hamilton et al., 2013; Potthast, Neuner, & Catani, 2014). According to Baker

(2009), these two subscales of the CTQ contain four items reflecting spurning and one item for

terrorizing. The CTQ does not assess isolating, exploiting/corrupting, or denying emotional

responsiveness. Thus, the CTQ is not truly a measure of PM as defined by the APSAC (1995),

even though researchers might identify the construct as such. Baker (2009) examined 15 scales

5

that have been created to assess childhood experiences of PM. Through examination of these

self-report measures, Baker (2009) concluded that only four measures included items referencing

all five APSAC categories of PM. Examination of available self-report measures reveals that the

majority of self-report measures focus on spurning (Baker, 2009). In a review of the literature,

Brassard and Donovan (2006) concluded that parent- and self-report instruments were in need of

improvement. Thus, rates of PM vary based on the definition and measurement used.

According to CPS records, there were an estimated 674,000 victims of abuse and neglect

in 2017 in the United States, of which 5.7% were psychologically maltreated (U.S. Department

of Health and Human Services [USDHHS], 2019). However, CPS records are the most stringent

measurement of maltreatment (Finkelhor et al., 2005; Sedlak et al., 2010; USDHHS, 2019;

White et al., 2016) and tend to significantly underestimate the rate of PM (Schneider, Ross,

Graham, & Zielinski, 2005; Sedlak et al., 2010; Trickett et al., 2009). In addition, the criteria for

substantiation and the definitions of PM, if included in a state’s definition of abuse and neglect at

all, vary considerably from state to state (Newcomb & Locke, 2001; USDHHS, 2019).

Relative to CPS records, other measurements yield higher rates of PM. The National

Incidence Studies (NIS), which estimate rates of child maltreatment based on data from CPS

agencies as well as reports from a national sample of community professionals, likely provide

more accurate estimates of maltreatment rates. According to the Fourth National Incidence Study

of Child Abuse and Neglect (NIS-4), nearly 3 million children or approximately 39.5 per 1,000

children in the United States experienced maltreatment during the study year (2005-2006; Sedlak

et al., 2010). The NIS-4 emotional abuse codes closely correspond to the APSAC (1995)

categories of terrorizing, spurning, and isolating. Emotional neglect is less closely related to the

APSAC (1995) definition of PM, although several of the emotional neglect codes reflect denying

emotional responsiveness and exploiting/corrupting (Paul & Eckenrode, 2015; Sedlak et al.,

2010). Of children who were abused, 36% (4.1 per 1,000 children) were emotionally abused (an

estimated 302,600 children; Sedlak et al., 2010). Of children who were neglected, 52% (15.9 per

1,000 children) were emotionally neglected (an estimated 1,173,800 children; Sedlak et al.,

2010).

Rates of child maltreatment in community surveys are even higher. An overall

maltreatment rate of 138 per 1,000 children was obtained from a nationally representative sample

of children and youth who reported maltreatment experiences during the past year (Finkelhor et

6

al., 2005). Emotional abuse (i.e., spurning) was the most frequent type of child maltreatment

experienced, at a rate of 103 per 1,000 (Finkelhor et al., 2005). In another large, community

survey with a nationally representative sample of 4,549 children and youth, Finkelhor, Turner,

Ormrod, and Hamby (2009) found a past-year rate of psychological abuse by an adult (i.e.,

spurning) of 6.4% (64 per 1,000) and a lifetime rate of 11.9% (119 per 1,000). Among

adolescents aged 14 to 17 years, 22.6% (226 per 1,000) had experienced spurning over their

lifetime (Finkelhor et al., 2009).

Rates of childhood PM retrospectively reported by adults vary. A sample of 8,613 adults

who attended a primary care clinic in California completed a survey, and 10.2% reported a

history of emotional abuse (i.e., spurning and terrorizing; Dube et al., 2003). In a study of parents

reporting their own parenting behaviors, Straus and Field (2003) found that by the time their

child had reached age two, 90% of parents in a national telephone survey reported using

psychological aggression (i.e., spurning) toward their child in the previous year. The “true

prevalence” of PM is not known, as the majority of incidents remain unreported (Hart et al.,

2011, p. 131); however, many researchers agree that PM is the most frequently occurring form of

child maltreatment (Hart et al., 1998; Armour et al., 2014; Barnett et al., 2005). The best estimate

of the overall rate of PM is approximately 30% of the population (Binggeli et al., 2001).

Evidence for subtypes of psychological maltreatment. Theory and research indicate

that PM is a multidimensional construct; nonetheless, most studies treat PM as unidimensional

(Paul & Eckenrode, 2015; White et al., 2016). Few published studies examine different

experiences of PM, despite widespread recognition of the vast range of abusive and neglectful

parenting behaviors that constitute this form of maltreatment (e.g., Baker, 2009; Binggeli et al.,

2001; Brassard & Donovan, 2006). Available research examining subtypes of PM indicates that

the rate and co-occurrence of the subtypes vary (e.g., Baker & Verrocchio, 2015; de la Vega, de

la Osa, Ezpeleta, Granero, & Domènech, 2011; Taussig & Culhane, 2010; Trickett et al., 2009).

For example, Baker and Ben-Ami (2011) examined subtypes of PM in a sample of 118 adult

children of divorced parents. Of participants who endorsed experiencing at least one type of PM

in childhood, 85.7% reported they were denied emotional responsiveness, 60.3% were spurned,

41.3% were isolated, 34.9% terrorized, and 30.2% exploited/corrupted. In addition, of

participants reporting a PM history, over one-fourth (27.0%) endorsed two PM subtypes, over

one-fifth (23.8%) endorsed four subtypes, and 6.3% endorsed all five subtypes (Baker & Ben-

7

Ami, 2011). Studies that have examined different subtypes of PM have found that some subtypes

tend to co-occur more than others. For example, the combination of terrorizing and spurning may

be the most common co-occurring type of PM, with one study finding that 78.9% of children

who experienced spurning or terrorizing also experienced the other (Trickett et al., 2009). In

addition, children who experience terrorizing/spurning may be more likely to experience

abandonment than not (Taussig & Culhane, 2010). Experiences of exploiting/corrupting and

isolating also frequently co-occur, with one study reporting that 65.0% of the children who

experienced one subtype also experienced the other (Trickett et al., 2009). Together, this

research suggests that some PM subtypes are more common than others, and that children

experience unique and perhaps predictable combinations of PM subtypes.

Effects of Psychological Maltreatment

PM is associated with a range of negative outcomes including symptoms of depression

and anxiety, risky behavior including substance use, and interpersonal problems (Hart et al.,

1998; Norman et al., 2012; Spinazzola et al.; 2014; Taillieu et al., 2016). In fact, with the

exception of child maltreatment that results in death, PM may produce the most destructive and

long-lasting effects (Berzenski & Yates, 2011; Binggeli et al., 2001; Ney, Fung, & Wickett,

1994) through broad impairments in social and emotional development, which can be observed

beginning in early childhood (Egeland & Sroufe, 1981; Egeland et al., 1983; McGee et al.,

1997). For example, studies using data from the Minnesota Longitudinal Study of Parents and

Children revealed that by 18 months of age, children who were denied emotional responsiveness

displayed a decline in social and emotional functioning that was more striking and devastating

than that observed in children exposed to other types of maltreatment (Egeland & Sroufe, 1981;

Egeland et al., 1983). In older children and adolescents, effects of PM are similarly devastating.

For instance, verbal abuse (i.e., terrorizing/spurning) and emotional neglect (i.e., denying

emotional responsiveness) were found to significantly impact development as measured by

children’s perceptions of themselves and their future (Ney et al., 1994).

The experience of PM in childhood continues to confer risk for mental health problems in

adulthood, although research on the effects of PM in adults is limited both in terms of the

number of studies and the outcomes examined. Some researchers posit that relative to other types

of child maltreatment, PM is equally or even more strongly associated with later psychological

functioning (Barnett et al., 2011; Berzenski & Yates, 2011; Hart et al., 1998). For example,

8

controlling for other types of child maltreatment, adolescents’ and adults’ perceptions of

childhood experiences of PM are associated with psychological distress (Crawford & Wright,

2007; McGee et al., 1997; Spertus, Yehuda, Wong, Halligan, & Seremetis, 2003; Wright,

Crawford, and Del Castillo, 2009). Similarly, Greenfield and Marks (2010) found that a history

of psychological violence (i.e., terrorizing/spurning) was associated with more negative affect

and less psychological well-being in adults, regardless of whether or not participants had

experienced physical abuse.

Anxiety and depression. Across samples, the evidence for PM as a risk for anxiety and

depression is consistently strong (Crow, Cross, Powers, & Bradley, 2014; McGee et al., 1997;

Nelson, Klumparendt, Doebler, & Ehring, 2017; Norman et al., 2012). In a large national sample

of clinic-referred youth, Spinazzola et al. (2014) found that compared to physical and sexual

abuse, PM was the strongest and most consistent predictor of internalizing problems including

depression, generalized anxiety, and social anxiety. In college student and adult samples, PM

continues to predict anxiety and depression (e.g., Briere & Runtz, 1988), and these effects

remain even when other types of child maltreatment are considered (Crow et al., 2014; Gibb,

Chelminski, & Zimmerman, 2007; Gross & Keller, 1992; Miller-Perrin, Perrin, & Kocur, 2009;

Spertus, Yehuda, Wong, Halligan, & Seremetis, 2003; Taillieu et al., 2016). For example, Miller-

Perrin et al. (2009) identified parental psychological aggression (i.e., spurning and terrorizing)

during childhood as the most unique predictor of depression severity scores in college students,

above and beyond the impact of physical abuse, the frequency of corporal punishment, and

demographic characteristics. In adult psychiatric patients, Martins, Von Werne Baes, Tofoli, and

Juruena (2014) found that patients with a history of emotional abuse (i.e., spurning and

terrorizing) had more severe symptoms of depression, hopelessness, suicidal ideation, and

anxiety compared to patients without a history of spurning. Further, approximately 70% of

patients diagnosed with a depressive disorder reported a history of spurning and terrorizing

(Martins et al., 2014). In a systematic review of the literature on non-sexual child maltreatment,

Norman et al. (2012) found that individuals with histories of emotional abuse (defined differently

across studies, but often reflecting spurning) had a higher risk of developing depressive and

anxiety disorders compared to individuals with histories of physical abuse and neglect and those

with no childhood maltreatment history. In addition, research suggests a dose-response

relationship between the severity of PM experiences and the likelihood of chronic or recurrent

9

major depression (Bifulco et al., 2002). Therefore, there is strong evidence for an association

between childhood experiences of PM and symptoms of anxiety and depression in adulthood;

however, this evidence is predominantly based on studies focusing on spurning/terrorizing and

on assessment of PM as a unidimensional construct.

Substance use. In contrast to the evidence for an association between PM and symptoms

of anxiety and depression, the evidence for substance use and substance-related problems is far

less clear. In a systematic review of the literature, Norman et al. (2012) concluded that the

evidence was not consistent across studies or across types of substances. Norman et al. (2012)

identified PM as a strong risk factor for drug use, but the evidence for alcohol problems and

tobacco smoking was weaker and inconsistent. However, since Norman et al.’s (2012) review,

several studies have presented evidence suggesting the relation between PM and alcohol use and

alcohol use problems (Duke, 2018; Hayre, Goulter, & Moretti, 2019; Taillieu et al., 2016) and

tobacco or nicotine use (Duke, 2018; Elliott, et al., 2014; Hayre et al., 2019; Taha, Galea, Hien,

& Goodwin, 2014) may be stronger than previously believed. Among cocaine dependent women,

severity of emotional abuse (i.e., spurning and terrorizing) is associated with a greater severity of

substance abuse and with younger age of first alcohol use, but not significantly associated with

age of onset of regular alcohol, nicotine, or cocaine use or age of first nicotine or cocaine use

(Hyman, Garcia, & Sinha, 2006). However, in non-treatment seeking samples, PM has been

identified as a predictor of alcohol use and alcohol-related problems (Dube, Anda, Felitti,

Edwards, & Croft, 2002; Mandavia, Robinson, Bradley, Ressler, & Powers, 2016; Mezquita,

Ibáñez, Moya, Villa, & Ortet, 2014; White et al., 2016). Moran, Vuchinich, and Hall (2004)

examined the relation between different types of child maltreatment and substance use among

high school students. Although all maltreatment types were associated with tobacco, alcohol, and

illicit drug use, emotional abuse (i.e., spurning) was associated with lower risk of substance use

compared to other types of maltreatment. It is worth noting that Moran et al. (2004) assessed

each type of child maltreatment with a single item, and the assessment of spurning was

particularly weak as it was not behaviorally specific, and it required participants to assume

intentionality on the part of perpetrator(s). In other studies, PM has emerged as a stronger

predictor of substance use and/or substance use problems than other types of child maltreatment

(Rosenkranz, Muller, & Henderson, 2012; Scheidell et al., 2017; Shin et al., 2015; Spinazzola et

al., 2014; Taillieu et al., 2016; White et al., 2016). For example, Potthast et al. (2014) explored

10

the impact of child maltreatment on alcohol dependence in a sample of treatment-seeking adults.

Results indicated that emotional maltreatment (i.e., spurning and terrorizing) was the strongest

predictor of alcohol dependence severity, above and beyond the effect of child sexual abuse

(CSA), child physical abuse (CPA), and peer victimization. Among non-treatment-seeking

adults, emotional abuse (i.e., spurning and terrorizing) is associated with frequency of alcohol

use, binge drinking (i.e., heavy episodic drinking), alcohol-related problems, and alcohol use

disorders, controlling for demographic characteristics, psychological distress, and other types of

child maltreatment (Shin et al., 2015).

Differential effects for different types of psychological maltreatment. There is

growing evidence to suggest that different subtypes of PM have different short- and long-term

effects. However, it is worth noting that research has tended to focus on children and adolescents

and on experiences of terrorizing and spurning, often excluding other subtypes of PM. Research

on young children has demonstrated the particularly devastating impact of psychologically

unavailable or emotionally unresponsive caregivers (i.e., denying emotional responsiveness) on

development, even when children are receiving adequate physical care (Brassard & Donovan,

2006; Egeland & Sroufe, 1981; Egeland et al., 1983). In middle childhood and adolescence,

research indicates that caregiving characterized by denying emotional responsiveness and

spurning has a negative impact on development. For example, Shaffer, Yates, and Egeland

(2009) found that emotional abuse (i.e., spurning) and emotional neglect (e.g., denying emotional

responsiveness) were associated with increased social withdrawal in middle childhood and lower

ratings of socioemotional competence in early adolescence. The relation between spurning, but

not denying emotional responsiveness, and adolescent socioemotional competence was mediated

by social withdrawal in middle childhood (Shaffer et al., 2009). In a longitudinal study

examining the impact of marital violence on children, Stuewig and McCloskey (2005) found that

parental rejection (i.e., spurning) and parental warmth (i.e., emotional responsiveness) assessed

when the youth were approximately 15 years old, were correlated with symptoms of depression

two years later. Spurning in adolescence was associated with the adolescent’s shame proneness,

which was in turn associated with depression (Stuewig & McCloskey, 2005). Paul and

Eckenrode (2015) found gender differences in the association between PM and adolescent

depressive symptoms. For adolescent girls, caregiver degradation (i.e., spurning) was predictive

of increased depressive symptoms, controlling for experiences of physical and sexual abuse. On

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the other hand, for adolescent boys, isolation was predictive of depressive symptoms (Paul &

Eckenrode, 2015).

With regard to older adolescents, White et al. (2016) used data from the Longitudinal

Studies of Child Abuse and Neglect (LONGSCAN) to explore the relation between childhood

experiences of PM and psychological symptoms and substance use at age 18. Measurement of

PM was based on self-report and CPS allegations of maltreatment that were coded using the

Maltreatment Classification System modified by LONGSCAN (MMCS). White et al. (2016)

found that both self-reports and CPS reports of failure to support psychological safety and

security (i.e., terrorizing) predicted anxiety, depression, and use of illegal drugs other than

marijuana, controlling for demographic characteristics and other types of child maltreatment.

Self-reports of terrorizing, but not official reports, predicted cigarette smoking. CPS reports of

failure to provide acceptance and self-esteem (i.e., spurning) emerged as a unique predictor of

problem drinking. Other PM subtypes including failure to allow age-appropriate autonomy (i.e.,

exploiting/corrupting) and restriction (i.e., isolating) did not emerge as significant predictors of

anxiety, depression, or any substance use (White et al., 2016).

Research examining the long-term effects of different PM subtypes in adults is quite

limited. In a sample of 256 undergraduate psychology students, Allen (2008) found that anxiety

and depression were significantly correlated with perceptions of childhood experiences of

degradation (i.e., spurning), terrorizing, ignoring (i.e., denying emotional responsiveness), and

witnessing family violence, but not with isolating. Regression analyses that included four

subtypes of PM, physical abuse, and gender, revealed differential effects of PM subtypes.

Specifically, terrorizing emerged as a unique predictor of anxiety and denying emotional

responsiveness emerged as a unique predictor of depression (Allen, 2008). Taillieu et al. (2016)

used cross-sectional data from the National Epidemiological Survey on Alcohol and Related

Conditions, which used a large, representative sample of adults living in the United States to

examine associations between child maltreatment and psychiatric disorders. Direct comparison

of a history of emotional neglect (i.e., other/denying emotional responsiveness) and emotional

abuse (i.e., spurning/terrorizing) revealed that spurning/terrorizing was associated with higher

odds of a lifetime diagnosis of psychiatric disorders including depression, anxiety disorders, and

any substance use disorder, controlling for other types of child maltreatment. Taken together,

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available research suggests that different experiences of PM may differentially affect adult

psychological functioning.

Purpose

Research suggests that PM has powerful and persistent effects on psychological

functioning. Although PM can be conceptualized as an umbrella term for a range of abusive and

neglectful parenting behaviors, different subtypes of PM are rarely assessed. In fact, most studies

treat PM as a unitary construct despite theory and evidence supporting a multidimensional

construct (Paul & Eckenrode, 2015; White et al., 2016). Moreover, while research indicates that

the rate and co-occurrence of subtypes of PM varies, research has yet to explore how these

subtypes naturally co-occur. There is very little research on the differential impacts of subtypes

of PM (Allen, 2008; Taussig & Culhane, 2010) and researchers have tended to assess PM using

measures that only capture experiences of spurning and terrorizing. When PM subtypes have

been studied, differential effects of the different subtypes have emerged (e.g., Allen, 2008; Paul

& Eckenrode, 2015; Schneider et al., 2005). Researchers including Hart and colleagues (1998)

have called for additional studies examining the relations between the severity of different

subtypes of PM and associated effects. Taussig and Culhane (2010) have emphasized the

importance of understanding the differential impacts of the different subtypes of PM in order to

truly understand the impact of PM on psychosocial development and adult functioning.

Study aims. The purpose of the present study was to examine childhood experiences of

PM in a sample of young women. The primary aim was to use a person-centered analytic

approach to empirically identify naturally occurring subgroups or profiles of women based on

subtypes of PM experienced in childhood. Prior to conducting the person-centered analysis, I

tested the fit of the data with the APSAC model, examining whether the CAMI PM items loaded

adequately onto the factors or PM subscales. Given the high co-occurrence of PM with other

types of child maltreatment (i.e., sexual abuse, physical abuse, and neglect), I planned to

investigate whether certain derived PM profiles are more strongly associated with the presence

and/or severity of other types of child maltreatment. The second aim of the present study was to

examine associations between specific PM experiences and adult outcomes. Specifically, I

examined whether PM profiles were differentially associated with indicators of psychological

functioning including depression, anxiety, and substance use.

13

Hypotheses. The person-centered analysis of naturally occurring profiles of different PM

experiences was exploratory in nature, as this was the first study to conduct such an analysis

using subtypes of PM. Therefore, there were no a priori hypotheses regarding the number of

latent profiles. I expected to find a derived latent profile of participants who reported relatively

low levels of all PM subtypes and at least one latent profile of participants who reported

experiencing PM during childhood.

In terms of the co-occurrence of other types of child maltreatment, certain PM profiles

were hypothesized to be differentially associated with the presence/absence and severity of

different types of child maltreatment. The hypothesized low PM profile was expected to have a

lower prevalence and lower levels of severity of all other types of child maltreatment (i.e.,

physical abuse, sexual abuse, and neglect). In addition, previous research suggests that physically

abusive parents are likely to also terrorize and spurn their children (Berzenski & Yates, 2011;

Kim, Mennen, & Trickett, 2016; Ney et al., 1994; Trickett et al., 2009). Therefore, a PM profile

characterized by high levels of terrorizing/spurning was hypothesized to have relatively higher

rates and levels of severity of child physical abuse compared to profiles with lower levels of

terrorizing/spurning. Given previous research suggesting that children who are neglected in one

domain are highly likely to be neglected in other domains (Claussen & Crittenden, 1991), a

derived PM profile characterized by having been denied emotional responsiveness was expected

to be more strongly associated with neglect than profiles with lower levels of denying emotional

responsiveness. It was also hypothesized that compared to a low PM profile, PM profiles with

more severe PM experiences would be more strongly associated with indicators of poor adult

psychological functioning (i.e., depressive and anxiety symptoms, substance use).

