Parent–Child Proximity: Automatic Cognitions Matter

Stefan Stieger • Martin Voracek • Ingo W. Nader

Accepted: 26 November 2013 / Published online: 5 December 2013� Springer Science+Business Media Dordrecht 2013

Abstract Individuals’ moving behavior (e.g., residential mobility) is an emerging topic

in many scientific disciplines. One specific aspect is the distance between parents and their

children (i.e., parent–child proximity). Although determinants and moderators of parent–

child proximity can be manifold, we concentrated on the psychological concepts self-

esteem and affect by assessing explicit (i.e., conscious) and implicit (i.e., automatic)

aspects. Besides well-known correlates of moving behavior (e.g., education), we found that

participants (N = 1,765; cross-sectional design) with high positive explicit affect and low

negative implicit affect moved further away from their parents’ homes. Therefore, parent–

child proximity may not be only based on fundamental sociocultural and socioeconomic

needs (e.g., income, family bonds), but also on automatic psychological aspects, such as

implicit affect.

Keywords Implicit cognitions � Moving behavior � Parent–child proximity �Positive/negative affect � Self-esteem

1 Introduction

The geographical distance between individuals is a topic studied in many different sci-

entific disciplines. For example, environmental studies analyze the determinants of moving

behaviors (i.e., residential change) and residential mobility (e.g., Oishi et al. 2012b; Oishi

and Schimmack 2010). In general, research concerning the interplay of individuals and

their (geographical) environment has so far focused on the following aspects: (1) socio-

cultural aspects, as for example family bonds that are tighter in southern than in northern

Europe (e.g., Reher 1998); (2) demographic aspects, such as age, sex, marital status, family

S. Stieger (&) � M. Voracek � I. W. NaderDepartment of Basic Psychological Research and Research Methods, School of Psychology, Universityof Vienna, Liebiggasse 5, 1010 Vienna, Austriae-mail: stefan.stieger@univie.ac.at

123

Soc Indic Res (2014) 119:967–978DOI 10.1007/s11205-013-0524-3

size, or education (for a discussion, see Hank 2007); (3) socioeconomic aspects, such as

housing tenure (children live closer to their parents when parents have financially sup-

ported their housing tenure: Tomassini et al. 2003); (4) geographical aspects, such as the

attractiveness of places (Altman and Low 1992).

Interestingly, little research has been conducted on correlates of psychological aspects

of individuals’ moving behaviors. In a large prospective study in Finland, temperament

traits (i.e., emotionality, sociability, and activity) influenced individuals’ moving behavior

(Jokela et al. 2008). More (vs. less) sociable individuals moved to urban (vs. rural) areas

and generally had longer (vs. shorter) moving distances. More active (vs. passive) indi-

viduals, on the other hand, moved in general to rural and urban areas alike, and high (vs.

low) emotional individuals had an increased likelihood of leaving parents’ home, but

decreased moving distance. Also, correlations of personality with moving behaviors have

been found (Big Five: Silventoinen et al. 2008). Men (but not women) higher in neurot-

icism and extraversion were more likely to move.

Recently, an increasing number of studies have been conducted on residential mobility (e.g.,

Oishi 2010; Oishi et al. 2012a; Oishi and Schimmack 2010), which is defined as the ‘‘frequency

with which individuals change their residence’’ (Oishi 2010, p. 6). More residential moves were

associated with lower self-esteem and higher negative affect; hence, frequent residential moves

in childhood may be stressful because of its negative effect on subjective well-being in later life

(Oishi et al. 2012a). This effect was only observed among introverted children (i.e., probably

having difficulty making new friends in new places), but not among extraverted children (Oishi

and Schimmack 2010; Oishi et al. 2012b). To sum up, mobility affects the lives of individuals

and is a key factor underlying globalization, thereby emphasizing its future significance for

psychological research (for a recent review, see Oishi, 2010).

One specific aspect of moving behavior is the distance between parents and their

children, which is called parent–child proximity. Unfortunately, research about psycho-

logical correlates of this behavior is still scarce, albeit of high societal interest (e.g., Guo

et al. 2011; Ha and Carr 2005; Lin and Rogerson 1995). For example, in gerontology,

parent–child proximities are analyzed in light of social integration and well-being of the

elderly (e.g., Ha and Carr 2005), which may become a major health care problem due to

decreasing birth rates and increasing life expectancy in Western societies.

Although one would rather assume that moving away from parents’ home is a highly

conscious decision (e.g., I want to be independent; I do not get along with my parents

anymore), past research on implicit social cognition suggests that many behaviors are not

only influenced by conscious decisions, but also by unconscious motives (Greenwald et al.

2009). For example, Pelham et al. (2002) found that the decision as to where to live is also

(unconsciously) influenced by peoples’ name letters. If a man’s first name is Jack, he is

more likely to live in Jacksonville than in Philadelphia (where the Philips are more likely

to live). This implicit egotism effect has been extended to other major life decisions, such

as in which company to work (Anseel and Duyck 2008; but see Simonsohn 2011). So the

decision as to where to live seems not only a conscious decision because of sociocultural

and socioeconomic reasons, but also partly an unconscious implicit one.

When it comes to unconscious decisions and behaviors, research has increased dra-

matically since the introduction of the concept of implicit cognitions (Greenwald and

Banaji 1995) and the Implicit Association Test (IAT: Greenwald et al. 1998). Implicit

(automatic) cognitions predict automatically elicited behaviors (e.g., sweating during a

speech), whereas explicit (conscious) cognitions predict conscious behaviors (e.g.,

behaviors individuals are aware of). This separation in distinct behavior-related cognitions

is reflected by dual-process theories (e.g., Strack and Deutsch 2009).

968 S. Stieger et al.

123

The present study was designed to analyze whether explicit and implicit cognitions are

associated with parent–child proximity (i.e., how far someone moves away from parents’

home). Although there are many potential psychological constructs that could be associ-

ated with partent-child proximity (e.g., personality, personal values, attitudes), we focused

on self-esteem and affect (see Oishi et al. 2012a) because of their ease of measurement

(i.e., survey-based). Other implicit measurement procedures such as the IAT are computer-

based reaction time tasks, which are time-consuming and not without problems when

applied multiple times for the measurement of different constructs because of order effects

(e.g., Stieger 2013).

Moving can be driven by many motives, external as well as internal ones, and can either be

resource-orientated (because I have money, I can move) or deficiency-orientated (because I

do not have money, I must move), thus leading to different predictions for self-esteem and

affect. For example, individuals with low self-esteem might move away (e.g., because I have

low self-esteem, this motivates me to change my life), but also could stay at their parents’

home (e.g., I have low self-esteem, therefore I do not dare to move away). Considering this,

specific hypotheses were not formulated and the study was designed as explorative.

2 Method

2.1 Participants

Participants (N = 1,765; 58 % women) were German-speaking volunteers recruited

through snowball sampling and constituting a convencience sample (74 % Austrians, 23 %

Germans, 3 % other; Mage = 37.7 years, SD = 17.0; range 16–89 years). The sample was

almost balanced regarding their highest educational level (19 % primary education, 22 %

apprenticeship diploma, 37 % secondary education, 22 % university degree). Participants’

current relationship status was: 25 % single, 32 % in a relationship, 36 % married, 4 %

divorced, and 3 % widowed.

For half of participants (53 %), the stated postal code was identical to their parents

postal code (both postal codes were self-stated by participants). The remaining participants

moved away on average 136.5 miles from their mothers’ home (SD = 532.4, range

.48–8,234.9 miles; see Fig. 1) and on average 164.9 miles away from their fathers’ home

(SD = 592.2, range .48–6,025.5 miles). In general, participants’ postal codes were wide-

spread, with 521 different postal codes returned.

2.2 Materials

2.2.1 Rosenberg Self-Esteem Scale (RSES)

The RSES (German form: von Collani and Herzberg 2003) is a 10-item global measure of

explicit self-esteem rated on a 4-point scale (0: Totally disagree; 3: Totally agree; sample

a = .82). Higher values reflect higher self-esteem.

2.2.2 Initial Preference Task (IPT)

The IPT (Kitayama and Karasawa 1997) is a trait measure of implicit self-esteem based on

the Name Letter Effect (NLE; i.e., individuals rate their name-letters more favorably than

Parent–Child Proximity 969

123

non-name letters; Nuttin 1985) with good psychometric properties (for a review, see

Stieger et al. 2012). Participants were presented with a randomized list of letters (A–Z) and

12 non-letter ASCII code symbols (e.g., ,, , €, D, �; for recent applications, see Gregg

and Sedikides 2010; Stieger and Burger 2013) that were rated on a 7-point scale (1: I don’t

like at all; 7: I like). The NLE was calculated, using the recommended I algorithm (Baccus

et al. 2004), separately for the first-name and the last-name initial (Stieger et al. 2012;

correlation between IPT-first and IPT-last: r = .41). The I algorithm controls for individual

response tendencies (e.g., liking more or less all letters) as well as for general normative

liking of certain letters (i.e., some letters are generally preferred more than others).

2.2.3 General Name Liking (GNL)

The GNL (Gebauer et al. 2008) is a measure of global implicit self-esteem. Participants

were asked ‘‘How much do you like your first name?’’ (GNL-first) and ‘‘How much do you

like your surname?’’ (GNL-last) which had to be answered on a 9-point scale (1: Not at all,

9: Very much; correlation between GNL-first and GNL-last: r = .34; their correlations

with RSES: r = .18 and r = .16 respectively; all ps \ .001).