Method

Participants

Participants were 491 community women between the ages of 18 and 25 (M = 21.74, SD

= 2.23) who were part of a larger, multi-site study examining mechanisms underlying sexual

revictimization. One participant was removed from analyses as all child maltreatment data was

missing. Approximately two-thirds of participants (61.3%) identified as White, 35% as African

American, 4.3% Asian/Pacific Islander, 3.1% Native American, and 2.6% other, and 5.7% of

participants identified at Latina/Hispanic. Over half of participants were students (61.7%; full- or

part-time) at the time of the study. The median household income was between $10,000 and

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$20,000. Two hundred two participants (41.2%) reported an annual household income under

$10,000, 79 participants (16.1%) reported income between $10,000 and $20,000, 19% reported

income between $20,000 and $40,000, 12.7% reported income between $40,000 and $75,000,

and 9.8% reported income above $75,000. The majority of participants had never been married

(83.3%), 13.6% were married or in a long-term relationship (defined as three or more years) and

cohabitating, and 2.6% were separated or divorced. One-fifth (20.2%) of participants had

children.

Procedures

Data were collected as part of the baseline assessment of a larger, multi-site prospective

study. The Project WISE data were collected at four geographic locations (Lincoln and Omaha,

Nebraska; Jackson, Mississippi; Oxford, Ohio). The larger study focused on emotion

dysregulation and sexual revictimization in young adult women. All procedures were approved

by the Institutional Review Board of the participating institutions. The larger study included a

community sample of young women from the four study locations. All women between ages of

18 and 25 within the specified geographic regions (zip codes) were mailed a letter notifying them

of study. Women were also recruited through advertisements in newspaper and on Craigslist,

fliers posted in the communities, and university mass emails. The consent process was completed

in person and all participants provided written informed consent for study participation. At the

baseline assessment, participants completed diagnostic interviews as well as a series of self-

report questionnaires. Self-report questionnaires were completed online on a computer in the

laboratory of one of the study sites. Participants were reimbursed $75 upon completion of the

baseline assessment.

Measures

Psychological maltreatment. The Computer Assisted Maltreatment Inventory (CAMI;

DiLillo, 2003; DiLillo et al., 2010) is a computer-based, self-report measure that was developed

to assess five types of child maltreatment (sexual abuse, physical abuse, exposure to domestic

violence, psychological maltreatment, and neglect). The CAMI uses specific behavioral criteria,

based on acts commonly defined as abuse in the child maltreatment literature. The CAMI has

been shown to have good test-retest reliability and good criterion-related validity compared to a

standard measure of childhood maltreatment (DiLillo et al., 2010). The psychological

maltreatment (PM) scale was developed from an initial pool of 57 items generated by a team of

15

researchers. The items were thought to represent the major domains of psychological

maltreatment based on the APSAC (1995) definition (i.e., terrorizing, spurning, isolating,

exploiting or corrupting, and denying emotional responsiveness). The CAMI PM scale was

reduced from 57 items to 24 items based on results of an exploratory factor analysis (EFA; Nash,

Hayes-Skelton, & DiLillo, 2012). The revised CAMI PM scale consisting of 24 items was used

in the present study. For the purpose of the present study, the number of CAMI PM items

referencing each APSAC (1995) category of PM was determined based on examination of each

item’s content, examination of Nash et al.’s (2012) EFA, and in consultation with my research

advisor. For the CAMI PM scale, participants were instructed to indicate how much they agree

or disagree with each statement using a 5-point Likert scale ranging from 1 (strongly disagree) to

5 (strongly agree). Items in the emotional responsiveness subscale were reverse scored.

Instructions for the PM scale read as follows: “Please indicate by using the scale below how

much you agree or disagree with each statement. By ‘parents’ we mean any parent, stepparent,

foster parent, or other primary caregiver who took care of you as a child, even if they were not

biologically related to you.” Mean scores were calculated for each PM subscale. Reliability

coefficients have been high for the 24-item PM scale, α = .91 (Nash et al., 2012).

Physical abuse, sexual abuse, and neglect. The CAMI (DiLillo, 2003; DiLillo et al.,

2010) was also used to assess childhood experiences of physical abuse (CPA), sexual abuse

(CSA), and neglect. The physical and sexual abuse scales were scored in two different ways,

continuously as a measure of severity and dichotomously as an indicator of the presence or

absence of both types of child maltreatment. The physical and sexual abuse scales begin with

behaviorally specific screener questions that reveal whether or not participants experienced or

were exposed to various abusive acts before age 18. The screener questions are followed by

questions that inquire about the details of such events. The presence of certain features of the

events, which were empirically determined to reflect greater severity of CPA and CSA (e.g.,

frequency of the acts, nature of the acts, duration of the acts, whether injury resulted from the

acts, number of perpetrators, relationship to perpetrator[s]) are assigned a weighted score

reflecting abuse severity. The neglect scale was developed from an initial pool of 38 items and a

revised version of the CAMI neglect scale consisting of 20 items was created based on results of

an exploratory factor analysis (Nash et al., 2012). The revised CAMI neglect scale was used in

the present study and was scored continuously with higher mean scores indicating greater

16

severity. The CAMI has demonstrated excellent internal consistency for child sexual abuse (α =

.96) and child physical abuse (α = .86; DiLillo et al., 2009) and good internal consistency for

neglect (α = .88; Nash et al., 2012). Cronbach’s alpha for the neglect subscale of the CAMI in

the present sample was .93. The CAMI has demonstrated adequate test-retest reliability, with

kappa coefficients ranging from .74 to .95 for sexual abuse and .66 to .82 for physical abuse.

Good criterion validity is suggested by strong correlations with corresponding scores on the CTQ

(Bernstein & Fink, 1998; r = .55 for sexual abuse and r = .53 for physical abuse; DiLillo et al.,

2010).

Anxiety and depression. The short form version of the Depression, Anxiety, and Stress

Scale (DASS-21; P. F. Lovibond & Lovibond, 1995) is a 21-item self-report measure of

depression, anxiety, and stress symptom severity. The DASS-21 is designed to measure

symptoms that are common to depression and anxiety. Respondents are asked to indicate the

presence of symptoms over the previous week on a 4-point scale ranging from 0 (did not apply to

me at all over the last week) to 3 (applied to me very much or most of the time over the past

week). Each of the three subscales consists of 7 items and total subscale scores are calculated by

summing responses of each item. Higher scores indicate greater levels of distress. The subscales

of the DASS-21 have demonstrated adequate internal consistency (range = .80 to 93; Henry &

Crawford, 2005; Sinclair, Siefert, Slavin-Mulford, Stein, Renna & Blais, 2012). Cut-off scores

for the subscales of the DASS-21 have been established to characterize degree of severity

relative to the general population. The cut-off scores define severity categories for normal, mild

(i.e., above the population mean, not a mild level of a disorder), moderate, severe, and extremely

severe. The depression and anxiety subscales were used in the present study. Cronbach’s alphas

for the DASS-21 depression and anxiety subscales in this sample are .89 and .80, respectively.

Substance use. The Alcohol Use Disorders Identification Test (AUDIT; Saunders,

Aasland, Babor, de la Fuente, & Grant, 1993) is a 10-item self-report measure designed to

identify individuals with hazardous and harmful patterns of alcohol consumption. The AUDIT is

frequently used as a screening tool for detection of high-risk drinking, and researchers often use

the AUDIT as a continuous measure of hazardous or problematic alcohol use (e.g., Watkins,

Maldonado, & DiLillo, 2014). The AUDIT assesses three aspects of problematic drinking

including quantity and frequency of alcohol use, symptoms of alcohol use disorder, and

problems caused by alcohol use. Each item is scored using a scale ranging from 0 to 4. Total

17

scores range from 0 to 40 with higher scores indicating greater problematic alcohol use. Two

scores were used in the present study: (1) a sum score consisting of all 10 items, reflecting

problematic alcohol use and, (2) a dichotomous score using a cutoff of 8 or more indicating

problematic alcohol use (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001; Saunders et al.,

1993). The AUDIT has high internal consistency and it has been shown reliably identify patients

with hazardous drinking patterns (Babor et al., 2001; Saunders et al., 1993). Cronbach’s alpha

for the AUDIT in this sample is .83.

Heavy episodic drinking (HED) in the past year was assessed with four items. The items

assessed past year frequency of heavy drinking (four or more alcoholic drinks on a single

occasion), alcohol intoxication, being unable to remember things from the night before, and

being unable to consent to sexual activity due to alcohol consumption. Participants responded to

each item using a 5-point Likert scale ranging from 0 to 4 (0 = Never; 1 = Monthly or less; 2 = 2-

4 times per month; 3 = 2-3 times per week; 4 = 4 or more times a week). The four items were

highly correlated with each other, with correlations ranging between .41 and .81, so items were

averaged to create a single score of HED.

The Drug Use Questionnaire (DUQ; Hien & First, 1991) is an 18-item self-report

measure that assesses frequency and problems associated with drug use in the past year. For

items assessing the frequency of drug use, participants responded to each item using a 6-point

Likert scale ranging from 0 to 5 (0 = Never; 1 = One time; 2 = Monthly or less; 3 = 2-4 times a

month; 3 = 2-3 times a week; 4 = 4 or more times a week). For items assessing problems

associated with drug use, participants indicated how frequently they experienced each problem

using a 5-point Likert scale ranging from 0 to 4 (0 = Never; 1 = Less than monthly; 2 = Monthly;

3 = Weekly; 4 = Daily or almost daily). Cronbach’s alpha for the DUQ in the present sample was

.82. Three frequency scores (i.e., nicotine use, cannabis use, and drug use other than cannabis)

and a score for problems associated with drug use were computed. Nicotine and cannabis use

were each one item. Item responses were summed to create composite indices of the frequency

of drug use other than cannabis and drug use problems. The distribution of the drug use

frequency and drug use problems scores were highly skewed and the differences between

intervals on the scales were not equal, ruling out the possibility of treating them as interval

scales. These two composite scores were transformed into ordinal scales. For the frequency of

drug use other than cannabis, original scores were recoded into three categories (0 = no drug use;

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1-2 = low drug use; ≥ 3 = moderate to high drug use). The ordinal scale for drug use problems

also contained three categories (0 = no drug use problems; 1-2 = mild drug use problems; ≥ 3 =

moderate to severe drug use problems).

Results

A Missing Value Analysis (MVA; SPSS 2016) was used to determine the pattern of the

missing data. According to Little’s MCAR test (Little, 1988), data were not consistent with a

pattern of missing completely at random, χ2 (332) = 454.06, p < .001. Examination of the

missing data revealed that only 0.35% of values were missing and there no clear patterns of

missingness. Widaman (2006) recommended the use of single imputation when the amount of

missing data is relatively low (i.e., 1-2%). Given the lack of patterns of missingness and the

small amount of missing data, single imputation was used. Missing data were singly imputed at

the scale level using the expected maximization algorithm with 25 iterations (Graham, 2009;

Little & Rubin, 1987). Scale scores were calculated only if participants had complete data for at

least 80% of the measure (Graham, 2009).

All study variables were examined for statistical normality. All variables with the

exception of the DUQ drug use frequency and drug use problems fulfilled the statistical

assumption of normality. Drug use frequency other than cannabis and drug use problem severity

scores were transformed into categorical variables.

For all one-way MANOVAs in the present study, the assumption of homogeneity of

covariance matrices was violated as indicated by Box’s M statistic (ps < .001). Pillai’s Trace is

more robust than other test statistics and it is recommended when there is heterogeneity of

covariance matrices and group sizes are unequal (Olson, 1974; Pillai & Jayachandran, 1967). For

all follow up one-way ANOVAs, the assumption of homogeneity of variances was not met as

indicated by Levine’s Test (ps < .001). The Games-Howell post hoc test for pairwise

comparisons is robust to non-normality and controls Type I error rate when there are unequal

size groups with unequal variances (Games & Howell, 1976; Sauder & DeMars, 2019; Shingala

& Rajyaguru, 2015). Therefore, the Pillai’s Trace statistic and Games-Howell post hoc procedure

were used in the present study.

Confirmatory Factor Analysis

The CAMI PM scale was submitted to confirmatory factor analysis (CFA) to determine

whether the APSAC (1995) model fit the data. A CFA was performed using robust weighted

19

least squares (WLSMV) using MPlus Version 7.3 (Muthén & Muthén, 1998-2012). Multiple

indices of model fit were used and models are thought to fit the data well with the following:

non-significant chi-square, root mean square error of approximation (RMSEA; < .06; Hu &

Bentler, 1999; Steiger & Lind, 1980), weighted root-mean-square residual (WRMR; < 1.0;

Muthén & Muthén, 1998-2012; Yu, 2002), Tucker-Lewis index (TLI; > .95; Tucker & Lewis,

1973), and comparative fit index (CFI; > .95; Bentler, 1990; Hu & Bentler, 1999).

The initial CFA specified a five-factor model with 5 items indicating the Terrorizing

factor, 3 items indicating the Spurning factor, 5 items indicating the Exploiting/Corrupting

factor, 2 items indicating the Isolating factor, and 6 items indicating the Denying Emotional

Responsiveness factor. Correlations were allowed among the five factors. Three CAMI PM items

(items 1, 5, and 13) that did not clearly fit into any of the APSAC theoretical categories were

allowed to load freely onto the factors. This model acceptable fit to the data, 2 (227, N = 490) =

790.73, p < .001; CFI = .97; TLI = .96; RMSEA = .07; WRMR = 1.23. In this model, the three

items allowed to load freely did not load onto any of the factors (factor loadings were low).

Examination of these three items revealed they were three of the four CAMI PM items that

comprised a factor identified by Nash et al. (2012; Demanding/Rigid) that was not one of the PM

subtypes in the APSAC (1995) definition of PM. Examination of the fourth item in Nash et al.’s

(2012) Demanding/Rigid factor, item 9, revealed adequate, although not optimal, factor loading

(.76) onto the Exploiting/Corrupting factor. Elimination of the four items determined by Nash et

al. (2012) to comprise the Demanding/Rigid factor resulted in a model with acceptable fit to the

data, 2 (160, N = 490) = 481.05, p < .001; CFI = .98; TLI = .98; RMSEA = .06; WRMR = 1.06,

and all factor loadings were substantial (>.72). Results of the CFA indicated the CAMI PM items

loaded adequately onto the PM subtypes as defined by APSAC (1995). Therefore, I determined

the CAMI PM accurately measured the APSAC (1995) model. Table 1 displays the standardized

factor loadings for the final model, which was used in subsequent analyses.

Person-Centered Analysis of Childhood Psychological Maltreatment

In order to identify naturally occurring groups of participants based on their exposure to

different subtypes of PM in childhood, I conducted a latent profile analysis (LPA) using MPlus

Version 7.3 (Muthén & Muthén, 1998-2012). LPA is a person-centered approach to analysis that

focuses on similarities and differences among individuals rather than variables (Berlin, Williams,

& Parra, 2014). LPA allows for simultaneous examination of multiple PM subtypes, accounting

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for the complexity of PM experiences. LPA is a statistical approach that can be used to classify

individuals with similar patterns into unobserved groups (Berlin et al., 2014). Thus, in LPA a

person-centered categorical latent variable is derived. Participants are assigned to one mutually

exclusive profile based on their responses to observed variables of interest (i.e., PM variables;

Berlin et al., 2014; Hazen, Connelly, Roesch, Hough, & Landsverk, 2009).

The groups or latent profiles were determined using multiple indicators of model fit as

well as theoretical justification. Indicators of fit that were used included entropy (Ramaswamy,

DeSarbo, Reibstein, & Robinson, 1993), Akaike Information Criterion (AIC; Akaike, 1974;

1987), Bayesian Information Criteria (BIC; Schwarz, 1978), sample-size adjusted BIC (Sclove,

1987), Lo-Mendell-Rubin Adjusted Likelihood Ratio Test (LMR; Lo, Mendell, & Rubin, 2001),

Vuong-Lo-Mendell-Rubin Likelihood Ratio Test (VLMR; Lo et al., 2001), and Bootstrap

Likelihood Ratio Test (BLRT; McLachlan & Peel 2000). Entropy assesses classification

accuracy and can range from 0 to 1. Higher scores indicate greater classification accuracy (i.e.,

the probability that a participant would be in one class versus another; Berlin et al., 2014). The

AIC, BIC, and sample-size adjusted BIC are measures of relative fit, which allow for comparison

of models with different numbers of classes. Lower values on these fit indices indicate better

model fit. LMR, VLMR, and BLRT are likelihood ratio tests that compare the estimated model

with k classes to a model with k – 1 classes. Significant p-values on these tests indicate that the

model with k classes reflects a statically significant improvement in fit than the model with k – 1

classes.

Maximum likelihood estimation with robust standard errors (i.e., MLR) was used. LPA

models were estimated using 100 initial stage random starts and 20 final stage optimizations to

determine if the best log-likelihood value was obtained and replicated. Additionally, 100

bootstrap draws with 100 random stage starts and 20 final stage optimizations were used for

BLRTs. Several models were fit to the data in a hierarchical fashion, beginning with specifying a

1-class model and adding classes hierarchically until adding an additional class no longer

resulted in improved model fit. Model fit indices are displayed in Table 2.

The AIC, BIC, and sample-size adjusted BIC indicated a 4-class model. For the

likelihood-based tests (LMR and BLRT), Nylund, Asparouhov, and Muthén (2007) suggest that

the first occurrence of a nonsignificant p-value is a good indication to stop increasing the number

of classes examined. LMR and VLMR both suggested that the 3-class model provided a

21

significantly better fit than the 2-class model, and that the 4-class model did not provide a

statistically significant improvement over the 3-class model. The BLRT, however, indicated that

a 3-class model provided a significantly better fit than the 2-class model and that the 4-class

model provided a significantly better fit than the 3-class model. While simulation studies suggest

that the BLRT outperforms other likelihood ratio tests, simulations with sample sizes similar to

the present sample size indicate similar performance of the BLRT and the LMR (Nylund et al.,

2007). Examination of the 4-class model indicated that two of the four classes each contained

less than 6% of the sample (n = 29 and n = 28). These two small classes comprised one class in

the 3-class model. Researchers have suggested that small classes (i.e., classes with 5% or less of

the total sample) are too small and potentially unstable for the purpose of comparisons (McCrae,

Chapman, & Christ, 2006; Pears, Kim, & Fisher, 2008). The best entropy value was for the 4-

class model. Given the relatively small number of members in two of the classes in the 4-class

model and that these two classes appeared to be subgroups of one of the classes in the 3-class

model, I chose the 3-class model as the best fitting model.

The patterns of PM experiences for the three latent classes are displayed in Table 3 and

Figure 1; 62.0% (n = 304) of the sample was classified into Class 1, 27.1% (n = 133) were

classified into Class 2, and 10.8% (n = 53) were classified into Class 3. As can be seen in Figure

1, Class 1 had a high probability of reporting low levels of childhood PM, across all five PM

subtypes. Class 2 had a high probability of reporting moderate levels of PM and Class 3 had a

high probability of endorsing high PM exposure, particularly for terrorizing, spurning, and

emotional nonresponsiveness. Thus, the three latent classes were labeled the low, moderate, and

high PM groups, based on the average PM exposure of the group members.

Examination of Characteristics of the Three PM Groups

A one-way MANOVA1 was conducted to examine whether the three PM groups differed

significantly on the five subtypes of PM (see Table 3 and Figure 1). Results revealed a statistically

significant difference between the groups on the combined dependent variables, Pillai’s Trace = .96,

F(10, 968) = 89.03, p < .001, ηp2 = .48. Follow up univariate one-way ANOVAs were conducted. A

Bonferroni adjustment was made such that statistical significance was accepted when p < .01. There

1. Analyses were run with demographic characteristics as control variables. A similar pattern of

results was found with and without demographic variables. Results for analyses without control

variables are presented.

22

were statistically significant differences in adjusted means for terrorizing, F(2, 487) = 788.38, p <

.001, ηp2 = .76, spurning, F(2, 487) = 750.97, p < .001, ηp2 = .76, exploiting/corrupting, F(2, 487) =

115.03, p < .001, ηp2 = .32, isolating, F(2, 487) = 188.24, p < .001, ηp2 = .44, and emotional

nonresponsiveness, F(2, 487) = 215.10, p < .001, ηp2 = .47. Games-Howell post hoc comparisons

were made for all three PM groups. Results indicated that the three groups were significantly

different from each other on all five subtypes of PM (see Table 3). The low PM group had the lowest

levels of PM, the high PM group had the highest levels of PM, and the moderate PM group fell in

between the low and high PM groups.

Demographic characteristics. One-way ANOVA and chi-square or Fisher’s exact tests

were used to examine demographic differences among the three PM groups (see Table 4).

Results revealed no significant differences in demographic characteristics between the groups.

Co-Occurrence of other types of child maltreatment. A one-way MANOVA was

conducted to examine whether the three PM groups differed significantly on the severity of other

types of child maltreatment (i.e., neglect, CPA, and CSA; Table 5). Results revealed a

statistically significant difference between the PM groups on the combined dependent variables,

Pillai’s Trace = .50, F(6, 934) = 52.22, p < .001, ηp2 = .25. Follow up univariate one-way

ANOVAs were conducted, and a Bonferroni adjustment was made such that statistical

significance was accepted when p < .0167. There were statistically significant differences in

adjusted means for neglect severity, F(2, 468) = 148.96, p < .001, ηp2 = .39, CPA severity, F(2,

468) = 59.93, p < .001, ηp2 = .20, and CSA severity, F(2, 468) = 43.01, p < .001, ηp2 = .16.

Games-Howell post hoc comparisons were made for all three child maltreatment variables.

Results indicated that the three classes were significantly different from each other on neglect,

CPA, and CSA severity such that the low PM group had the lowest severity and the high PM

group had the highest severity on these three other types of child maltreatment. Figure 2 displays

standardized neglect, CPA, and CSA severity scores for the three PM groups.

Figure 3 displays the percent of participants in each PM group categorized as victims of

CSA and CPA. Approximately one-third of the low PM group was categorized as CSA and CPA

victims. Over half of the participants in the moderate PM group were victims of CSA and

approximately three-fourths were victims of CPA. Of the participants in the high PM group,

almost 80% were victims of CSA and over 95% were victims of CPA. Figure 4 displays the

percent of participants in the PM groups categorized as CSA and/or CPA victims, taking into

23

account the overlap between CSA and CPA. Therefore, Figure 4 contains information regarding

four mutually exclusive categories: No CSA or CPA, CSA only, CPA only, and the combination

of CSA and CPA. Almost 50% of the low PM group were not victims of CSA or CPA compared

to only 12.8% of the moderate PM group and none of the high PM group. Conversely, a majority

of the high PM group (75.5%) were classified as victims of both CSA and CPA compared to

45.9% of the moderate PM group and 16.4% of the low PM group

A series of binomial logistic regressions were performed to examine the effects of PM

group on the likelihood that participants would be classified as victims of CPA or CSA (Table

6). The logistic regression models predicting CPA victim status from the three pairs of PM

groups were all significant. The model predicting CPA victim status from the low PM group vs.

the moderate PM group was statistically significant, 2 (4) = 54.56, p < .001. The model

explained 15.4% (Nagelkerke R2) of the variance in CPA victim status and correctly classified

66.5% of cases. Participants in the moderate PM group had 4.97 times higher odds of being

classified as a victim of CPA than those in the low PM group. The model predicting CPA victim

status from the low PM group vs. the high PM group was statistically significant, 2 (4) = 74.75,

p < .001. The model explained 25.3% (Nagelkerke R2) of the variance in CPA victim status and

correctly classified 68.0% of cases. Participants in the high PM group had 6.59 times higher odds

of being classified as a victim of CPA than those in the low PM group. The model predicting

CPA victim status from the moderate PM group vs. the high PM group was statistically

significant, 2 (4) = 14.54, p < .001. The model explained 12.0% (Nagelkerke R2) of the variance

in CPA victim status and correctly classified 80.6% of cases. Participants in the high PM group

had 8.76 times higher odds to be classified as a victim of CPA than those in the moderate PM

group.

The logistic regression models predicting CSA victim status from the three pairs of PM

groups were also all significant. The model predicting CSA victim status from the low PM group

vs. the moderate PM group was statistically significant, 2 (4) = 25.89, p < .001. The model

explained 7.8% (Nagelkerke R2) of the variance in CSA victim status and correctly classified

64.8% of cases. Participants in the moderate PM group had 2.94 times higher odds of being

classified as a victim of CSA than those in the low PM group. The model predicting CSA victim

status from the low PM group vs. the high PM group was statistically significant, 2 (4) = 41.22,

p < .001. The model explained 14.8% (Nagelkerke R2) of the variance in CSA victim status and

24

correctly classified 69.2% of cases. Participants in the high PM group had 2.81 times higher odds

of being classified as a victim of CSA than those in the low PM group. The model predicting

CSA victim status from the moderate PM group vs. the high PM group was statistically

significant, 2 (4) = 7.43, p < .001. The model explained 5.4% (Nagelkerke R2) of the variance in

CSA victim status and correctly classified 64.5% of cases. Participants in the high PM group had

2.69 times higher odds of being classified as a victim of CSA than those in the moderate PM

group.

Figures 5 and 6 depict descriptive characteristics of CSA and CPA experiences,

respectively, of participants in the three PM groups identified as CSA or CPA victims. For each

abuse characteristic, the most severe category was identified, and participants received a code (0

= absent, 1 = present) for each characteristic. Many of the CSA victims in the high PM group

were exposed to the most severe forms of CSA. For example, 66.7% were abused by a family

member, 71.4% experienced penetration, 64.3% were physically held down by their

perpetrator(s), and 45.2% were abused over ten times. Of CSA victims in the low and moderate

PM groups, 29.3% and 46.2% were abused by a family member, 55.6% and 66.7% experienced

penetration, 47.5% and 47.4% were physically held down by their perpetrator(s), and 21.2% and

32.1% were abused over ten times, respectively.

Examination of the characteristics of CPA experienced by participants in this sample

revealed that almost all of the participants in the three PM groups who were physically abused

reported they were abused by a parent (96.0% - 97.3%). Across PM groups, a majority of CPA

victims experienced at least two years of physical abuse (over 90% for the moderate and high

PM groups and almost 85% for the low PM group). Similar to the findings for CSA, many of the

CPA victims in the high PM group were exposed to the most severe forms of CPA. For example,

almost 95% of CPA victims in the high PM group were choked, beaten, or burned while

approximately 80% of the moderate PM group and 50% of the low PM group experienced this

type of abuse. In addition, over 20% of CPA victims in the high PM group were seriously injured

(e.g., bone fractures, burns, internal injuries, paralysis) while 7.1% of the moderate PM group

and less than 1% of the low PM group were seriously injured.

Intercorrelation of Variables

Table 7 contains bivariate correlations, means, and standard deviations for the continuous

outcome variables of adult psychological functioning for the entire sample (i.e., depression,

25

anxiety, problematic alcohol use, heavy episodic drinking, cannabis use frequency, nicotine use

frequency). All outcome variables were significantly correlated with each other. Table 8 contains

bivariate correlations between the continuous outcome variables and demographic

characteristics. Age was not significantly correlated with any of the outcome variables.

Identifying as White was significantly positively correlated with problematic alcohol use, heavy

episodic drinking, and nicotine use frequency. Identifying as African American was negatively

correlated with problematic alcohol use, heavy episodic drinking, nicotine use frequency, and

depression. Identifying as Latina was positively correlated with depression and anxiety.

Identifying as Native American was negatively correlated with heavy episodic drinking.

Identifying as another racial/ethnic identity was negatively correlated with problematic alcohol

use and heavy episodic drinking. Being a student was negatively correlated with all outcome

variables except anxiety. Having children was positively correlated with anxiety and nicotine use

frequency and negatively correlated with problematic alcohol use and heavy episodic drinking.

Depression and Anxiety

A one-way MANOVA was conducted to examine whether the three PM groups differed

significantly on symptoms of depression and anxiety. Results revealed a statistically significant

difference between the groups on the combined dependent variables, Pillai’s Trace = .12, F(4,

974) = 14.89, p < .001, ηp2 = .06. Follow up univariate one-way ANOVAs were conducted. A

Bonferroni adjustment was made such that statistical significance was accepted when p < .025.

There were statistically significant differences in adjusted means for symptoms of depression,

F(2, 487) = 19.55, p < .001, ηp2 = .07, and symptoms of anxiety, F(2, 487) = 29.98, p < .001, ηp2

= .11. Games-Howell post hoc comparisons were made for depression and anxiety. Results

indicated that symptoms of depression and anxiety were both significantly lower for the low PM

group compared to the moderate and high PM groups, but there was no significant difference

between the moderate and the high PM groups (see Table 9).

The association between the PM groups and symptoms of depression and anxiety were

also examined utilizing the categorical severity indicators for depression and anxiety. Using

previously defined cut-off scores (S. H. Lovibond & Lovibond, 1995), scores for depression and

anxiety were categorized as normal, mild to moderate, and severe to extremely severe.

Examination of depression severity scores for the full sample revealed 68.0% (n = 333) reported

symptoms in the normal range, 21.6% (n = 106) reported symptoms in the mild to moderate

26

range, and 10.4% (n = 51) reported symptoms in the severe to extremely severe range. For

symptoms of anxiety, 64.9% (n = 318) of the full sample reported anxiety in the normal range,

21.0% (n = 103) reported symptoms falling the mild to moderate range, and 14.1% (n = 69)

reported symptoms in the severe to extremely severe range. Chi-square tests of independence

were conducted between PM group and depression and anxiety scored categorically (Tables 10

and 11). For both analyses, all expected cell frequencies were greater than five. Results revealed

a statistically significant association between the PM groups and depression severity, 2(4) =

49.34, p < .001, and between the PM groups and anxiety severity, 2(4) = 45.04, p < .001,

although the associations were small (Cohen, 1988), Cramer’s V = .22 and .21, respectively. For

depression severity, examination of adjusted residuals indicated that participants in the low PM

group were more likely than expected to report symptoms of depression in the normal range. The

low PM group was more likely than expected to have participants with depression in the normal

range than the moderate and high PM groups. The moderate PM group was more likely than

expected to have participants who reported symptoms in the mild to moderate range. Finally, the

high PM group was more likely to have participants with symptoms in the severe to extremely

severe range. For anxiety, examination of adjusted residuals indicated that the low PM group was

more likely than expected to have participants who reported symptoms of anxiety in the normal

range and less likely to have participants with mild to moderate and severe to extremely severe

anxiety. On the other hand, the moderate and high PM groups were more likely than expected to

have participants with anxiety in the severe to extremely severe range.

Substance Use

Alcohol use. A one-way MANOVA was conducted to examine whether the three PM

groups differed significantly on problematic alcohol use and heavy episodic drinking. Results

revealed a statistically significant difference between the groups on the combined dependent

variables, Pillai’s Trace = .04, F(4, 974) = 4.63, p = .001, ηp2 = .02. Follow-up univariate one-

way ANOVAs were conducted, and a Bonferroni adjustment was made such that statistical

significance was accepted when p < .025. There were statistically significant differences in

adjusted means for problematic alcohol use, F(2, 487) = 8.21, p < .001, ηp2 = .03, and heavy

episodic drinking, F(2, 487) = 6.23, p = .002, ηp2 = .03 (Table 12). Games-Howell post hoc

comparisons indicated that problematic alcohol use scores were significantly different for the

low versus the moderate and high PM groups, such that the low PM group had lower problematic

27

alcohol use scores. Problematic alcohol use was not significant different for the moderate

compared to the high PM group. Heavy episodic drinking scores were significantly lower in the

low PM group compared to the high PM group, but were not significantly different for the low

versus the moderate or the moderate versus the high PM group.

A chi-square test of independence (Table 13) was conducted between the PM groups and

the AUDIT scored dichotomously based on the recommended cutoff ( 8) indicating the

presence or absence of problematic alcohol use. All expected cell frequencies were greater than

five. There was a statistically significant association between the PM groups and problematic

alcohol use, 2(2) = 8.96, p = .011. The association was small (Cohen, 1988), Cramer’s V = .14.

Results revealed that the low PM group was less likely than expected to have problematic

alcohol use, with only 17.5% of the low PM group obtaining AUDIT scores greater than or equal

to eight. A trend suggested that the moderate and high PM groups may have been more likely

than expected to report problematic alcohol use, although results did not reach statistical

significance.

Drug Use. A one-way MANOVA was conducted to examine whether the three PM

groups differed significantly on cannabis and nicotine use frequency in the past year. Results

revealed a statistically significant difference between the groups on the combined dependent

variables, Pillai’s Trace = .07, F(4, 974) = 8.69, p < .001, ηp2 = .03. Follow up univariate one-

way ANOVAs were conducted. A Bonferroni adjustment was made such that statistical

significance was accepted when p < .025. There were statistically significant differences in

adjusted means for cannabis use frequency, F(2, 487) = 6.72, p = .001, ηp2 = .03, and nicotine use

frequency, F(2, 487) = 16.96, p < .001, ηp2 = .07. Games-Howell post hoc comparisons were

made for cannabis and nicotine use frequency (see Table 14). The moderate PM group reported

significantly more frequent cannabis use than the low PM group. The high PM group did not

differ from the low or moderate PM groups; however, the pattern of results was in the expected

direction. In addition, Games-Howell is affected by group size and within group variance such

that power is the lowest for the smallest groups with the largest variance (Sauder & DeMars,

2019). Nicotine use frequency was significantly higher in the moderate and high PM groups

compared to the low PM group. While the difference between the moderate and high PM groups

on nicotine use did not reach statistical significance, the pattern was consistent with what one

28

would expect given previous research finding positive associations between PM and increased

tobacco or nicotine use (Duke, 2018; Hayre et al., 2019; Moran et al., 2004).

A chi-square test of independence was conducted between PM group and drug use

frequency scored categorically (see Table 15). One cell had expected count of less than five,

otherwise all expected cell frequencies were greater than five, which meets the statistical

assumption that at least 80% of cells have expected values of five or more (e.g., McHugh, 2013).

Results revealed a statistically significant association between PM group and drug use frequency

2(4) = 23.29, p < .001, although the strength of the association was small (Cohen, 1988),

Cramer’s V = .15. The low PM group was more likely than expected to report no drug use and

less likely to report moderate to high drug use in the past year. The moderate and high PM

groups were both less likely than expected to have no drug use in the past year and more likely to

have moderate to high drug use. Approximately 20% of the high PM group reported moderate to

high drug use compared to only 5.9% of the low PM group.

A chi-square test of independence was conducted between PM group and drug use

problems scored categorically (Table 16). All expected cell frequencies were greater than five.

There was a statistically significant association between PM group and drug use problems 2(4)

= 39.22, p < .001. The association was small (Cohen, 1988), Cramer’s V = .20. The low PM

group was more likely than expected to report no drug use problems in the past year and less

likely than expected to report any drug use problems (i.e., mild and moderate to high drug use

problems). The moderate and high PM groups were less likely than expected to report no drug

use problems and more likely to report moderate to high drug use problems. The moderate PM

group was also more likely to report mild drug use problems than expected. Approximately 80%

of the low PM group reported no drug use problems compared to 58.0% and 50.9% of the

moderate and high PM groups, respectively.

Controlling for Other Types of Child Maltreatment

Other types of child maltreatment were included in the models examining differences

between the three PM groups on different indicators of adult psychological functioning. Given

that the PM group variable was categorical, I chose to use CSA and CPA victim status as control

variables instead of using CSA and CPA severity scores. When CSA and CPA victim status were

included as control variables in the one-way MANCOVAs examining differences between PM

29

groups on the dependent variables, some group differences still emerged, but the magnitude of

the differences decreased.

The one-way MANCOVA with depression and anxiety as dependent variables revealed a

statistically significant difference between the PM groups on the combined dependent variables,

Pillai’s Trace = .05, F(4, 968) = 6.08, p < .001, ηp2 = .02. Follow up univariate one-way

ANOVAs with a Bonferroni adjustment revealed a significant difference among the PM groups

on depression, F(2, 484) = 5.27, p = .005, ηp2 = .02, and anxiety, F(2, 484) = 12.03, p < .001, ηp2

= .05 (see Table 17). Games-Howell post hoc comparisons indicated that the low PM group was

significantly different from the high PM group on depression. For anxiety, the low PM group

was significantly lower than the moderate and high PM group. There was also a statistically

significant difference between victims and nonvictims of both CSA, Pillai’s Trace = .05, F(2,

483) = 11.43, p < .001, ηp2 = .05, and CPA, Pillai’s Trace = .02, F(2, 483) = 3.63, p = .027, ηp2 =

.02, on the combined dependent variables (depression and anxiety). Results of follow up

univariate one-way ANOVAs with a Bonferroni adjustment revealed that being a victim of CSA

was associated with higher levels of depression, F(1, 484) = 14.30, p < .001, ηp2 = .03, and

anxiety, F(1, 484) = 21.75, p < .001, ηp2 = .04. Being a victim of CPA was associated with higher

levels of depression, F(1, 484) = 7.20, p = .008, ηp2 = .02, but not with anxiety, F(1, 484) = 2.20,

p = .139, ηp2 = .01.

The one-way MANCOVA with problematic alcohol use and heavy episodic drinking as

dependent variables revealed a statistically significant difference between the PM groups on the

combined alcohol use variables, Pillai’s Trace = .02, F(4, 968) = 2.56, p = .037, ηp2 = .01. Follow

up univariate one-way ANOVAs with a Bonferroni adjustment revealed a significant difference

among the PM groups on problematic alcohol use, F(2, 484) = 3.86, p = .022, ηp2 = .02, but

results of Games-Howell post hoc comparisons did not reach statistical significance (see Table

18). There was no significant difference among the PM groups on heavy episodic drinking, F(2,

484) = 3.63, p = .027, ηp2 = .02. In addition, CSA and CPA victim status were not significantly

associated with problematic alcohol use, Pillai’s Trace = .00, F(2, 483) = .54, p = .582, ηp2 = .00,

and Pillai’s Trace = .00, F(2, 483) = .71, p = .492, ηp2 = .00, respectively.

For cannabis and nicotine use frequency, results of the one-way MANCOVA controlling

for CSA and CPA victim status revealed a statistically significant difference between the PM

groups on the combined dependent variables, Pillai’s Trace = .03, F(4, 968) = 3.13, p = .014, ηp2

30

= .01. Table 23 displays results of follow up univariate one-way ANOVAs with a Bonferroni

adjustment. Results revealed a significant difference among the PM groups for nicotine use

frequency, F(2, 484) = 5.42, p = .005, ηp2 = .02, but no significant difference emerged among the

PM groups for cannabis use frequency, F(2, 484) = 2.70, p = .068, ηp2 = .01. Games-Howell post

hoc comparisons indicated there was a significant difference between the low and high PM

groups on nicotine use frequency, but not between the low and moderate or the moderate and

high PM groups. Results also indicated that being a victim of CSA was associated with greater

frequency of nicotine use, F(1, 484) = 10.75, p = .001, ηp2 = .02, but being a victim of CSA was

not associated with cannabis use frequency, F(1, 484) = 4.17, p = .04, ηp2 = .01. Being a victim

of CPA was not significantly associated with either nicotine use frequency, F(1, 484) = 4.42, p =

.036, ηp2 = .01, or cannabis use frequency, F(1, 484) = .67, p = .414, ηp2 = .00.

Discussion

The present study explored the natural co-occurrence of psychological maltreatment

subtypes experienced in childhood and associated indicators of adult psychological functioning

in a geographically diverse sample of young women. To my knowledge, this is the first study to

use a person-centered approach to examine the co-occurrence of PM subtypes. According to the

most widely used and agreed upon definition of PM outlined by APSAC (1995), PM consists of

five distinct subtypes: spurning, terrorizing, exploiting/corrupting, isolating, and denying

emotional responsiveness. Results of the CFA in the present study supported the use of the

CAMI PM scale to adequately measure the five PM subtypes based on the APSAC (1995)

model. Using a person-centered analysis, I explored the experiences of different PM subtypes

and severity levels reported by women in the current sample. Based on previous research

indicating differences in the rate and co-occurrence of PM subtypes (e.g., Baker & Ben-Ami,

2011; Taussig & Culhane, 2010; Trickett et al., 2009), I expected to find groups of participants

based on exposure to different PM subtypes and levels of severity. In contrast to hypotheses,

however, I found evidence of a unitary construct of PM that differed by severity of PM exposure,

but not by subtype of PM experienced. Results indicated the best fit for the data was a three-class

model, with the three latent classes reflecting exposure to low, moderate, and high PM severity

across subtypes such that the high PM group was exposed to severe spurning, terrorizing,

exploiting/corrupting, isolating, and denying emotional responsiveness. I did not find evidence of

different types of PM experiences, instead I found participants were exposed to different levels

31

of PM severity across all five subtypes. Therefore, differences between the PM groups on

indicators of adult psychological functioning were driven by differences in PM severity rather

than by exposure to different PM subtypes.