2.2.4 Positive and Negative Affect Schedule (PANAS)

The PANAS (German form: Krohne et al. 1996) is a measure of explicit positive and

negative affect. Participants were asked how they feel ‘‘in general’’ (to measure affect as a

Fig. 1 Geographical distances between participants and their mothers

970 S. Stieger et al.

123

trait) regarding ten descriptors of positive and negative affect (1: Not at all, 2: A little, 3:

Moderately, 4: Quite a bit, 5: Extremely). Higher values reflect higher explicit positive

affect (EPA; a = .81) and higher explicit negative effect (ENA; a = .82), respectively.

2.2.5 Implicit Positive and Negative Affect Test (IPANAT)

The IPANAT is a trait measure of implicit positive and negative affect with good psy-

chometric properties (Quirin et al. 2009). The IPANAT uses mood judgments of artificial

words (e.g., TUNBA) to assess implicit affect. Participants rated six nonsensical words on

three positive (happy, cheerful, energetic) and three negative emotions (helpless, tense,

inhibited) each on a 4-point scale (1: Doesn’t fit at all, 2: Fits somewhat, 3: Fits quite well,

4: Fits very well). Implicit positive affect (IPA; a = .80) is the mean score of all positive

mood adjectives and implicit negative affect (INA; a = .78) the mean score of all negative

mood adjectives.

2.2.6 Place of Residence

Participants were asked about the country and postal code of their place of residence,

which they would describe as ‘‘home.’’ This could differ from their current place of

residence (e.g., place of study) and follows the place attachment concept (emotional

bond between a person and a particular place; Altman and Low 1992). Furthermore,

participants were asked about the country and postal code of their mothers’ and fathers’

current place of residence to calculate the distance between participants and their

parents.

2.3 Procedure

Participants gave their informed consent, completed the IPT, and then the further study

parts in the following order: RSES, PANAS, GNL, IPANAT, demographic details (age,

sex, nationality, and current relationship status) and name initials. Finally, participants

were thanked and debriefed.

2.4 Analysis of Distance Between Parents and Participants’ Home

In Austria and Germany, municipalities (equivalent to US counties) are divided into

several postal code areas, i.e., the latter ones geographically are more precise than the

municipality itself. First, the distance as the crow flies between participants’ and

parents’ place of residence was determined using an online tool (www.distance.to).

Because distances were highly skewed in the sample (skewness [ 9.8), we log-trans-

formed both the distance between participants and their mothers as well as participants

and their fathers. Because log-transformed distances were highly correlated (r = .91,

p \ .001) and because mothers’ and fathers’ place of residence differed in only 5.5 %

of cases, we used mean distance scores for analyses (skewness = .9; SE = .06). For a

graphical overview of the distances between participants and their parents, see Fig. 1

(for ease of use, we only used the zip-code of mothers).

Parent–Child Proximity 971

123

3 Results

As can be seen from Table 1, the distance between participants and their parents was

correlated with several study variables. To account for intercorrelations of study variables

to see the unaffected real effects of study variables on parent–child proximity, we per-

formed a hierarchical linear regression with parent–child distance as the criterion. In the

first block of predictor inclusion, possible demographic correlates of moving behavior were

included (age, sex, education). To analyze whether implicit evaluations have incremental

value in predicting parent–child proximity over and above explicit evaluations, explicit

self-esteem and affect were included in the second block, whilst implicit self-esteem and

affect in the final third block.

The regression model was significant at block 1, with 10 % of variance attributable to

sex and education (Table 2). In block 2, only explicit positive affect had additional pre-

dictive power (the higher explicit positive affect, the larger was the distance to parents). In

block 3, only implicit negative affect had additional predictive power (the lower implicit

negative affect, the larger was the distance to parents). Multicollinearity was acceptable

(maximum variance inflation factors [VIF] = 1.5; values above 10 are often considered

problematic). The largest b values were observed for education (.31) followed by explicit

positive affect (.08) and implicit negative affect (-.08) and finally sex (.05).1

Unfortunately, multiple linear regression analyses have the problem of inferring the

relative importance of each predictor variable. The first chosen predictor in a regression

model usually accounts for the major part of variance compared to the last predictor

variable included in the model, i.e., the last predictor has less chance of explaining vari-

ance of the criterion variable (but only if predictor variables share some variance). In order

to account for this problem, we reversed the order of blocks in the hierarchical linear

regression (Table 2, last six columns). The b values basically remained identical, i.e., the

order of inclusion into the model did not alter the values substantially to assume a sequence

effect. This supports the robustness of our finding.2

To sum up, although b values for explicit positive and implicit negative affect were

modest (albeit nominally significant), as compared to the predictive power of education as

a potent moderator of moving behavior, their predictive value can be regarded as sub-

stantial (39 % of the effect size of education). Because education was the strongest pre-

dictor for parent–child proximity, we furthermore were interested whether this relationship

is of a linear trend or some other association (e.g., quadratic). The explained variances for a

logarithmic (R2 = .09), quadratic (R2 = .11), and cubic (R2 = .11) relationship did not