While previous studies have found evidence to suggest that different subtypes of PM

have different psychological effects (e.g., Allen, 2008; Brassard & Donovan, 2006; Shaffer et al.,

2009; White et al., 2016), the current results cannot be directly compared to these past studies

due to important methodological differences. First, no prior studies have used a person-centered

analysis to explore participants’ experiences of different subtypes of PM. In addition, the vast

majority of research on PM has failed to include all five PM subtypes. Research has tended to

focus on terrorizing and spurning, while neglecting other subtypes (Baker, 2009; Hamilton et al.,

2013; Potthast et al., 2014; Shin et al., 2015). Measures of PM have typically been combined into

a single PM variable (e.g., Dubowitz et al., 2016; Lewis et al., 2019), obscuring effects of any

specific PM subtype. Furthermore, researchers have often used dichotomous indicators of the

presence/absence of PM or PM subtypes (e.g., Taussig & Culhane, 2010; Trickett et al., 2009),

ignoring the fact that PM exists along a continuum of severity. As discussed earlier, many

researchers advocate for measuring the severity of child maltreatment exposure and this is

especially true for PM as it may be the most difficult form of maltreatment to dichotomize

(Clemmons et al., 2007; Litrownik et al., 2005; Newcomb & Locke, 2001; Wekerle, 2011). In

light of the current findings of PM groups based on severity across all subtypes, discussion of

results cannot address effects of specific PM subtypes.

Adult Psychological Functioning

Results of the present study are consistent with previous research finding that PM is

associated with decreased psychological functioning in adulthood (e.g., Hart et al., 1998; Miller-

Perrin et al., 2009; Norman et al., 2012; Spinazzola et al., 2014). Results generally suggest a

positive association between PM severity and psychological symptoms. Participants in the low

PM group tended to report the least distress while participants in the high PM group tended to

report the most distress and the most severe outcomes (i.e., more severe symptoms of depression

and anxiety, greater substance use, and more severe substance use problems).

Depression and anxiety. Results of the present study revealed significant differences in

symptoms of depression and anxiety between the PM groups. Using continuous severity scores

for depression and anxiety, results indicated that scores were significantly lower for the low PM

32

group compared to both the moderate and high PM groups. The low PM group reported very low

levels of depression and anxiety, with scores that have been categorized as falling in the normal

range, indicating that the scores are similar to the population mean (S. H. Lovibond & Lovibond,

1995). Depression and anxiety scores for the high PM group fall in the high end of the mild

range, suggesting symptoms are above the population mean but are mild compared to people

typically seeking treatment. The lack of significant differences between the moderate and high

groups on symptoms of depression and anxiety may have been due to a lack of power given the

small sample size and relatively small number of participants in the high PM group. The pattern

of results, which suggests a positive association between PM severity and symptoms of

depression and anxiety, is in the expected direction and is consistent with previous research (e.g.,

Crow et al., 2014; Norman et al., 2012; Spinazzola et al., 2014).

When the distribution of scores was maintained with the use of categorical indicators of

depression, a more nuanced picture emerged. Specifically, the high PM group reported more

severe to extremely severe depression than expected while the moderate group was more likely

to report mild to moderate depression. Comparison of the severity of depression and anxiety

symptoms obtained in the present study to those obtained in previous studies using the DASS

(Crawford & Henry, 2003; Wardenaar et al., 2018) indicates a larger percentage of participants

in the current sample reported any symptoms (mild and above) of depression and anxiety. In a

general population sample of adults in the UK, Crawford and Henry (2003) found that 18.3%

reported symptoms of depression that were at least mild and 11% reported anxiety that was in the

mild range or greater. Similarly, in a sample of almost 8,000 Dutch adults, 26.1% reported

depression in the mild range and above and 15.2% reported anxiety that was in the mild range or

greater (Wardenaar et al., 2018). The high PM group seems to stand out with regard to symptoms

of depression and anxiety as approximately 30% of participants in the high PM group reported

depression and anxiety in the severe to extremely severe range compared to rates of 5.8% and

7.2% for depression and 5.2% and 4.6% for anxiety reported by adults in Europe (Crawford &

Henry, 2003; Wardenaar et al., 2018, respectively). Examination of the frequencies of depression

scores obtained by participants in the current study suggests that more severe PM is associated

with more severe depression in almost a gradient of severity for some individuals. Current results

seem to support findings of Bifulco et al. (2002) who found that PM severity was associated with

chronic or recurrent major depression in a community sample of women. For anxiety, the

33

analysis using the categorical severity variable indicated that women in the moderate and high

PM groups reported more severe to extremely severe anxiety than expected. This finding may

suggest a threshold level of PM exposure (i.e., somewhere between moderate and severe PM) is

associated with an increased likelihood of severe anxiety in adulthood.

Substance use. Given the inconsistent findings regarding the relation between PM and

substance use in the literature, the current study sought to clarify this relation by examining

different PM subtypes and several substance use variables. In general, results for substance use

were largely consistent with previous studies that have found a positive association between PM

and substance use (e.g., Dube et al., 2002; Rosenkranz et al., 2012; Spinazzola et al., 2014;

Taillieu et al., 2016).

Alcohol use. With regard to alcohol use, participants in the moderate and high PM groups

reported more severe problematic drinking than those in the low PM group. Similarly, when

problematic alcohol use was scored dichotomously, participants in the low PM group were less

likely than expected to report problematic alcohol use. Although not reaching statistical

significance, trend-level results suggest the moderate and high PM groups may have been more

likely than expected to have participants with problematic alcohol use. For heavy episodic

drinking, participants in the high PM group reported engaging in heavy episodic drinking more

frequently in the past year than those in the low PM group. Interestingly, participants in the

moderate group did not differ significantly from those in either the low or high group. These

findings may be evidence for a threshold level of PM exposure above which women are at

increased risk for alcohol use problems in early adulthood. On the other hand, similar to the

findings for depression and anxiety, the lack of significant differences may be due to a lack of

power. The pattern of results does suggest that the severity of PM exposure is positively

associated with the severity of alcohol problems and the frequency of heavy episodic drinking.

This pattern is consistent with previous research demonstrating an association between alcohol

use or alcohol-related problems and PM severity (Potthast et al., 2015; Schwandt, Heilig,

Hommer, George, & Ramchandani, 2013).

Cannabis and nicotine use. Examination of the pattern of results for cannabis and

nicotine use suggests that more severe PM is associated with more frequent cannabis and

nicotine use. Although not all of the comparisons between groups reached statistical significance,

the pattern of results was in the expected direction based on previous research finding positive

34

associations between PM and cannabis use (Banducci, Felton, Bonn-Miller, & Lejuez, 2018;

Duke, 2018; Hayre et al., 2019) and tobacco or nicotine use (Duke, 2018; Hayre et al., 2019;

Moran et al., 2004). The ability to make direct comparisons across studies is limited, however,

as the majority of previous studies (e.g., Banducci et al., 2018; Duke, 2018; Hayre et al., 2019;

Moran et al., 2004) did not include all PM subtypes, instead they included only spurning and/or

terrorizing. In addition, previous studies have tended to use adolescent samples (Banducci et al.,

2018; Duke, 2018; Hayre et al., 2019; Moran et al., 2004) and dichotomous indicators of PM

exposure (Duke, 2018; Moran et al., 2004). One recent study by Dubowitz et al. (2016) found

contradicting evidence indicating that PM (i.e., terrorizing and spurning) was not associated with

adolescent marijuana use; however, all subtypes of PM were not included and measurement of

child maltreatment was based on CPS reports, which tend to underestimate the prevalence and

extent of PM exposure (Sedlak et al., 2010; Trickett et al., 2009). Thus, the pattern of results for

cannabis and nicotine use seems to be consistent with the majority of previous research, which

has revealed positive associations between PM and increased cannabis and nicotine use.

Drug use. The association between PM and drug use has been largely neglected in the

literature. The available literature suggests that PM is associated with increased drug use and

increased drug use problems. However, past studies have typically combined drug use with the

use of cannabis and other types of substances (e.g., alcohol, nicotine) to create a single substance

use frequency or problems variable (e.g., Moran et al., 2004; Rosenkranz et al., 2012; Taillieu et

al., 2016). In addition, a majority of previous research has used dichotomous variables for PM as

well as for drug use (Dube et al., 2003; Post et al., 2015; Taillieu et al., 2016). In the present

study, participants in the moderate and high PM groups were more likely than expected to report

moderate to high frequency of drug use other than cannabis in the past year (e.g., use of one drug

2-4 times per month to 4 or more times per week), while those in the low PM group were more

likely to report no drug use. Results suggest that exposure to moderate or high PM severity does

not increase risk of occasional or experimental drug use (e.g., use of one drug once in the past

year). In contrast, exposure to moderate or high PM does increase the chance of past year regular

or frequent drug use (i.e., use of three drugs one time each, use of one drug 2-4 times per month,

or multiple drugs four or more times per week). Examination of drug use problems revealed a

slightly different pattern of results. Participants in the low PM group were more likely than

expected to report no drug use problems. Those in the moderate PM group were more likely than

35

expected to report mild drug use problems and moderate to severe drug use problems. Finally,

participants in the high PM group were more likely than expected to report moderate to severe

drug use problems. These results suggest that the severity of PM exposure is positively

associated with the severity of drug use problems in early adulthood. Taken together, these

results suggest that exposure to moderate or high PM severity increases the risk of regular to

frequent drug use, which increases the risk of drug use problems; however, individuals with the

most severe PM are at increased risk of the most severe drug use problems.

Co-Occurrence of Other Types of Child Maltreatment

In the current study, PM did not occur in isolation, as the women who reported exposure

to PM also reported experiencing neglect, CSA, and CPA. Different types of child maltreatment

are highly correlated and frequently co-occur (Cecil et al., 2017; Dong et al., 2004; Higgins &

McCabe, 2000), that maltreated children frequently experience more than one type of

maltreatment (Cecil et al., 2017; Herrenkohl & Herrenkohl, 2009), and exposure to one type of

child maltreatment increases the likelihood of exposure to another type of maltreatment (Arata,

Langhinrichsen-Rohling, Bowers, & O’Brien, 2007; Finkelhor et al., 2009; Herrenkohl and

Herrenkohl, 2009; Higgins & McCabe, 2001). In the current study, a majority of participants in

the moderate and high PM groups were victims of CPA and/or CSA, which is consistent with

research finding that PM has a particularly high rate of co-occurrence with other types of child

maltreatment (Berzenski et al., 2019; Bifulco et al., 2002; Cicchetti & Rogosch, 2001; McGee et

al., 1997; Taillieu et al., 2016; Trickett et al., 2009). In addition, the likelihood of being a victim

of CPA and/or CSA increased from the low to the moderate to the high PM group. The relation

between PM and CPA was particularly noteworthy. The odds of being a victim of CPA increased

by 5 times from the low to the moderate group and over 8.5 times from the moderate to the high

group. The rate of CPA in the moderate and high PM groups supports previous research finding

a particularly high co-occurrence between PM and CPA (Claussen & Crittenden, 1991;

Crittenden et al., 1994; Dong et al., 2004; Hodgdon et al., 2018; McGee et al., 1997; Rice et al.,

2001). Thus, for the women in the current sample, as the severity of PM exposure increased, the

likelihood of experiencing CPA and/or CSA also increased.

In the present study, the increasing rate of CPA and CSA across PM groups corresponded

to a decrease in psychological functioning. This finding can be conceptualized as the effect of

cumulative abuse, in which the number of types of maltreatment that are experienced is

36

associated with worse mental health correlates (Cecil et al., 2017; Martin, Cromer, DePrince, &

Freyd, 2011; Scott-Storey, 2011). Researchers have found associations between the number of

different types of adverse or traumatic experiences and depression and/or anxiety (Arata,

Langhinrichsen-Rohling, Bowers, & O’Farrill-Swails, 2005; Bifulco et al., 2002; Chapman et al.,

2004; Edwards et al., 2003; Schilling, Aseltine, & Gore, 2007; Shin, McDonald, & Conley,

2018; Teicher, Tomoda, & Anderson, 2006), alcohol use and/or alcohol use problems (Dube et

al., 2002; Hughes et al., 2017), drug use and/or drug use problems (Dube et al., 2003; Hughes et

al., 2017; Scheidell et al., 2018; Schilling et al., 2007; Vilhena-Churchill & Goldstein, 2014),

trauma symptoms (Clemmons et al., 2007; Sundermann & DePrince, 2015), and emotion

dysregulation (Sundermann & DePrince, 2015; Vilhena-Churchill & Goldstein, 2014). Results of

the current study provide further support for the adverse effect of cumulative abuse on adult

psychological functioning.

In the current study, not only did the rate of exposure to other types of child maltreatment

increase across PM groups, the severity of neglect, CPA, and CSA also increased from the low to

the moderate to the high PM group. Given that results generally suggested a positive relation

between PM severity and negative outcome, results were largely consistent with literature

indicating that more severe child maltreatment is associated with decreased psychological

functioning (e.g., Chapman et al., 2004; Higgins, 2004; Norman et al., 2012). Participants in the

high PM group were exposed to the most severe PM as well as the most severe neglect, CPA,

and CSA compared to the moderate and low PM groups. The severity of CPA and CSA was

observed in the continuous severity scores and in the specific trauma characteristics (Figures 5

and 6). The characteristics of CPA and CSA experiences appeared to increase in severity as PM

severity increased from the low to the moderate to the high PM group. The relation between PM

group and the severity of neglect, CPA, and CSA in combination with the rate of CPA and CSA

exposure in the PM groups, suggests an association between maltreatment severity and

cumulative abuse or exposure to multiple types of child maltreatment. This pattern of results is

consistent with those found by Clemmens et al. (2007), who identified a positive association

between the number of child maltreatment types and more severe maltreatment.

Child maltreatment type versus severity. While some researchers have debated the

relative importance of specific child maltreatment experiences versus the number of types of

abuse experienced, others have posited that child maltreatment severity, regardless of

37

maltreatment type, best characterizes individuals’ experiences. Results of the present study

suggest child maltreatment severity may better capture experiences of maltreatment than

maltreatment type, providing support for the argument that child maltreatment types should be

assessed and analyzed as a unitary construct. Higgins (2004) explored whether maltreatment

experiences were best classified based on exposure to distinct types of maltreatment or a single,

overarching construct of child maltreatment. Higgins (2004) found evidence to suggest that

experiences of child maltreatment are better characterized by overall severity of maltreatment

experiences (low, moderate, and high) than by maltreatment type. In comparing results from the

current study to those reported by Higgins (2004), it is important to note several differences in

methodology. First, I did not include other types of child maltreatment in analyses aimed at

identifying underlying groups of participants (i.e., LPA). In addition, Higgins’ (2004) study did

not include PM subtypes and used cluster analysis rather than a person-centered approach to

characterize participants’ experiences. Several studies have used person-centered approaches

(i.e., latent class and latent profile analyses) and identified groups of participants based on child

maltreatment severity across maltreatment types (e.g., Cecil, Viding, Barker, Guiney, &

McCrory, 2014; Lin et al., 2016), although none have included PM subtypes. Lin et al. (2016)

used LPA to classify 256 Chinese children with oppositional defiant disorder into groups based

on exposure to emotional abuse (i.e., spurning), emotional neglect (i.e., terrorizing), and physical

abuse. In another study, Cecil and colleagues (2014) used LPA to categorize experiences of

participants from a community sample of high-risk adolescents and young adults based on

exposure to emotional (i.e., spurning), physical, and sexual abuse and physical and emotional

neglect (i.e., terrorizing). The results of both studies revealed three profiles of child maltreatment

reflecting exposure to low, moderate, and high maltreatment severity across maltreatment types.

While the aim of the present study was not to identify groups based on the severity of exposure

to different types of child maltreatment, results nonetheless appear to support past findings

indicating that people may be best classified based on the severity rather than type of

maltreatment exposure.

Isolating effects of PM. It is not clear what accounts for the differences in outcomes

between the PM groups due to the co-occurrence of other types of child maltreatment in the

current sample. The differences in adult psychological functioning may be due to the effect of

PM alone, the effect of another type of child maltreatment (i.e., neglect, CSA, or CPA), or the

38

effect of exposure to multiple types of maltreatment (i.e., cumulative abuse). One approach to

identifying which child maltreatment experience is contributing the most to negative outcomes is

to statistically control for other types of maltreatment in an attempt to isolate the unique effects

of a single maltreatment type. Researchers have pointed out that failure to consider other types of

child maltreatment can lead to an overestimation of the effects of one type of maltreatment

(Cecil et al., 2017; Thibodeau et al., 2017). In the present study, controlling for CSA and CPA

victim status attenuated, but did not eliminate, the effects of PM. In general, when controlling for

CSA and CPA, the magnitude of the difference between the PM groups on indicators of

psychological functioning decreased. For most substance-related variables (i.e., problematic

alcohol use, heavy episodic drinking, and cannabis use frequency), the previously significant

differences between the PM groups in the unadjusted model ceased to exist. This finding is in

contrast to those of previous studies, which found that PM continued to predict indicators of

problematic alcohol use after controlling for other types of child maltreatment (Mandavia et al.,

2016; Potthast et al., 2014; Rosenkranz et al., 2012; Shin et al., 2015; White et al., 2016). Few

studies have examined the effect of PM on cannabis use and the available research is

inconsistent. White et al. (2016) found that PM (based on official and self-reports) was not a

significant predictor of cannabis use for adolescents after controlling for demographic

characteristics and other types of maltreatment. Also using a sample of adolescents and official

reports for measures of child maltreatment, Dubowitz et al. (2016) found that maltreated youth

were more likely to report cannabis use than non-maltreated youth; however, PM did not predict

cannabis use above and beyond the effects of demographic characteristics and other types of

maltreatment. On the other hand, Scheidell et al. (2017) did find a significant effect of PM on

cannabis use. Scheidell et al. (2017) examined the longitudinal relations between self-reported

childhood trauma and later drug use. Controlling for sociodemographic characteristics and a

range of childhood traumas, PM predicted past year cannabis use in emerging adulthood, but

CPA, CSA, and neglect did not.

For depression and nicotine use frequency in the present study, after controlling for CSA

and CPA, the low and moderate PM groups were no longer significantly different from each

other and only the difference between the low and high PM group remained statistically

significant. These results suggest the effect of PM was attenuated after controlling for CSA and

CPA, but PM continued to contribute significantly to the model. Similarly, Taillieu et al. (2016)

39

found that exposure to PM was associated with an increased odds of major depression,

dysthymia, panic disorder, and generalized anxiety disorder in a large sample of US adults, and

these relations were attenuated after controlling for other experiences of child maltreatment.

Other studies have also found that PM predicts depression over and above the effects of other

types of child maltreatment (e.g., Bifulco et al., 2002; Gibb et al., 2007; White et al., 2016).

Several researchers have examined the effect of PM on the combination of depression and

anxiety and found that PM is a significant predictor over and above the effects of other types of

child maltreatment (Cecil et al., 2017; Spertus et al., 2003). With regard to nicotine use, studies

examining the relation between PM and nicotine use are scarce. However, the available literature

indicates that PM predicts nicotine/tobacco use after controlling for other maltreatment (Elliott et

al., 2014; White et al., 2016). For example, in a large sample of adults with nicotine dependence,

PM, CSA, and CPA predicted the persistence of nicotine dependence approximately three years

later, controlling for demographics, other adverse experiences, and other types of child

maltreatment (Elliott et al., 2014).

Overall, the current results suggest that even when accounting for exposure to CSA and

CPA, more severe PM tends to be associated with worse psychological functioning in early

adulthood. While the inclusion of CSA and CPA in the models decreased the magnitude of the

differences between PM groups, CSA and CPA victim status emerged as significantly associated

with only a few indicators of psychological functioning. Being a victim of CSA was significantly

associated with higher levels of depression, anxiety, and nicotine use. On the other hand, being a

victim of CPA was only significantly associated with higher levels of depressive symptoms,

which may be due to the fact that PM and CPA are highly co-occurring and conceptually

overlapping. While research has demonstrated an association between CPA and CSA and adult

psychological functioning (Amado, Arce, & Herraiz, 2015; Dube et al., 2005; Elliott et al., 2014;

Lindert et al., 2014; Mandavia et al., 2016), studies that have accounted for PM have tended to

find the effects of CSA and/or CPA are weaker in comparison to those of PM (e.g., Cecil et al.,

2017; Gibb et al., 2007; Infurna et al., 2015; White et al., 2016).

It has been suggested that PM may be the context within which all child maltreatment

occurs (Edwards et al., 2003). Some researchers have conceptualized PM as an indicator of

family environment and the background upon which other types of child maltreatment are

experienced (Edwards et al., 2003). While controlling for other types of child maltreatment

40

allows researchers to statistically isolate unique effects of PM, it may not be ecologically valid as

it may not reflect people’s actual experiences. Characteristics common among maltreating

families include a chaotic and unstable family system, anger, and conflict (Cicchetti &

Valentino, 2006). Hodgdon et al. (2018) suggested that PM is especially likely to occur with

non-responsive caregivers in a chaotic and unpredictable home environment. In a study

examining the differential effects of different types of maltreatment with a large sample of clinic-

referred youth, Hodgdon and colleagues (2018) found that youth with PM had experienced more

co-occurring trauma types, such as exposure to domestic violence, impaired caregiving, and

neglect, than youth with CSA or CPA without PM. In the present study, every participant in the

high PM group was a victim of CPA and/or CSA and three-fourths of participants in this group

were victims of both CPA and CSA. This finding supports the theory that PM may be the

environmental context within which child maltreatment occurs. Other researchers posit that PM

is an underlying component of all types of child maltreatment, and it may even be the driving

force or common factor accounting for the negative effects of all types of maltreatment (Binggeli

et al., 2001; Hart et al., 1998; Hart et al., 2011). Further, types of child maltreatment experienced

during a single incident of abuse are not mutually exclusive. Price-Robertson, Higgins, and

Vassallo (2013) noted that “some individual acts of violence against children involve multiple

forms of maltreatment” (p. 85). For example, a parent who hits their child (CPA) may also curse

at the child (spurning) and threaten to become increasingly violent if unrealistic expectations are

not met (terrorizing). Given several different conceptualizations of how PM may be inherently

intertwined in all child maltreatment experiences, statistically controlling for other maltreatment

types may be overly conservative, leading to an underestimation of the effect of PM.

Results of the current study suggest that participants, rarely, if ever, experienced PM in

the absence of other types of child maltreatment. Given the substantial co-occurrence among

child maltreatment types, we cannot conclude that PM alone is driving group differences in

outcome. In fact, when controlling for other types of child maltreatment, PM seemed to have

little effect outside the context of these other types of maltreatment. The present findings are

consistent with previous research indicating that PM rarely occurs in isolation (Arata et al., 2007;

Arata et al., 2005; Dong et al., 2004; Ney et al., 1994). However, other researchers have

identified individuals exposed to PM with no other maltreatment histories (e.g., Lewis et al.,

2019; Moran et al., 2004; Spinazzola et al., 2014). In the present sample, a PM only group does

41

not seem to exist. For the high PM group in particular, the different types of child maltreatment

appear to be occurring together. Comparison of the current study to studies that found

individuals who had only experienced PM reveals important differences in the study designs. In

contrast to the present study, which used a young adult sample of women, continuous measures

of PM, and all five APSAC (1995) subtypes of PM, previous studies were focused on youth,

used dichotomous measures of the presence/absence of PM, and included only partial definitions

of PM (Lewis et al., 2019; Moran et al., 2004; Spinazzola et al., 2014).

Perhaps increased psychological distress is not due to a single type of child maltreatment,

but rather the co-occurrence or combination of different types of maltreatment. For example,

Lewis et al. (2019) found that individuals who had experienced PM (i.e., denying emotional

responsiveness and spurning) plus physical and/or sexual abuse were more likely to smoke

cigarettes. PM alone did not predict smoking, nor did physical and/or sexual abuse alone. It was

the specific combination of PM and physical and/or sexual abuse that predicted smoking.

Researchers have found evidence of an interactive effect of PM with other types of child

maltreatment such that the impact of exposure to maltreatment is worsened in the context of PM

(Berzenski & Yates, 2011; Edwards et al., 2003; McGee et al., 1997; Spinazzola et al., 2014). In

a large sample of adult members of a health maintenance organization, Edwards et al. (2003)

found that the presence of PM (i.e., spurning) heightened the effect of child maltreatment (CPA,

CSA, and witnessing of maternal battering) on depression and anxiety. Similarly, Spinazzola et

al. (2014) found that the co-occurrence of PM with CSA or CPA was associated with greater

internalizing, externalizing, and PTSD symptoms compared to CSA or CPA alone in a large

national sample of clinic-referred youth. Using the same sample of youth, Hodgdon et al. (2018)

found there was something particularly potent about the combination of PM and CPA. Youth

with exposure to PM and CPA had the youngest age of abuse onset, the greatest proportion of

lifetime abuse exposure, and the highest number of co-occurring traumas compared to youth with

PM, CPA, or CSA alone or any other combination of maltreatment types (i.e., CSA and CPA,

PM and CSA). Thus, it may not be possible to determine whether PM alone is responsible for

group differences in outcomes in the current study. Pursuing these findings may even be

counterproductive as isolating individual types of child maltreatment may cease to reflect

children’s actual experiences.

42

Strengths and Limitations

Strengths of the present study lie within the novel approach to identifying maltreatment

groups based on the severity of PM subtypes (i.e., spurning, terrorizing, isolating,

exploiting/corrupting, denying emotional responsiveness) within a racially/ethnically and

geographically diverse community sample of women. However, findings should be interpreted

with consideration of several limitations. The current study’s sample was entirely female, over

half students, and relatively homogeneous in terms of age, which limits the generalizability of

results. Future research should include male and female participants given research finding

gender differences in the effects of PM (Banducci et al., 2018; Berzenski & Yates, 2011; Harper

& Arias, 2004; Hyman et al., 2006; Paul & Eckenrode, 2015; Taussig & Culhane, 2010). While

the current sample had some racial and ethnic diversity, African American and Latina

participants were underrepresented relative to national statistics (U.S. Census Bureau, 2019). In

addition, the sample size precluded examination of the effect of race on variables of interest.

Given past research finding racial/ethnic differences in the effect of child maltreatment on adult

mental health outcomes (e.g., Lee & Chen, 2017; Schilling et al. 2007), future studies with

larger, diverse samples are needed.

The assessment of PM and other types of child maltreatment was a considerable strength

of the current study. The PM scale of the CAMI (DiLillo et al., 2010) was created to reflect all

five major domains of the APSAC (1995) model. The CAMI also utilizes behaviorally specific

descriptions, which increase the reliability of reports. In a review of the literature, Hardt and

Rutter (2004) found that retrospective reports of details of childhood maltreatment experiences

and reports that required judgment or interpretation tended to be less reliable than those with

those with clearly defined definitions of maltreatment. Nonetheless, retrospective self-report

measures of child maltreatment are subject to retrospective recall and social desirability biases

and tend to produce underestimates of maltreatment experienced (Widom, 1989; Widom &

Morris, 1997; Widom & Shepard, 1996).

The measures used to assess substance use in the present study presented some

limitations. Cannabis and nicotine use were assessed with a single item. While this is standard in

the literature (e.g., Banducci et al., 2018), future research would benefit from using a more

comprehensive assessment. In addition, the drug use frequency and drug use problems scores

were highly skewed, so the two variables were transformed into ordinal scales to maintain the

43

general distribution of scores and increase ease of interpretation. The rate of past year drug use in

this sample was also quite low. Future research should use a larger sample or oversample for

higher levels of drug use.

The lack of specific, detailed information about the nature of the participants’

experiences of PM was a limitation of the current study. The CAMI PM does not assess for the

relationship to the perpetrator(s), number of perpetrators, age(s) of PM initiation and exposure,

or duration of the maltreatment. Researchers have found associations between specific trauma

characteristics and outcomes. Access to data on the specific trauma characteristics would have

allowed for a more complex understanding of the PM experiences and might have provided a

richer picture of the relation between PM and other types of child maltreatment. For example, the

moderate and high PM groups might have different from each other by factors such as the

number of abusive caretakers. Davis et al. (2019) identified latent classes based on child

maltreatment experiences and trauma characteristics in a sample of adolescents entering

substance use treatment. Davis and colleagues (2019) found that youth with the highest

probability of exposure to PM, CPA, and CSA also endorsed the highest rates of theoretically-

harmful trauma characteristics, such as being abused by a family member or other trusted

individual, experiencing abuse over a long period of time, and experiencing negative reactions

upon abuse disclosure. Some researchers have suggested that one reason PM may be so powerful

is that it tends to be associated with an earlier age of onset and a longer duration of exposure than

other types of child maltreatment. Hodgdon et al. (2018) examined specific trauma

characteristics within maltreatment groups and found that youth who experienced PM alone or in

combination with other types of maltreatment (i.e., CSA and CPA) had an earlier age of abuse

onset and a higher lifetime proportion of exposure compared to youth without PM. Age of abuse

onset, which often signifies duration or chronicity of maltreatment, may be a better predictor of

symptoms than other trauma characteristics (Sundermann & DePrince, 2015). Other research has

suggested that the relation between child maltreatment and outcomes may be more complex than

previously thought. Research indicates there are sensitive developmental periods during which

exposure to child maltreatment is particularly damaging (Teicher & Samson, 2016). For

example, Cowell et al. (2015) found that children who were maltreated during infancy

demonstrated worse inhibitory control and working memory performance than children

maltreated later in childhood. Research on the neurobiological effects of maltreatment have

44

demonstrated the importance of maltreatment type and the developmental timing of exposure.

For instance, Pechtel, Lyons-Ruth, Anderson, and Teicher (2014) examined amygdala volume as

this “stress-susceptible brain region” (Teicher & Samson, 2016, p. 243) may be particularly

sensitive to the effects of child maltreatment. Using a sample of adults with a history of child

maltreatment and healthy controls, Pechtel and colleagues (2014) found that the severity of child

maltreatment exposure at 10-11 years of age was the strongest predictor of greater right

amygdala volume, which is believed to be associated with self-referential processing of negative

emotional stimuli. Thus, future research should assess for the specific characteristics of PM as

this would allow for a more complex understanding of PM experiences and the associated long-

term effects.

Implications

The present findings have important implications for future research and clinical work. The

conceptualization of PM and the five primary subtypes as defined by the APSAC (1995) model (i.e.,

spurning, terrorizing, exploiting/corrupting, isolating, denying emotional responsiveness) is strongly

grounded in theory and supported by research (Brassard & Donovan, 2006). The APSAC (1995) model

was further supported by results from the current study; however, participants were best classified

based on the severity of all PM experiences and there was no evidence for PM groups based on the

specific subtype(s) of PM. This finding raises questions about the conceptualization of PM as a

multidimensional construct and suggests the distinction among subtypes may not translate into people’s

actual experiences. It is possible that distinct PM subtypes exist but tend to co-occur at similar levels of

severity. The highly traumatized nature of the present sample is noteworthy and suggests the

participants’ experiences of maltreatment may differ from that of the general population. Perhaps the

participants’ experience of PM speaks to the context within which the maltreatment occurred. For

example, once maltreatment has reached a certain level of severity, the PM subtypes might not occur

outside the context of one another.

Examination of previous research providing evidence of differences in rates across PM subtypes

reveals the sample and measurement of PM subtypes differed from those in the current study. Past

research was largely based on samples of children (e.g., de la Vega et al., 2011; Taussig & Culhane,

2010; Trickett et al., 2009) and unlike the current study, did not utilize self-report measures of PM.

Instead, past studies used official case records (Taussig & Culhane, 2010; Trickett et al., 2009) or an

interview with the participant’s mother (de la Vega et al., 2011). The present study’s use of self-report

45

measures might have impacted results as it may be difficult for adults to retrospectively recall and

differentiate exposure to specific PM subtypes in childhood. While the CAMI PM was designed to be a

behaviorally specific measure, PM is much more difficult to quantify than other forms of maltreatment,

especially compared to those consisting of acts of commission that tend to cause immediate and visible

harm (Barnett et al., 2011; Wekerle, 2011). In addition, PM subtypes are not entirely mutually

exclusive as a single parenting behavior could fit the definition of more than one PM subtype (Perrin-

Miller & Perrin, 2007). For example, locking a child in a closet, which is a form of isolating, could be

considered terrorizing if it induces extreme fear and/or involves an element of threat and it could also

be considered a form of denying emotional responsiveness when it is done without expressed affection

and the child’s cries are ignored. Given evidence supporting the APSAC (1995) model of PM and

multiple explanations for the present findings, results should not be interpreted as evidence that PM is a

unidimensional construct without further research.

Results of the current study suggest a close connection between PM and other types of

child maltreatment. Participants who reported the most severe PM also reported high levels of

exposure to neglect, CSA, and CPA. On the other hand, participants who reported minimal

exposure to PM experienced relatively low levels of other types of child maltreatment. In an

attempt to better understand the relation between PM and other types of child maltreatment,

future research should explore whether it is possible to experience other types of maltreatment

without PM. For decades researchers have proposed that PM may be embedded within all other

types of child maltreatment (Binggeli et al., 2001; Hart et al., 1998; Hart et al., 2011). Therefore,

future research should attempt to determine precisely how PM relates to other types of

maltreatment.

The present findings also provide support for utilizing continuous measures of PM rather

than dichotomous indicators of PM exposure. If PM had been dichotomized in the present study,

participants in the moderate PM group would have been grouped with either the low or high PM

group, which would have obscured a more nuanced understanding of the impact of PM. In future

research on PM, it will be important to use consistent definitions and reliable measurements that

assess all PM subtypes according to the APSAC (1995) model. The sixth subtype of PM defined

by APSAC (1995), mental health, medical, and educational neglect, should be considered for

inclusion in future research as this subtype has received minimal research attention to date (e.g.,

Binggeli et al., 2001; Brassard & Donovan, 2006; Hart et al., 1998). In addition, future research

46

should determine whether witnessing intimate partner violence should be included as an aspect

of PM. While witnessing intimate partner violence is not explicitly included in the APSAC

(1995) definition, some researchers have conceptualized it as a subtype of PM (Kairys &

Johnson, 2002) and others have identified witnessing intimate partner violence as a form of

terrorizing and corrupting (Somer & Braunstein, 1999). In addition, some CPS definitions of

emotional maltreatment include witnessing intimate partner violence (Sedlak et al., 2010).

Results of the current study highlight the importance of assessing exposure to PM

regardless of exposure to other types of child maltreatment. Results suggest a positive

association between PM severity and decreased psychological functioning in early adulthood,

and this association remained for several outcomes even when controlling for other types of child

maltreatment. Thorough assessment and early identification of PM may provide the opportunity

for earlier intervention and perhaps the prevention of other types of child maltreatment. Previous

research has found that PM is associated with an earlier age of onset than other types of child

maltreatment (e.g., Hodgdon et al., 2018), suggesting PM may be a precursor and an early

indication of later exposure to other types of maltreatment. Clinicians working with children

experiencing PM may want to regularly assess and monitor for exposure to other types of

maltreatment. For participants in the current study, we do not know whether their exposure to

PM predated their exposure to other types of maltreatment. However, participants with the most

severe PM reported extremely high rates of CSA and CPA, suggesting individuals exposed to

severe PM may be more likely to experience other types of child maltreatment as well.

It is important that clinicians are aware of the breadth of PM experiences that can occur

as many aspects of PM do not fit into commonly known definitions of emotional or verbal abuse

and emotional neglect. The current results suggest that if a child or adolescent is experiencing

one subtype of PM, it is likely they are also experiencing other subtypes. Some researchers have

suggested that interventions for PM would be more effective if they targeted specific subtypes of

PM (Brassard & Hardy, 1997; Glaser, 2002; 2011). Results of the current study do not appear to

support this assertion as participants reported exposure to all PM subtypes at similar levels.

However, this finding does not eliminate the possibility that a particular PM subtype could result

in different outcomes for different people.

Finally, researchers have been calling for increased attention on PM by CPS agencies

(English et al., 2015) as CPS records grossly underestimate the rate of PM (Schneider et al.,

47

2005; Sedlak et al., 2010). Trickett et al. (2009) examined CPS records of 303 adolescents with

substantiated child maltreatment and found that almost half of the adolescents had experienced

PM, although less than 10% were identified as having experienced PM at the time they were

referred to CPS. Hodgdon and colleagues (2018) suggested that the adverse effects of PM are

made worse by disagreement and confusion about how to define, assess, and intervene in cases

of PM. CPS agencies across the country would be better equipped to identify cases of PM with

clearly defined criteria for PM exposure. Accurate and consistent identification of PM by CPS

agencies would improve if researchers defined a threshold level of severity at which poor

parenting becomes PM and children are placed at increased risk for negative outcomes.

Conclusion

Psychological maltreatment is associated with devasting, long-lasting effects (Berzenski

& Yates, 2011; Binggeli et al., 2001; Ney et al., 1994), yet it is the least studied form of child

maltreatment (Barnett et al., 2005; Finkelhor et al., 2005; Hart & Glaser, 2011). In the current

study, PM was associated with decreases in adult psychological functioning as evidenced by

differences between the low, moderate, and high PM severity groups on symptoms of depression

and anxiety, substance use, and substance-related problems. Furthermore, this association

remained for some outcomes even when controlling for other types of child maltreatment, which

were highly co-occurrent with participants’ experiences of PM. The current study also provided

further support for the APSAC (1995) model of PM with PM subtypes existing as distinct

constructs, even though the young women in this sample experienced all subtypes at similar

levels. Despite evidence that a range of parenting behaviors can be considered psychological

maltreatment, different subtypes of PM are rarely assessed (Paul & Eckenrode, 2015; White et

al., 2016). Researchers need to come to a consensus on the definition and conceptualization of

PM, which includes determining whether PM is a separate type of child maltreatment or an

inherent part of all other types of maltreatment. These perspectives are not mutually exclusive.

PM can both constitute a distinct type of maltreatment that can occur independent of other types

of maltreatment and reflect core contextual elements found in all types of child maltreatment. As

a core component of all types of child maltreatment, it is essential that we continue to develop

our understanding of all forms of PM.

48

References

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on

Automatic Control, 19, 716-723. doi:10.1109/TAC.1974.1100705

Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317-332. doi:10.1007/BF02294359

Allen, B. (2008). An analysis of the impact of diverse forms of childhood psychological

maltreatment on emotional adjustment in early adulthood. Child Maltreatment, 13, 307-

312. doi:10.1177/1077559508318394

Amado, B. G., Arce, R., & Herraiz, A. (2015). Psychological injury in victims of child sexual

abuse: A meta-analytic review. Psychosocial Intervention, 24, 49-62.

doi:10.1016/j.psi.2015.03.002

American Professional Society on the Abuse of Children (APSAC; 1995). Guidelines for

psychosocial evaluation of suspected psychological maltreatment in children and

adolescents. Chicago: Author.

Arata, C. M., Langhinrichsen-Rohling, J., Bowers, D., & O’Brien, N. (2007). Differential

correlates of multi-type maltreatment among urban youth. Child Abuse & Neglect, 31,

393-415. doi:10.1016/j.chiabu.2006.09.006

Arata, C. M., Langhinrichsen-Rohling, J., Bowers, D., & O’Farrill-Swails, L. (2005). Single

versus multi-type maltreatment: An examination of the long-term effects of child abuse.

Journal of Aggression, Maltreatment & Trauma, 11, 29-52. doi:10.1300/J146v11n04

Armour, C., Elklit, A., & Christoffersen, M. N. (2014). A latent class analysis of childhood

maltreatment: Identifying abuse typologies. Journal of Loss and Trauma, 19, 23-39.

doi:10.1080/15325024.2012.734205

Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (2001). AUDIT. The

Alcohol Use Disorders Identification Test: Guidelines for use in primary health care (2nd

ed.). Geneva, Switzerland: World Health Organization.

Baker, A. J. L. (2009). Adult recall of childhood psychological maltreatment: Definitional

strategies and challenges. Children and Youth Services Review, 31, 703-714.

doi:10.1016/j.childyouth.2009.03.001

Baker, A. J. L., & Ben-Ami, N. (2011). Adult recall of childhood psychological maltreatment in

“adult children of divorce”: Prevalence and associations with concurrent measures of well-

49

being. Journal of Divorce & Remarriage, 52, 203-219.

doi:10.1080/10502556.2011.556973

Baker, A. J. L., & Verrocchio, M. C. (2015). Parental bonding and parental alienation as

correlates of psychological maltreatment in adults in intact and non-intact families.

Journal of Child and Family Studies, 24, 3047-3057. doi:10.1007/s10826-014-0108-0

Banducci, A. N., Felton, J. W., Bonn-Miller, M. O., & Lejuez, C. W. (2018). An examination of

the impact of childhood emotional abuse and gender on cannabis use trajectories among

community youth. Translational Issues in Psychological Science, 4, 85-98.

doi:10.1037/tps0000142

Banducci, A. N., Hoffman, E. M., Lejuez, C. W., & Koenen, K. C. (2014). The impact of

childhood abuse on inpatient substance users: Specific links with risky sex, aggression,

and emotion dysregulation. Child Abuse & Neglect, 38, 928-938.

doi:10.1016/j.chiabu.2013.12.007

Barnett, O. W., Miller-Perrin, C. L., & Perrin, R. D. (2005). Family violence across the lifespan:

An introduction (2nd ed.). Thousand Oaks, CA: Sage.

Barnett, O. W., Miller-Perrin, C. L., & Perrin, R. D. (2011). Family violence across the lifespan:

An introduction (3rd ed.). Thousand Oaks, CA: Sage.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107,

238-246. doi:10.1037/0033-2909.107.2.238

Berlin, K. S., Williams, N. A., & Parra, G. R. (2014). An introduction to latent variable mixture

modeling (Part 1): Overview and cross-sectional latent class and latent profile

analyses. Journal of Pediatric Psychology, 39, 174-187. doi:10.1093/jpepsy/jst084

Bernstein, D. P., & Fink, L. (1998). Childhood Trauma Questionnaire: A retrospective self-

report manual. New York: The Psychological Corporation.

Bernstein, D. P., Stein, J. A., Newcomb, M. D., Walker, E., Pogge, D., Ahluvalia, T., Stokes, J.,

Handelsman, L., Medrano, M., Desmond, D., & Zule, W. (2003). Development and

validation of a brief screening version of the Childhood Trauma Questionnaire. Child

Abuse & Neglect, 27, 169-190. doi:10.1016/S0145-2134(02)00541-0

Berzenski, S. R., Madden, A. R., & Yates, T. M. (2019). Childhood emotional abuse

characteristics moderate associations with adult psychopathology and caregiving. Child

Abuse & Neglect, 87, 77-87. doi:10.1016/j.chiabu.2018.06.004

50

Berzenski, S. R., & Yates, T. M. (2011). Classes and consequences of multiple maltreatment: A

person-centered analysis. Child Maltreatment, 16, 250-261.

doi:10.1177/1077559511428353

Bifulco, A., Moran, P. M., Baines, R., Bunn, A., & Stanford, K. (2002). Exploring psychological

abuse in childhood: II. Association with other abuse and adult clinical depression.

Bulletin of the Menninger Clinic, 66, 241-258. doi:10.1521/bumc.66.3.241.23366

Binggeli, N. J., Hart, S. N., & Brassard, M. R. (2001). Psychological maltreatment of children:

The APSAC study guides 4. Thousand Oaks, CA: Sage.

Brassard, M. R., & Donovan, K. L. (2006). Defining psychological maltreatment. In M. M.

Feerick, J. F. Knutson, P. K. Trickett, & S. M. Flanzer (Eds.), Child abuse and neglect:

Definitions, classifications, & a framework for research (pp. 151-197). Baltimore, MD:

Paul H. Brookes.

Brassard, M. R., & Hardy, D. D. (1997). Psychological maltreatment. In M. Helfer, R. Kempe, &

R. Krugman (Eds.), The battered child (pp. 392-412). Chicago, IL: University of Chicago

Press.

Briere, J., & Runtz, M. (1988). Multivariate correlates of childhood psychological and physical

maltreatment among university women. Child Abuse & Neglect, 12, 331-341.

doi:10.1016/0145-2134(88)90046-4

Cecil, C. A. M., Viding, E., Barker, E. D., Guiney, J., & McCrory, E. J. (2014). Double

disadvantage: The influence of childhood maltreatment and community violence

exposure on adolescent mental health. Journal of Child Psychology and Psychiatry and

Allied Disciplines, 55, 839-848. doi:10.1111/jcpp.12213

Cecil, C. A. M., Viding, E., Fearon, P., Glaser, D., & McCrory, E. J. (2017). Disentangling the

mental health impact of childhood abuse and neglect. Child Abuse & Neglect, 63, 106-

119. doi:10.1016/j.chiabu.2016.11.024

Chapman, D. P., Whitfield, C. L., Felitti, V. J., Dube, S. R., Edwards, V. J., & Anda, R. F.

(2004). Adverse childhood experiences and the risk of depressive disorders in adulthood.

Journal of Affective Disorders, 82, 217-225. doi:10.1016/j.jad.2003.12.013

Child Abuse Prevention and Treatment Act. (1974). Pub. L. No. 93-247, 42. U.S.C. 5101, sec. 3.

51

Cicchetti, D., & Rogosch, F. A. (2001). The impact of child maltreatment and psychopathology

on neuroendocrine functioning. Development and Psychopathology, 13, 783-804.

doi:10.1017/S0954579401004035

Cicchetti, D., & Valentino, K. (2006). An ecological-transactional perspective on child

maltreatment: Failure of the average expectable environment and its influence on child

development. In D. Cicchetti & D. Cohen (Eds.), Handbook of developmental

psychopathology (2nd ed., Vol. 1, pp. 129-201). Hoboken, NJ: Wiley.

Claussen, A. H., & Crittenden, P. M. (1991). Physical and psychological maltreatment: Relations

among types of maltreatment. Child Abuse & Neglect, 15, 5–18. doi:10.1016/0145-

2134(91)90085-R

Clemmons, J. C., Walsh, K., DiLillo, D., & Messman-Moore, T. L. (2007). Unique and

combined contributions of multiple child abuse types and abuse severity to adult trauma

symptomatology. Child Maltreatment, 12, 172-181. doi:10.1177/1077559506298248

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ:

Lawrence Erlbaum Associates.

Cowell, R. A., Cicchetti, D., Rogosch, F. A., & Toth, S. L. (2015). Childhood maltreatment and

its effect on neurocognitive functioning: Timing and chronicity matter. Development and

Psychopathology, 27, 521-533. doi:10.1017/S0954579415000139

Crawford, J. R., & Henry, J. D. (2003). The Depression Anxiety Stress Scales (DASS):

Normative data and latent structure in a large non-clinical sample. British Journal of

Clinical Psychology, 42, 111-131. doi:10.1348/014466503321903544

Crawford, E., & Wright, M. O. (2007). The impact of childhood psychological maltreatment on

interpersonal schemas and subsequent experiences of relationship aggression. Journal of

Emotional Abuse, 7, 93-116. doi:10.1300/J135v07n02_06

Crittenden, P. M. (1992). Children’s strategies for coping with adverse home environments: An

interpretation using attachment theory. Child Abuse and Neglect, 16, 329-343.

doi:10.1016/0145-2134(92)90043-Q

Crittenden, P. M., Claussen, A. H., & Sugarman, D. B. (1994). Physical and psychological

maltreatment in middle childhood and adolescence. Development and Psychopathology,

6, 145-164. doi:10.1017/S0954579400005927

52

Crow, T., Cross, D., Powers, A., & Bradley, B. (2014). Emotion dysregulation as a mediator

between childhood emotional abuse and current depression in a low-income African

American sample. Child Abuse & Neglect, 38, 1590-1598.

doi:10.1016/j.chiabu.2014.05.015

Davis, J. P., Dworkin, E. R., Helton, J., Prindle, J., Patel, S., Dumas, T. M., & Miller, S. (2019).

Extending poly-victimization theory: Differential effects of adolescents’ experiences of

victimization on substance use disorder diagnoses upon treatment entry. Child Abuse &

Neglect, 89, 165-177. doi:10.1016/j.chiabu.2019.01.009

de la Vega, A., de la Osa, N., Ezpeleta, L., Granero, R., & Domènech, J. M. (2011). Differential

effects of psychological maltreatment on children of mothers exposed to intimate partner

violence. Child Abuse & Neglect, 35, 524-531. doi:10.1016/j.chiabu.2011.03.006

DiLillo, D. (2003). The Computer Assisted Maltreatment Inventory (CAMI). Unpublished

measure.

DiLillo, D., Hayes-Skelton, S. A., Fortier, M. A., Perry, A. R., Evans, S. E., Messman-Moore, T.

L., Walsh, K., Nash, C. & Fauchier, A. (2010). Development and initial psychometric

properties of the Computer Assisted Maltreatment Inventory (CAMI): A comprehensive

self-report measure of child maltreatment history. Child Abuse & Neglect, 34, 305-317.

doi:10.1016/j.chiabu.2009.09.015

DiLillo, D., Peugh, J., Walsh, K., Panuzio, J., Trask, E., & Evans, S. (2009). Child maltreatment

history among newlywed couples: A longitudinal study of marital outcomes and

mediating pathways. Journal of Consulting and Clinical Psychology, 77, 680-692.

doi:10.1037/a0015708

Dong, M., Anda, R. F., Felitti, V. J., Dube, S. R., Williamson, D. F., Thompson, T. J., Loo, C.

M., & Giles, W. H. (2004). The interrelatedness of multiple forms of childhood abuse,

neglect, and household dysfunction. Child Abuse & Neglect, 28, 771-784.

doi:10.1016/j.chiabu.2004.01.008

Dube, S. R., Anda, R. F., Felitti, V. J., Edwards, V. J., & Croft, J. B. (2002). Adverse childhood

experiences and personal alcohol abuse as an adult. Addictive Behaviors, 27, 713-725.

doi:10.1016/S0306-4603(01)00204-0

Dube, S. R., Anda, R. F., Whitfield, C. L., Brown, D. W., Felitti, V. J., Dong, M., & Giles, W. H.

(2005). Long-term consequences of childhood sexual abuse by gender of victim.

53

American Journal of Preventive Medicine, 28, 430-438.

doi:10.1016/j.amepre.2005.01.015

Dube, S. R., Felitti, V. J., Dong, M., Chapman, D. P., Giles, W. H., & Anda, R. F. (2003).

Childhood abuse, neglect, and household dysfunction and the risk of illicit drug use: The

Adverse Childhood Experiences Study. Pediatrics, 111, 564-572.

doi:10.1542/peds.111.3.564

Dubowitz, H., Thompson, R., Arria, A. M., English, D., Metzger, R., & Kotch, J. B. (2016).

Characteristics of child maltreatment and adolescent marijuana use: A prospective study.

Child Maltreatment, 21, 16-25. doi:10.1177/1077559515620853

Duke, N. N. (2018). Adolescent adversity and concurrent tobacco, alcohol, and marijuana use.

American Journal of Health Behavior, 42(5), 85-99. doi:10.5993/AJHB.42.5.8

Edwards, V. J., Holden, G. W., Felitti, V. J., & Anda, R. F. (2003). Relationship between

multiple forms of childhood maltreatment and adult mental health in community

respondents: Results from the Adverse Childhood Experiences Study. American Journal

of Psychiatry, 160, 1453-1460. doi:10.1176/appi.ajp.160.8.1453

Egeland, B., & Sroufe, L. A. (1981). Developmental sequelae of maltreatment in infancy. New

Directions for Child Development, 11, 77-92. doi:10.1002/cd.23219811106

Egeland, B., Sroufe, L. A., & Erickson, M. (1983). The developmental consequence of different

patterns of maltreatment. Child Abuse & Neglect, 7, 459-469. doi:10.1016/0145-

2134(83)90053-4

Elliott, J. C., Stohl, M., Wall, M. M., Keyes, K. M., Goodwin, R. D., Skodol, A. E., Krueger, R.

F., Grant, B. F., & Hasin, D. S. (2014). The risk for persistent adult alcohol and nicotine

dependence: The role of childhood maltreatment. Addiction, 109, 842-850.

doi:10.1111/add.12477

English, D., Thompson, R., White, C. R., & Wilson, D. (2015). Why should child welfare pay

more attention to emotional maltreatment? Children and Youth Services Review, 50, 53-

63. doi:10.1016/j.childyouth.2015.01.010

Finkelhor, D., Ormrod, R. K., Turner, H. A., & Hamby, S. L. (2005). The victimization of

children and youth: A comprehensive, national survey. Child Maltreatment, 10, 5-25.

doi:10.1177/1077559504271287

54

Finkelhor, D., Turner, H., Ormrod, R., & Hamby, S. L. (2009). Violence, abuse, and crime

exposure in a national sample of children and youth. Pediatrics, 124, 1411-1423.

doi:10.1542/peds.2009-0467

Games, P. A., & Howell, J. F. (1976). Pairwise multiple comparison procedures with unequal

N’s and/or variances: A Monte Carlo study. Journal of Educational Statistics, 1, 113-125.

doi:10.2307/1164979

Garbarino, J., & Vondra, J. (1987). Psychological maltreatment: Issues and perspectives. In M.

R. Brassard, R. Germain, & S. N. Hart (Eds.), Psychological maltreatment of children

and youth (pp. 24-44). New York, NY: Pergamon Press.

Gibb, B. E., Chelminski, I., & Zimmerman, M. (2007). Childhood emotional, physical, and

sexual abuse and diagnoses of depressive and anxiety disorders in adult psychiatric

outpatients. Depression and Anxiety, 24, 256-263. doi:10.1002/da.20238

Glaser, D. (2002). Emotional abuse and neglect (psychological maltreatment): A conceptual

framework. Child Abuse & Neglect, 26, 697-714. doi:10.1016/S0145-2134(02)00342-3

Glaser, D. (2011). How to deal with emotional abuse and neglect – Further development of a

conceptual framework (FRAMEA). Child Abuse & Neglect, 25, 866-875.

doi:10.1016/j.chiabu.2011.08.002

Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of

Psychology, 60, 549-576. doi:10.1146/annurev.psych.58.110405.085530

Greenfield, E. A., & Marks, N. F. (2010). Identifying experiences of physical and psychological

violence in childhood that jeopardize mental health in adulthood. Child Abuse & Neglect,

34, 161-171. doi:10.1016/j.chiabu.2009.08.012

doi:10.1016/j.comppsych.2008.04.005

Gross, A. B., & Keller, H. R. (1992). Long-term consequences of childhood physical and

psychological maltreatment. Aggressive Behavior, 18, 171-185. doi:10.1002/1098-

2337(1992)18:3<171::AID-AB2480180302>3.0.CO;2-I

Hamilton, J. L., Shapero, B. G., Stange, J. P., Hamlat, E. J., Abramson, L. Y., & Alloy, L. B.

(2013). Emotional maltreatment, peer victimization, and depressive versus anxiety

symptoms during adolescence: Hopelessness as a mediator. Journal of Clinical Child &

Adolescent Psychology, 42, 332-347. doi:10.1080/15374416.2013.777916

55

Harper, F. W. K., & Arias, I. (2004). The role of shame in predicting adult anger and depressive

symptoms among victims of child psychological maltreatment. Journal of Family

Violence, 19, 367-375. doi:10.1007/s10896-004-0681-x

Hart, S. N., Binggeli, N. J., & Brassard, M. R. (1998). Evidence for the effects of psychological

maltreatment. Journal of Emotional Abuse, 1, 27-58. doi:10.1300/J135v01n01_03

Hart, S. N., & Brassard, M. R. (1987). A major threat to children’s mental health: Psychological

maltreatment. American Psychologist, 42, 160-165. doi:10.1037/0003-066X.42.2.160

Hart, S. N., Brassard, M.R., Davidson H. A., Rivelis, E., Diaz, V., & Binggeli, N. J. (2011).

Psychological maltreatment. In J. E. B. Myers (Ed.), The APSAC handbook on child

maltreatment (3rd ed., pp. 125-144). Thousand Oaks, CA: Sage.

Hart, S. N., Brassard, M. R., Binggeli, N. J., & Davidson, H. A. (2002). Psychological

maltreatment. In J. E. B. Myers, L. Berliner, J. Briere, C. T. Hendrix, C. Jenny, & T. A.

Reid (Eds.), The APSAC handbook on child maltreatment (2nd ed., pp. 79-103).

Thousand Oaks, CA: Sage.

Hart, S. N., Germain, R. B., & Brassard, M. (1987). The challenge: To better understand and

combat psychological maltreatment of children and youth. In M. R. Brassard, R.

Germain, & S. N. Hart (Eds.), Psychological maltreatment of children and youth (pp. 3-

24). New York, NY: Pergamon.

Hart, S. N., & Glaser, D. (2011). Psychological maltreatment - Maltreatment of the mind: A

catalyst for advancing child protection toward proactive primary prevention and

promotion of personal well-being. Child Abuse & Neglect, 35, 758-766.

doi:10.1016/j.chiabu.2011.06.002

Hayre, R. S., Goulter, N., & Moretti, M. M. (2019). Maltreatment, attachment, and substance use

in adolescence: Direct and indirect pathways. Addictive Behaviors, 90, 196-203.

doi:10.1016/j.addbeh.2018.10.049

Hazen, A. L., Connelly, C. D., Roesch, S. C., Hough, R. L., & Landsverk, J. A. (2009). Child

maltreatment profiles and adjustment problems in high-risk adolescents. Journal of

Interpersonal Violence, 24, 361–378. doi:10.1177/0886260508316476

Henry, J. D., & Crawford, J. R. (2005). The short-form version of the Depression Anxiety Stress

Scales (DASS-21): Construct validity and normative data in a large non-clinical sample.

British Journal of Clinical Psychology, 44, 227-239. doi:10.1348/014466505X29657

56

Herrenkohl, R. C., & Herrenkohl, T. I. (2009). Assessing a child’s experience of multiple

maltreatment types: Some unfinished business. Journal of Family Violence, 24, 485-496.

doi:10.1007/s10896-009-9247-2

Hien, D.A., & First, M. (1991). Drug use questionnaire. Unpublished scale, Columbia College of

Physicians and Surgeons, New York State Psychiatric Institute, New York, New York.

Higgins, D. J. (2004). The importance of degree versus type of maltreatment: A cluster analysis

of child abuse types. Journal of Psychology, 138, 303-324. doi:10.3200/JRLP.138.4.303-

324

Higgins, D. J., & McCabe, M. P. (2001). The development of the Comprehensive Child

Maltreatment Scale. Journal of Family Studies, 7, 7-28. doi:10.5172/jfs.7.1.7

Hodgdon, H. B., Spinazzola, J., Briggs, E. C., Liang, L.-J., Steinberg, A. M., & Layne, C. M.

(2018). Maltreatment type, exposure characteristics, and mental health outcomes among

clinic referred trauma-exposed youth. Child Abuse & Neglect, 82, 12-22.

doi:10.1016/j.chiabu.2018.05.021

Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure

analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6,

1-55. doi:10.1080/10705519909540118

Hughes, K., Bellis, M. A., Hardcastle, K. A., Sethi, D., Butchart, A., Mikton, C., Jones, L., &

Dunne, M. P. (2017). The effect of multiple adverse childhood experiences on health: A

systematic review and meta-analysis. Lancet Public Health, 2, e356–e366.

doi:10.1016/S2468-2667(17)30118-4

Hyman, S. M., Garcia, M., & Sinha, R. (2006). Gender specific associations between types of

childhood maltreatment and the onset, escalation and severity of substance use in cocaine

dependent adults. The American Journal of Drugs and Alcohol Abuse, 32, 655-664.

doi:10.1080/10623320600919193

Infurna, M. R., Reichl, C., Parzer, P., Schimmenti, A., Bifulco, A., & Kaess, M. (2016).

Associations between depression and specific childhood experiences of abuse and

neglect: A meta-analysis. Journal of Affective Disorders, 190, 47-55.

doi:10.1016/j.jad.2015.09.006

57

Kairys, S. W., Johnson, C. F., & Committee on Child Abuse and Neglect. (2002). The

psychological maltreatment of children - Technical Report. Pediatrics, 109, e68-e72.

doi:10.1542/peds.109.4.e68

Kim, J., Mennen, F. E., & Trickett, P. K. (2016). Patterns and correlates of co-occurrence among

multiple types of child maltreatment. Child & Family Social Work, 22, 492-502.

doi:10.1111/cfs.12268

Lee, R. D., & Chen, J. (2017). Adverse childhood experiences, mental health, and excessive

alcohol use: Examination of race/ethnicity and sex differences. Child Abuse & Neglect,

69, 40-48. doi:10.1016/j.chiabu.2017.04.004

Lewis, T., Kotch, J., Proctor, L., Thompson, R., English, D., Smith, J., Zolotor, A., Block, S., &

Dubowitz, H. (2019). The role of emotional abuse in youth smoking. American Journal

of Preventive Medicine, 56, 93-99. doi:10.1016/j.amepre.2018.08.020

Lindert, J., von Ehrenstein, O. S., Grashow, R., Gal, G., Braehler, E., & Weisskopf, M. G.

(2014). Sexual and physical abuse in childhood is associated with depression and anxiety

over the life course: Systematic review and meta-analysis. International Journal of

Public Health, 59, 359-372. doi:10.1007/s00038-013-0519-5

Lin, X., Li, L., Chi, P., Wang, Z., Heath, M. A., Du, H., & Fang, X. (2016). Child maltreatment

and interpersonal relationship among Chinese children with oppositional defiant disorder.

Child Abuse & Neglect, 51, 192-202. doi:10.1016/j.chiabu.2015.10.013

Litrownik, A. J., Lau, A., English, D. J., Briggs, E., Newton, R. R., Romney, S., & Dubowitz, H.

(2005). Measuring the severity of child maltreatment. Child Abuse & Neglect, 29, 553-

573. doi:10.1016/j.chiabu.2003.08.010

Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing

values. Journal of the American Statistical Association, 83, 1198-1202.

doi:10.2307/2290157

Little, R. J., & Rubin, D. B. (1987). Statistical analysis with missing data (Vol. 4). New York:

Wiley.

Lo, Y, Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal

mixture. Biometrika, 88, 767-778. doi:10.1093/biomet/88.3.767

58

Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states:

Comparison of the depression anxiety stress scales (DASS) with the Beck Depression and

Anxiety Inventories. Behaviour Research and Therapy, 33, 335-343. doi:10.1016/0005-

7967(94)00075-U

Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety & Stress Scales.

(2nd ed.). Sydney: Psychological Foundation.

Mandavia, A., Robinson, G. G. N., Bradley, B., Ressler, K. J., & Powers, A. (2016). Exposure to

childhood abuse and later substance use: Indirect effects of emotion dysregulation and

exposure to trauma. Journal of Traumatic Stress, 29, 422-429. doi:10.1002/jts

Martin, C. G., Cromer, L. D., DePrince, A. P., & Freyd, J. J. (2013). The role of cumulative

trauma, betrayal, and appraisals in understanding trauma symptomatology. Psychological

Trauma, 52, 110-118. doi:10.1037/a0025686

Martins, C. M., Von Werne Baes, C., Tofoli, S. M., & Juruena, M. F. (2014). Emotional abuse in

childhood is a differential factor for the development of depression in adults. Journal of

Nervous and Mental Disorders, 202, 774-782. doi:10.1097/NMD.0000000000000202

McCrae, J. S., Chapman, M. V., & Christ, S. L. (2006). Profiles of children investigated for

sexual abuse: Association with psychopathology symptoms and services. American

Journal of Orthopsychiatry, 76, 468-481. doi:10.1037/0002-9432.76.4.468

McGee, R. A., & Wolfe, D. A. (1991). Psychological maltreatment: Toward an operational

definition. Development and Psychopathology, 3, 3-18.

doi:10.1017/S0954579400005034

McGee, R. A., Wolfe, D. A., & Wilson, S. K. (1997). Multiple maltreatment experiences and

adolescent behavior problems: Adolescents’ perspectives. Development and

Psychopathology, 9, 131-149. doi:10.1017/S0954579497001107

McHugh, M. L. (2013). The chi-square test of independence. Biochemia Medica, 23, 143-149.

doi:10.11613/bm.2013.018

McLachlan, G., & Peel, D. (2000). Finite mixture models. New York, NY: Wiley.

Mezquita, L., Ibáñez, M. I., Moya, J., Villa, H., & Ortet, G. (2014). A longitudinal examination

of different etiological pathways to alcohol use and misuse. Alcoholism: Clinical and

Experimental Research, 38, 1770-1779. doi:10.1111/acer.12419

59

Miller-Perrin, C. L., Perrin, R. D., & Kocur, J. L. (2009). Parental physical and psychological

aggression: Psychological symptoms in young adults. Child Abuse & Neglect, 33, 1-11.

doi:10.1016/j.chiabu.2008.12.001

Moran, P. B., Vuchinich, S., & Hall, N. K. (2004). Associations between types of maltreatment

and substance use during adolescence. Child Abuse & Neglect, 28, 565–574.

doi:10.1016/j.chiabu.2003.12.002

Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th ed.). Los Angeles, CA:

Muthén & Muthén.

Nash, C. L., Hayes-Skelton, S. A., & DiLillo, D. (2012). Reliability and factor structure of the

psychological maltreatment and neglect scales of the Computer Assisted Maltreatment

Inventory (CAMI). Journal of Aggression, Maltreatment & Trauma, 21, 583-607.

doi:10.1080/10926771.2012.680574

Navarre, E. L. (1987). Psychological maltreatment: The core component of child abuse. In M. R.

Brassard, R. Germaine, & S. N. Hart (Eds.), Psychological maltreatment of children and

youth (pp. 45-88). New York, NY: Pergamon Press.

Nelson, J., Klumparendt, A., Doebler, P., & Ehring, T. (2017). Childhood maltreatment and

characteristics of adult depression: Meta-analysis. British Journal of Psychiatry, 210, 96-

104. doi:10.1192/bjp.bp.115.180752

Newcomb, M. D., & Locke, T. F. (2001). Intergenerational cycle of maltreatment: A popular

concept obscured by methodological limitations. Child Abuse & Neglect, 25, 1219-1240.

doi:10.1016/S0145-2134(01)00267-8

Ney, P. G., Fung, T., & Wickett, A. R. (1994). The worst combinations of child abuse and

neglect. Child Abuse & Neglect, 18, 705-714. doi:10.1016/0145-2134(94)00037-9

Norman, R. E., Byambaa, M., De, R., Butchart, A., Scott, J., & Vos, T. (2012). The long-term

health consequences of child physical abuse, emotional abuse, and neglect: A systematic

review and meta-analysis. PLOS Medicine, 9. doi:10.1371/journal.pmed.1001349

Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in

latent class analysis and growth mixture modeling: A Monte Carlo simulation study.

Structural Equation Modeling, 14, 535-569. doi:10.1080/10705510701575396

O’Hagan, K. P. (1995). Emotional and psychological abuse: Problems of definition. Child Abuse

& Neglect, 19, 449-461. doi:10.1016/0145-2134(95)00006-T

60

Olson, C. L. (1974). Comparative robustness of six tests in multivariate analysis of variance.

Journal of the American Statistical Association, 69, 894-908.

doi:10.1080/01621459.1974.10480224

Paul, E., & Eckenrode, J. (2015). Childhood psychological maltreatment subtypes and adolescent

depressive symptoms. Child Abuse & Neglect, 47, 38-47. doi:10.1016/j.chiabu.2015.05.018

Pears, K. C., Kim, H. K., & Fisher, P. A. (2008). Psychosocial and cognitive functioning of

children with specific profiles of maltreatment. Child Abuse & Neglect, 32, 958-971.

doi:10.1016/j.chiabu.2007.12.009

Pechtel, P., Lyons-Ruth, K., Anderson, C. M., & Teicher, M. H. (2014). Sensitive periods of

amygdala development: The role of maltreatment in preadolescence. NeuroImage, 97,

236-244. doi:10.1016/j.neuroimage.2014.04.025

Perrin-Miller, C. L., & Perrin, R. D. (2007). Child maltreatment: An introduction (2nd ed.).

Thousand Oaks, CA: Sage.

Pillai, S., & Jayachandran, K. (1967). Power comparisons of tests of two multivariate hypotheses

based on four criteria. Biometrika, 54, 195-210. doi:10.2307/2333863

Post, R. M., Altshuler, L. L., Kupka, R., Mcelroy, S. L., Frye, M. A., Rowe, M., Leverich, G. S.,

Grunze, H., Suppes, T., Keck, P. E. Jr., & Nolen, W. A. (2015). Verbal abuse, like

physical and sexual abuse, in childhood is associated with an earlier onset and more

difficult course of bipolar disorder. Bipolar Disorders, 17, 323-330.

doi:10.1111/bdi.12268

Potthast, N., Neuner, F., & Catani, C. (2014). The contribution of emotional maltreatment to

alcohol dependence in a treatment-seeking sample. Addictive Behaviors, 39, 949-958.

doi:10.1016/j.addbeh.2014.01.015

Price-Robertson, R., Higgins, D., & Vassallo, S. (2013). Multi-type maltreatment and

polyvictimization: A comparison of two research frameworks. Family Matters, 93, 84-98.

Retrieved from https://aifs.gov.au/publications/family-matters

Ramaswamy, V., DeSarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An empirical

pooling approach for estimating marketing mix elasticities with PIMS data. Marketing

Science, 12, 103-124. doi: 10.1287/mksc.12.1.103

61

Rice, C., Mohr, C. D., Del Boca, F. K., Mattson, M. E., Young, L., Brady, K., & Nickless, C.

(2001). Self-reports of physical, sexual and emotional abuse in an alcoholism treatment

sample. Journal of Studies on Alcohol, 62, 114-123. doi:10.15288/jsa.2001.62.114

Rohner, R. P., & Rohner, E. C. (1980). Antecedents and consequences of parental rejection: A

theory of emotional abuse. Child Abuse & Neglect, 4, 189-198. doi:10.1016/0145-

2134(80)90007-1

Rosenkranz, S. E., Muller, R. T., & Henderson, J. L. (2012). Psychological maltreatment in

relation to substance use problem severity among youth. Child Abuse & Neglect, 36, 438-

448. doi:10.1016/j.chiabu.2012.01.005

Sanders, B., & Becker-Lausen, E. (1995). The measurement of psychological maltreatment:

Early data on the Child Abuse and Trauma Scale. Child Abuse & Neglect, 19, 315-323.

doi:10.1016/S0145-2134(94)00131-6

Saunders, J. B., Aasland, O. G., Babor, T. F., de la Fuente, J. R., & Grant, M. (1993).

Development of the alcohol use disorders identification test (AUDIT): WHO

collaborative project on early detection of persons with harmful alcohol consumption: II.

Addiction, 88, 791-804. doi:10.1111/j .13600443.1993.tb02093.x

Scheidell, J. D., Quinn, K., McGorray, S. P., Frueh, B. C., Beharie, N. N., Cottler, L. B., &

Khan, M. R. (2017). Childhood traumatic experiences and the association with marijuana

and cocaine use in adolescence through adulthood. Addiction, 113, 44-56.

doi:10.1111/add.13921

Schilling, E. A., Aseltine, R. H., Jr., & Gore, S. (2007). Adverse childhood experiences and

mental health in young adults: A longitudinal survey. BMC Public Health, 7, 7-30.

doi:10.1186/1471-2458-7-30

Schneider, M. W., Ross, A., Graham, J. C., & Zielinski, A. (2005). Do allegations of emotional

maltreatment predict developmental outcomes beyond that of other forms of

maltreatment? Child Abuse & Neglect, 29, 513-532. doi:10.1016/j.chiabu.2004.08.010

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.

doi:10.1214/aos/1176344136

Schwandt, M. L., Heilig, M., Hommer, D. W., George, D. T., & Ramchandani, V. A. (2013).

Childhood trauma exposure and alcohol dependence severity in adulthood: Mediation by

62

emotional abuse severity and neuroticism. Alcoholism: Clinical and Experimental

Research, 37, 984-992. doi:10.1111/acer.12053

Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate

analysis. Psychometrika, 52, 333-343. doi:10.1007/BF02294360

Scott-Storey, K. (2011). Cumulative abuse: Do things add up? An evaluation of the

conceptualization, operationalization, and methodological approaches in the study of the

phenomenon of cumulative abuse. Trauma, Violence, & Abuse, 12, 135-150.

doi:10.1177/1524838011404253

Sedlak, A. J., Mettenburg, J., Basena, M., Petta, I., McPherson, K., Greene, A., & Li, S. (2010).

Fourth national incidence study of child abuse and neglect (NIS-4). Washington, DC:

U.S. Department of Health and Human Services, Administration for Children and

Families.

Shaffer, A., Yates, T. M., & Egeland, B. R. (2009). The relation of emotional maltreatment to

early adolescent competence: Developmental processes in a prospective study. Child

Abuse & Neglect, 33, 36-44. doi:10.1016/j.chiabu.2008.12.005

Shin, S. H., Lee, S., Jeon, S.-M., & Wills, T. A. (2015). Childhood emotional abuse, negative

emotion-driven impulsivity, and alcohol use in young adulthood. Child Abuse &

Neglect, 50, 94-103. doi:10.1016/j.chiabu.2015.02.010

Shin, S. H., McDonald, S. E., & Conley, D. (2018). Patterns of adverse childhood experiences

and substance use among young adults: A latent class analysis. Addictive Behaviors, 78,

187-192. doi:10.1016/j.addbeh.2017.11.020

Shingala, M. C., & Rajyaguru, A. (2015). Comparison of post hoc tests for unequal variance.

International Journal of New Technologies in Science and Engineering, 2(5), 22-23.

https://www.ijntse.com/

Sinclair, S. J., Siefert, C. J., Slavin-Mulford, J. M., Stein, M. B., Renna, M., & Blais, M. A.

(2012). Psychometric evaluation and normative data for the Depression, Anxiety, and

Stress Scales-21 (DASS-21) in a nonclinical sample of U.S. adults. Evaluation & the

Health Professions, 35, 259-279. doi:10.1177/0163278711424282

Somer, E., & Braunstein, A. (1999). Are children exposed to interparental violence being

psychologically maltreated? Aggression and Violent Behavior, 4, 449-456.

doi:10.1016/S1359-1789(98)00023-8

63

Spertus, I. L., Yehuda, R., Wong, C. M., Halligan, S., & Seremetis, S. V. (2003). Childhood

emotional abuse and neglect as predictors of psychological and physical symptoms in

women presenting to a primary care practice. Child Abuse & Neglect, 27, 1247-1258.

doi:10.1016/j.chiabu.2003.05.001

Spinazzola, J., Hodgdon, H., Liang, L. J., Ford, J. D., Layne, C. M., Pynoos, R., Briggs, E. C.,

Stolbach, B., & Kisiel, C. (2014). Unseen wounds: The contribution of psychological

maltreatment to child and adolescent mental health and risk outcomes. Psychological

Trauma: Theory, Research, Practice, and Policy, 6(Suppl 1), S18-S28.

doi:10.1037/a0037766

Steiger, J. H., & Lind, J. C. (1980, June). Statistically based tests for the number of common

factors. Paper presented at the Psychometric Society annual meeting, Iowa City, IA.

Straus, M. A., & Field, C. J. (2003). Psychological aggression by American parents: National

data on prevalence, chronicity, and severity. Journal of Marriage and Family, 65, 795-

808. doi:10.1111/j.1741-3737.2003.00795.x

Stuewig, J., & McCloskey, L. A. (2005). The relation of child maltreatment to shame and guilt

among adolescents: Psychological routes to depression and delinquency. Child

Maltreatment, 10, 324-336. doi:10.1177/1077559505279308

Sundermann, J. M., & DePrince, A. P. (2015). Maltreatment characteristics and emotion

regulation (ER) difficulties as predictors of mental health symptoms : Results from a

community-recruited sample of female adolescents. Journal of Family Violence, 30, 329-

338. doi:10.1007/s10896-014-9656-8

Taha, F., Galea, S., Hien, D., & Goodwin, R. D. (2014). Childhood maltreatment and the

persistence of smoking: A longitudinal study among adults in the US. Child Abuse &

Neglect, 38, 1995-2006. doi:10.1016/j.chiabu.2014.10.022

Taillieu, T. L., Brownridge, D. A., Sareen, J., & Afifi, T. O. (2016). Childhood emotional

maltreatment and mental disorders: Results from a nationally representative adult sample

from the United States. Child Abuse & Neglect, 59, 1-12.

doi:10.1016/j.chiabu.2016.07.005

Taussig, H. N., & Culhane, S. E. (2010). Emotional maltreatment and psychosocial functioning

in preadolescent youth placed in out-of-home care. Journal of Aggression, Maltreatment

& Trauma, 19, 52-74. doi:10.1080/10926770903476008

64

Teicher, M. H., & Samson, J. A. (2016). Annual research review: Enduring neurobiological

effects of childhood abuse and neglect. Journal of Child Psychology and Psychiatry, 57,

241-266. doi:10.1111/jcpp.12507

Teicher, M. H., Tomoda, A., & Andersen, S. L. (2006). Neurobiological consequences of early

stress and childhood maltreatment: Are results from human and animal studies

comparable? Annals of the New York Academy of Sciences, 1071, 313-323.

doi:10.1196/annals.1364.024

Thibodeau, M.-E., Lavoie, F., Hébert, M., & Blais, M. (2017). Childhood maltreatment and

adolescent sexual risk behaviors: Unique, cumulative and interactive effects. Child Abuse

& Neglect, 72, 411-420. doi:10.1016/j.chiabu.2017.09.002

Trickett, P. K., Mennen, F. E., Kim, K., & Sang, J. (2009). Emotional abuse in a sample of

multiply maltreated, urban young adolescents: Issues of definition and identification.

Child Abuse & Neglect, 33, 27-35. doi:10.1016/j.chiabu.2008.12.003

Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor

analysis. Psychometrika, 38, 1-10. doi:10.1007/BF02291170

U.S. Department of Health and Human Services, Administration for Children and Families,

Administration on Children, Youth, and Families, Children’s Bureau. (2019). Child

maltreatment 2017. http://www.acf.hhs.gov/cb/research-data-technology/statistics-

research/child-maltreatment

U.S. Census Bureau. (2019). QuickFacts: United States. http://www.census.gov/quickfacts/fact/

table/us/PST045219#

Vilhena-Churchill, N., & Goldstein, A. L. (2014). Maltreatment and marijuana problems in

young adults: Examining the role of motives and emotion dysregulation. Child Abuse &

Neglect, 38, 962-972. doi:10.1016/j.chiabu.2013.10.009

Wardenaar, K. J., Wanders, R. B. K., Jeronimus, B. F., & de Jonge, P. (2018). The psychometric

properties of an internet-administered version of the Depression Anxiety and Stress

Scales (DASS) in a sample of Dutch adults. Journal of Psychopathology and Behavioral

Assessment, 40, 318-333. doi:10.1007/s10862-017-9626-6

Watkins, L. E., Maldonado, R. C., & DiLillo, D. (2014). Hazardous alcohol use and intimate

partner aggression among dating couples: The role of impulse control

difficulties. Aggressive Behavior, 40, 369-381. doi:10.1002/ab.21528

65

Wekerle, C. (2011). Emotionally maltreated: The under-current of impairment? Child Abuse &

Neglect, 35, 899-903. doi:10.1016/j.chiabu.2011.09.002

White, C. R., English, D., Thompson, R., & Roberts, Y. H. (2016). Youth self-report of

emotional maltreatment: Concordance with official reports and relation to outcomes.

Children and Youth Services Review, 62, 111-121. doi:10.1016/j.childyouth.2016.02.004

Widaman, K. F. (2006). Missing data: What to do with or without them. Monographs of the

Society for Research in Child Development, 71(3), 42-64. doi:10.1111/j.1540-

5834.2006.00404.x

Widom, C. S. (1989). Does violence beget violence? A critical examination of the literature.

Psychology Bulletin, 106, 3-28. doi:10.1037/0033-2909.106.1.3

Widom, C. S., & Morris, S. (1997). Accuracy of adult recollections of childhood victimization:

Part 2. Childhood sexual abuse. Psychological Assessment, 9, 34-46. doi:10.1037/1040-

3590.9.1.34

Widom, C. S., & Shepard, R. L. (1996). Accuracy of adult recollections of childhood

victimization: Part 1. Childhood physical abuse. Psychological Assessment, 8, 412-421.

doi:10.1037/1040-3590.8.4.412

Wright, M. O., Crawford, E., & Del Castillo, D. (2009). Childhood emotional maltreatment and

later psychological distress among college students: The mediating role of maladaptive

schemas. Child Abuse & Neglect, 33, 59-68. doi:10.1016/j.chiabu.2008.12.007

Yu, C. Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with

binary and continuous outcomes. (Unpublished doctoral dissertation). University of

California Los Angeles, Los Angeles, CA. doi:10.1.1.310.3956

66

Table 1

Standardized Factor Loadings for the CFA for the Final Model of the CAMI PM

Item Factor Loading

Terrorizing

2. My parents put me in situations that frightened me. .74

6. My parents threatened to leave me somewhere so that I could never come home. .80

14. My parents sometimes got angry and destroyed things that were mine. .81

19. My parents threatened to leave me and never come back. .84

23. My parents threatened to hit or physically hurt me when I was a child. .81

Spurning

4. My parents often made me cry for no good reason. .83

20. My parents purposely embarrassed me in front of my friends. .72

22. I was cursed or sworn at as a child by my parents. .79

Exploiting/Corrupting

3. My parents didn’t really care when I did things that were wrong. .69

7. I used illegal drugs with my parents before I was 18 years old. .83

11. I saw my parents do illegal things like use drugs or steal. .89

21. My parents encouraged me to do things that some might consider illegal or

immoral.

.91

Isolating

10. My parents often sent me to bed without dinner. .87

16. My parents punished me by confining me to a closet or other small places. .79

Denying Emotional Responsiveness

8. My parents often asked me about my day. .83

12. My parents liked spending time with me. .89

15. My childhood achievements were acknowledged by my parents. .84

17. My parents paid attention to me when I talked to them. .90

18. My parents showed a lot of interest in me as a child. .92

24. As a child I felt loved by my parents. .90

2 = 481.05***

RMSEA = .06 (.06, .07)

CFI = .98

TLI = .98

Note. CFA = confirmatory factor analysis; CAMI PM = Computer Assisted Maltreatment Inventory

Psychological Maltreatment scale. N = 490. Factors are in bold. Deleted items include items 1

(“Being second best was never good enough for my parents”), 5 (“My parents were very

controlling”), 9 (“I felt like my parents used me to meet their own emotional needs”), and 13

(“When I was in school, only A’s were good enough for my parents”).

*p < .05. **p < .01. ***p < .001.

67

Table 2

Fit Indices for Latent Profile Models 1 – 4

Model AIC BIC ABIC LMR (p) VLMR (p) BLRT (p) Entropy

1 6407.06 6449.01 6417.27 --- --- --- ---

2 5403.35 5470.46 5417.68 989.10 (.003) 1015.71 (.002) 1015.71 (<.001) 0.917

3 5083.23 5175.50 5105.68 323.42 (<.001) 332.13 (<.001) 332.13 (<.001) 0.919

4 4967.22 5084.66 4995.79 124.657 (.53) 128.01 (.53) 128.01 (<.001) 0.931

Note. AIC = Akaike information criteria; BIC = Bayesian information criteria; ABIC = Sample-size

adjusted BIC; LMR = Lo-Mendell-Rubin adjusted likelihood ratio test; VLMR = Vuong-Lo-Mendell-

Rubin likelihood ratio test; BLRT = Bootstrap likelihood ratio test.

68

Table 3

Characteristics of Psychological Maltreatment (PM) Subtypes for the PM Groups

Psychological maltreatment group

Full sample

(N = 490)

Low

(n = 304)

Moderate

(n = 133)

High

(n = 53)

PM subtypes M (SD) M (SD) M (SD) M (SD) F (2, 487) η2partial

Terrorizing 1.81 (.92) 1.27 (.36)a 2.29 (.51)b 3.72 (.67)c 736.45** .76

Spurning 2.12 (1.12) 1.41 (.47)a 2.95 (.68)b 4.17 (.66)c 711.37** .75

Exploiting/corrupting 1.47 (.77) 1.14 (.33)a 1.83 (.86)b 2.38 (1.14)c 105.90** .31

Isolating 1.38 (.71) 1.09 (.27)a 1.56 (.68)b 2.58 (1.02)c 182.79** .43

Denying ER 2.00 (1.00) 1.54 (.62)a 2.36 (.85)b 3.66 (.95)c 202.03** .46

Note. All p-values are Bonferroni adjusted. Denying ER = denying emotional responsiveness.

aWithin each row, means with different subscripts are significantly different at p < .01.

*p < .05. **p < .01.

69

Table 4

Demographic Characteristics of Psychological Maltreatment Groups

Psychological maltreatment group

Full

sample

(N = 490)

Low

(n = 304)

Moderate

(n = 133)

High

(n = 53)

M (SD) M (SD) M (SD) M (SD) F (df) ηp2

Age 21.73

(2.23)

21.66

(2.26)

21.84

(2.17)

21.83

(2.18)

.37 (2,

487) .00

n (%) n (%) n (%) n (%) 2

Race/ethnicity

White 301 (61.4) 178 (58.6) 89 (66.9) 34 (64.2) 2.92

African American 171 (34.9) 113 (37.2) 45 (33.8) 13 (24.5) 3.27

Latina 28 (5.7) 14 (4.6) 8 (6.0) 6 (11.3) −

Native American 15 (3.1) 8 (2.6) 3 (2.3) 4 (7.5) −

Asian 21 (4.3) 12 (3.9) 6 (4.5) 3 (5.7) −

Other 13 (2.7) 6 (2.0) 5 (3.8) 2 (3.8) −

Student 303 (61.8) 198 (65.1) 81 (60.9) 24 (45.3) 6.87

Children 98 (20.0) 50 (16.4) 33 (24.8) 15 (28.3) 6.86

Note. All p-values are Bonferroni adjusted. There were no significant differences. For cells n < 5,

Fisher’s Exact Test was used in place of the Chi-square. Other = other race/ethnicity; Student =

participant was a student; Children = participant had children. Demographic characteristics, with the

exception of age, were coded 0 = no, 1 = yes. Percentages for race/ethnicity add up to over 100%

because participants could select more than one race/ethnicity.

70

Table 5

Co-occurrence of Other Types of Child Maltreatment

Psychological maltreatment group

Full sample

(N = 490)

Low

(n = 291)

Moderate

(n = 131)

High

(n = 49)

M (SD) M (SD) M (SD) M (SD) F (2, 460) η2partial

Neglect severity 1.57 (.62) 1.30 (.35)a 1.78 (.59)b 2.48 (.72)c 148.96** .39

CPA severity 9.07 (6.20) 6.98 (6.15)a 11.52 (5.00)b 14.80 (1.49)c 59.93** .20

CSA severity 5.10 (6.19) 3.36 (5.30)a 6.94 (6.27)b 10.59 (6.43)c 43.01** .16

Note. All p-values are Bonferroni adjusted. CPA = child physical abuse; CSA = child sexual abuse.

aWithin each row, means with different subscripts are significantly different at p < .05.

*p < .05. **p < .01.

71

Table 6

Binomial Logistic Regression Predicting Likelihood of Sexual and Physical Abuse Victim Status with

the Psychological Maltreatment (PM) Groups

Independent

variable

PM group Dependent

variable B SE Wald 2 p OR 95% CI

Low vs. moderate CPA victim 1.60 .23 47.84** .000 4.97 [3.15, 7.82]

Low vs. high 1.89 .37 26.66** .000 6.59 [3.22, 13.49]

Moderate vs. high 2.17 .75 8.42* .004 8.76 [2.02, 37.92]

Low vs. moderate CSA victim 1.08 .21 25.24** .000 2.94 [1.93, 4.47]

Low vs. high 1.03 .18 32.96** .000 2.81 [1.98, 4.00]

Moderate vs. high .99 .38 6.73* .009 2.69 [1.27, 5.69]

Note. All p-values are Bonferroni adjusted. CPA = child physical abuse; CSA = child sexual abuse.

*p < .05. **p < .01.

72

Table 7

Intercorrelations, Means, and Standard Deviations for Outcome Variables for the Full Sample

Depression Anxiety

Problematic

alcohol use

Heavy episodic

drinking

Cannabis use

frequency

Nicotine Use

Frequency

Depression

(DASS-21)

−

Anxiety

(DASS-21)

.67** −

Problematic alcohol use

(AUDIT)

.30** .26** −

Heavy episodic drinking

(HED)

.25** .22** .84** −

Cannabis use frequency

(DUQ)

.22** .26** .42** .41** −

Nicotine use frequency

(DUQ)

.20** .26** .38** .31** .50** −

M 3.75 3.34 4.85 .66 1.04 1.20

SD 4.50 3.81 5.13 .69 1.64 1.93

Note. DASS-21= Depression, Anxiety, and Stress Scale; AUDIT = Alcohol Use Disorders Identification

Test; HED = Heavy Episodic Drinking; DUQ = Drug Use Questionnaire.

*p < .05. **p < .01.

73

Table 8

Bivariate Correlations for Outcomes Variables and Demographic Characteristics for the Full Sample

Age White

African

American Latina

Native

American Asian Other Student Children

Depression

(DASS-21) .02 .07 -.10* .11* .00 .01 -.01 -.09* .08

Anxiety

(DASS-21) -.05 .01 -.05 .14** .02 .03 .03 -.04 .12**

Problematic alcohol use

(AUDIT) .00

.26*

* -.25** .04 -.03 -.01 -.10* -.11* -.13**

Heavy episodic drinking

(HED) .00

.24*

* -.23** .04 -.09* .00 -.09* -.10* -.12**

Cannabis use frequency

(DUQ) -.05 .09 -.06 .01 .07 -.05 -.07 -.12** -.04

Nicotine use frequency

(DUQ) .06

.25*

* -.24** -.03 .01 .02 -.07 -.24** .09*

Note. DASS-21= Depression, Anxiety, and Stress Scale; AUDIT = Alcohol Use Disorders

Identification Test; HED = Heavy Episodic Drinking; DUQ = Drug Use Questionnaire. Other = other

race/ethnicity; Student = participant was a student; Children = participant had children. Demographic

characteristics, with the exception of age, were coded 0 = no, 1 = yes.

*p < .05. **p < .01

74

Table 9

Depression and Anxiety Between the Psychological Maltreatment Groups

Psychological maltreatment groups

Full sample

(N = 490)

Low

(n = 304)

Moderate

(n = 133)

High

(n = 53)

M (SD) M (SD) M (SD) M (SD) F (df) ηp2

Depression 3.75 (4.50) 2.88 (3.96)a 4.66 (4.31)b 6.47 (6.12)b 19.55 (2, 487)** .07

Anxiety 3.34 (3.81) 2.41 (3.20)a 4.45 (4.00)b 5.91 (4.62)b 29.98 (2, 487)** .11

Note. All p-values are Bonferroni adjusted. Depression and anxiety were assessed with the Depression,

Anxiety, and Stress Scale (DASS-21).

aWithin each row, means with different subscripts are significantly different at p < .01.

*p < .05. **p < .01.

75

Table 10

Crosstabulation of Psychological Maltreatment Groups and Depression Severity Category

Psychological maltreatment group

Depression severity

(DASS-21 score multiplied by 2) Low

n = 304

Moderate

n = 133

High

n = 53

Full sample

(N = 490)

Normal

(0 – 9)

77.3%

n = 235

(5.7)

54.1%

n = 72

(-4.0)

49.1%

n = 26

(-3.1)

68.0%

n = 333

Mild – moderate

(10 – 20)

16.1%

n = 49

(-3.8)

34.6%

n = 46

(4.3)

20.8%

n = 11

(-.2)

21.6%

n = 106

Severe – extremely severe

(11 – 42)

6.6%

n = 20

(-3.5)

11.3%

n = 15

(.4)

30.2%

n = 16

(5.0)

10.4%

n = 51

Note. Adjusted residuals appear in parentheses below observed frequencies. Adjusted residuals

exceeding ±1.96 are significant at p < .05. Depression was assessed with the Depression, Anxiety, and

Stress Scale (DASS-21).

76

Table 11

Crosstabulation of Psychological Maltreatment Groups and Anxiety Severity Category

Psychological maltreatment group

Anxiety severity

(DASS-21 score multiplied by 2) Low

n = 304

Moderate

n = 133

High

n = 53

Full sample

(N = 490)

Normal

(0 – 7)

75.0%

n = 228

(6.0)

52.6%

n = 70

(-3.5)

37.7%

n = 20

(-4.4)

64.9%

n = 318

Mild – moderate

(8 – 14)

17.1%

n = 52

(-2.7)

26.3%

n = 35

(1.8)

30.2%

n = 16

(1.7)

21.0%

n = 103

Severe – extremely severe

(15 – 42)

7.9%

n = 24

(-5.0)

21.1%

n = 28

(2.7)

32.1%

n = 17

(4.0)

14.1%

n = 69

Note. Adjusted residuals appear in parentheses below observed frequencies. Adjusted residuals

exceeding ±1.96 are significant at p < .05. Anxiety was assessed with the Depression, Anxiety, and

Stress Scale (DASS-21).

77

Table 12

Alcohol Use Between the Psychological Maltreatment Groups

Psychological maltreatment group

Full sample

(N = 490)

Low

(n = 304)

Moderate

(n = 133)

High

(n = 53)

M (SD) M (SD) M (SD) M (SD) F (df) ηp2

AUDIT 4.85 (5.13) 4.15 (4.39)a 5.75 (5.57)b 6.61 (6.96)b 8.21 (2, 487)** .03

HED .66 (.69) .59 (.62)a .73 (.70)ab .92 (.94)b 6.23 (2, 487)* .03

Note. AUDIT = Alcohol Use Disorder Identification Test; HED = Heavy Episodic Drinking. All p-

values are Bonferroni adjusted.

aWithin each row, means with different subscripts are significantly different at p < .05.

*p < .05. **p < .01.

78

Table 13

Crosstabulation of Psychological Maltreatment Group and Problematic Alcohol Use

Psychological maltreatment group

Low

n = 302

Moderate

n = 131

High

n = 53

Full sample

(N = 486)

Non-problematic alcohol use

(AUDIT scores < 8)

82.5%

n = 249

(2.9)

72.5%

n = 95

(-1.8)

67.9%

n = 36

(-1.9)

78.2%

n = 380

Problematic alcohol use

(AUDIT scores 8)

17.5%

n = 53

(-2.9)

27.5%

n = 36

(1.8)

32.1%

n = 17

(1.9)

21.8%

n = 106

Note. Adjusted residuals appear in parentheses below observed frequencies. AUDIT = Alcohol Use

Disorders Identification Test. Adjusted residuals exceeding ±1.96 are significant at p < .05.

79

Table 14

Cannabis and Nicotine Use Frequency Between the Psychological Maltreatment Groups

Psychological maltreatment group

Full sample

(N = 490)

Low

(n = 304)

Moderate

(n = 133)

High

(n = 53)

M (SD) M (SD) M (SD) M (SD) F (df) ηp2

Cannabis 1.04 (1.64) .83 (1.47)a 1.34 (1.80)b 1.47 (2.00)ab 6.72 (2, 487)** .03

Nicotine 1.20 (1.93) .82 (1.62)a 1.58 (2.16)b 2.28 (2.34)b 16.96 (2, 487)** .07

Note. All p-values are Bonferroni adjusted. Cannabis and nicotine use frequency were assessed with

the Drug Use Questionnaire (DUQ).

aWithin each row, means with different subscripts are significantly different at p < .05.

*p < .05. **p < .01.

80

Table 15

Crosstabulation of Psychological Maltreatment Groups and Drug Use Frequency

Psychological maltreatment group

Drug use frequency

Low

n = 304

Moderate

n = 132

High

n = 53

Full sample

(N = 489)

None

85.9%

n = 261

(4.2)

70.5%

n = 93

(-3.2)

69.8%

n = 37

(-2.0)

80.0%

n = 391

Low

8.2%

n = 25 (-.8)

10.6%

n = 14 (.8)

9.4%

n = 5 (.1)

9.0%

n = 44

Moderate – high

5.9%

n = 18

(-4.6)

18.9%

n = 25

(3.4)

20.8%

n = 11

(2.4)

11.0%

n = 54

Note. Adjusted residuals appear in parentheses below observed frequencies. Adjusted residuals

exceeding ±1.96 are significant at p < .05. Drug use frequency was assessed with the Drug Use

Questionnaire (DUQ).

81

Table 16

Crosstabulation of Psychological Maltreatment Groups and Drug Use Problems

Psychological maltreatment group

Drug use problem severity

Low

n = 302

Moderate

n = 131

High

n = 53

Full sample

(N = 489)

None

80.5%

n = 243

(5.8)

58.0%

n = 76

(-3.9)

50.9%

n = 27

(-3.4)

80.0%

n = 391

Mild

11.9%

n = 36

(-2.6)

21.4%

n = 28

(2.3)

18.9%

n = 10

(.8)

9.0%

n = 44

Moderate – severe

7.6%

n = 23

(-4.9)

20.6%

n = 27

(2.7)

30.2%

n = 16

(3.7)

11.0%

n = 54

Note. Adjusted residuals appear in parentheses below observed frequencies. Adjusted residuals

exceeding ±1.96 are significant at p < .05. Drug use problem severity was assessed with the Drug Use

Questionnaire (DUQ).

82

Table 17

Depression and Anxiety Between the Psychological Maltreatment Groups Controlling for CSA and CPA Victim Status

Psychological maltreatment groups

Low

(n = 303)

Moderate

(n = 133)

High

(n = 53)

M (SD) M (SD) M (SD) F (df) ηp2

Depression 2.88 (3.97)a 4.66 (4.31)ab 6.47 (6.12)b 5.27 (2, 484)* .02

Anxiety 2.38 (3.16)a 4.45 (4.00)b 5.91 (4.62)b 12.03 (2, 484)** .05

Note. All p-values are Bonferroni adjusted. CSA = child sexual abuse; CPA = child physical abuse.

Depression and anxiety were assessed with the Depression, Anxiety, and Stress Scale (DASS-21).

aWithin each row, means with different subscripts are significantly different at p < .01.

*p < .05. **p < .01.

83

Table 18

Alcohol Use Between the Psychological Maltreatment Groups Controlling for CSA and CPA Victim Status

Psychological maltreatment groups

Low

(n = 303)

Moderate

(n = 133)

High

(n = 53)

M (SD) M (SD) M (SD) F (df) ηp2

AUDIT 4.16 (4.40)a 5.75 (5.57)a 6.61 (6.96)a 3.86 (2, 484)* .02

HED .59 (.62) .73 (.70) .92 (.94) 3.63 (2, 484) .02

Note. AUDIT = Alcohol Use Disorder Identification Test; HED = Heavy Episodic Drinking; CSA =

child sexual abuse; CPA = child physical abuse. All p-values are Bonferroni adjusted.

aWithin each row, means with different subscripts are significantly different at p < .05.

*p < .05.

84

Table 19

Cannabis and Nicotine Use Frequency Between the Psychological Maltreatment Groups Controlling for CSA and CPA Victim Status

Psychological maltreatment groups

Low

(n = 303)

Moderate

(n = 133)

High

(n = 53)

M (SD) M (SD) M (SD) F (df) ηp2

Cannabis use frequency .82 (1.46) 1.34 (1.80) 1.47 (2.00) 2.70 (2, 484) .01

Nicotine use frequency .84 (1.61)a 1.58 (2.16)ab 2.28 (2.34)b 5.42 (2, 484)* .02

Note. CSA = child sexual abuse; CPA = child physical abuse. All p-values are Bonferroni adjusted.

Cannabis and nicotine use frequency were assessed with the Drug Use Questionnaire (DUQ).

aWithin each row, means with different subscripts are significantly different at p < .05.

*p < .05.

85

Figure 1

Figure 1. Subtypes of psychological maltreatment (PM) for the three PM groups, low PM group

(n = 304), moderate PM group (n = 133), high PM group (n = 53). Error bars represent 95%

confidence intervals.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Terrorizing Spurning Exploiting/

corrupting

Isolating Denying

emotional

responsiveness

Mea

n P

sych

olo

gic

al

Mal

trea

tmen

t S

ubty

pe

Sco

res

Psychological Maltreatment Subtypes

Low Moderate High

86

Figure 2

Figure 2. Standardized severity of other types of child maltreatment for the three

psychological maltreatment (PM) groups, low PM group (n = 291), moderate PM group (n =

131), high PM group (n = 49). Standardized scores = z-scores + 1. Error bars represent 95%

confidence intervals. CSA = child sexual abuse; CPA = child physical abuse. Neglect,

physical abuse, and sexual abuse severity were measured with the Computer Assisted

Maltreatment Inventory.

0

0.5

1

1.5

2

2.5

3

Neglect CSA CPA

Sta

ndar

diz

ed s

ever

ity s

core

s

Psychological Maltreatment Group

Low Moderate High

87

Figure 3

Figure 3. Percent of participants who were victims of CSA and CPA in each of the three

psychological maltreatment (PM) groups, low PM group (CSA: n = 304, CPA: n = 303),

moderate PM group (CSA: n = 133, CPA: n = 133), high PM group (CSA: n = 53, CPA: n =

53). CSA = child sexual abuse; CPA = child physical abuse. Physical abuse and sexual abuse

victim status were assessed using the Computer Assisted Maltreatment Inventory.

32.6%

58.6%

79.2%

36.8%

74.4%

96.2%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Low Moderate High

Per

cent

Vic

tim

s

Psychological Maltreatment Group

CSA

CPA

88

Figure 4

Figure 4. Percent of participants who were victims of CSA and/or CPA in each of the three

psychological maltreatment (PM) groups, low PM group (CSA: n = 304, CPA: n = 303),

moderate PM group (CSA: n = 133, CPA: n = 133), high PM group (CSA: n = 53, CPA: n = 53).

CSA = child sexual abuse; CPA = child physical abuse. Physical abuse and sexual abuse victim

status were assessed using the Computer Assisted Maltreatment Inventory.

Low Moderate High

No CSA or CPA 47.4% 12.8% 0.0%

CSA Only 20.4% 28.6% 20.8%

CPA Ony 15.8% 12.8% 3.8%

CSA & CPA 16.4% 45.9% 75.5%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%P

erce

nt

Vic

tim

s

Psychological Maltreatment Group

Figure 5

Figure 5. Characteristics of child sexual abuse (CSA) experiences by psychological maltreatment (PM) group. Only CSA victims

were included, low PM group (n = 99), moderate PM group (n = 78), high PM group (n = 42). Characteristics of sexual abuse were

assessed using the Computer Assisted Maltreatment Inventory. For each characteristic, the most severe category (or maximum

value) was identified and coded as present or absent for each participant.

Intrafamilial

perpetrator

Number of

perpetrators:

≥ 3

Nature:

Penetration

Tactic:

Physically held

down

Frequency:

> 10 timesDuration:

≥ 2 years

Total sample 42.5% 21.9% 62.6% 50.7% 29.7% 34.7%

Low PM group 29.3% 15.2% 55.6% 47.5% 21.2% 27.3%

Moderate PM group 46.2% 23.1% 66.7% 47.4% 32.1% 38.5%

High PM group 66.7% 35.7% 71.4% 64.3% 45.2% 45.2%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Per

cent

Characteristics of CSA

90

Figure 6

Figure 6. Characteristics of child physical abuse (CPA) experiences by psychological maltreatment (PM) group. Only CPA victims

were included, low PM group (n = 112), moderate PM group (n = 99), high PM group (n = 51). Characteristics of physical abuse

were assessed using the Computer Assisted Maltreatment Inventory. For each characteristic, the most severe category (or maximum

value) was identified and coded as present or absent for each participant.

Perpetrator

type: Parent

Number of

perpetrators:

≥ 3

Nature:

Choked, beaten,

burned

Injury:

Bone fractures,

burns, internal

injury, paralysis

Frequency:

> 10 timesDuration:

≥ 2 years

Total sample 96.6% 25.2% 71.0% 7.3% 42.4% 88.9%

Low PM group 97.3% 21.4% 50.9% 0.9% 35.7% 83.0%

Moderate PM group 96.0% 24.2% 81.8% 7.1% 37.4% 92.9%

High PM group 96.1% 35.3% 94.1% 21.6% 66.7% 94.1%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Per

cent

Characteristics of CPA