1 We also calculated a linear regression by including interaction terms between implicit and explicit affectas well as implicit and explicit self-esteem (see Jordan et al. 2003). These interaction terms were included inthe regression analysis in a separate block to see whether the interaction terms have additional predictivepower. Although we found some low effect (b = .07) of the interaction between explicit self-esteem (RSES)and implicit self-esteem measured with the IPT-last on parent–child proximity, this additional block in theregression analysis was not significant, DF(6,1,731) = 1.82, p = .09. Doing the same hierarchical linearregression with the reversed order of blocks, the interaction terms failed to explain any of the variance,F(6,1,743) = 1.40, p = .21.2 One reviewer correctly pointed out that some of the prerequisites of a linear regression might be violated.In combination with the interpretation of low effect sizes, this could be problematic because some effectsmight be erratic. Nevertheless, a categorical regression taking the different levels of measurements of thepredictor variables into account, revealed basically the same pattern (i.e., significance of sex, education,EPA, INA), which again speaks for the robustness of our findings (detailed results omitted for brevity).

972 S. Stieger et al.

123

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Parent–Child Proximity 973

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974 S. Stieger et al.

123

substantially differ from the more parsimonious linear model (R2 = .10), i.e., the linear

model should therefore be favored.3

4 Discussion

The results at hand not only support the assumption that psychological concepts are

associated with parent–child proximity, they also support the assumption that implicit

evaluations have incremental predictive power beyond their explicit counterparts. Moving

behaviors are therefore not only a product of conscious decisions and attitudes, but might

also be associated with automatically activated mood evaluations as well.

In line with related research (Lin and Rogerson 1995), education had notable influence

on moving behavior. Participants with higher education moved farther away than partic-

ipants with a lower educational degree. This effect was of a linear trend, i.e., participants

with an apprenticeship level move away from their parents, but live closer to their parents

than participants with secondary education. Participants with a secondary education move

farther away from their parents than participants with an apprenticeship level, but not as far

as participants with a university degree. The average distance gain for different partici-

pants’ educational levels is constant, i.e., about 20.8 miles for each educational level.

Furthermore, women moved farther away than men, which has also been frequently

found (e.g., Ha and Carr 2005). To analyze this in more detail, we performed a post hoc

analysis by calculating the hierarchical linear regression separately for men and women.

Again, the same pattern appeared for men and women, except that some predictors just

failed to reach nominal significance because of the lower sample size (detailed results

omitted for brevity). Therefore, reasons for the larger distance between women and their

parents compared to the distance of men and their parents probably are not grounded in the

study variables at hand (e.g., education, age, affect), but would be a fruitful research

question for future research.

However, affect—best described as a basic mood which is the ‘‘breeding ground’’ for

emotions—was also correlated with parent–child proximity. Participants with high positive

explicit affect (enthusiastic, active, and attentive on a conscious level; Watson et al. 1988)

as well as low negative implicit affect (calm, placid, and relaxed on an automatic implicit

level) lived farther from their parents than participants with low positive explicit affect and

high negative implicit affect. Because the decision to move away is potentially influenced

by many further variables (that are impossible to account for), explained variance values

were low but not uncommon with studies analyzing geographical proximity (e.g., Leopold

et al. 2012: 10 %).

4.1 Limitations

Assessing the place of residence poses several difficulties. First, current residential place

might be not the actual living place (e.g., student hostels). We tried to account for this

limitation by asking for the residential place one would describe as ‘‘home’’ (i.e., where

most friends life, where one feels comfortable). Second, the postal code might be too

imprecise, because participants and their parents can share the same postal code without

3 The same analysis was performed with the real distance between participants and their parents. Again thelogarithmic, quadratic, and cubic relationship did not substantially differ from the linear model (allR2 * .01).

Parent–Child Proximity 975

123

living in the same household. This likely adds some margin of error to the same postal-

code group, but not to those who moved away. Third, places of residence might have

changed through life not only for participants, but also for their parents. This surely adds

some error variance to our data, but we think this case is rather rare, because intrastate

(within-country) migration is not that common for Austria or Germany as it is for other

countries, including the USA. For example, only 8 % of Austrians moved in the year 2010

and 54 % of those moved within the same municipality (Statistics Austria 2012). Fourth,

our design was cross-sectional, so conclusions about causality are not possible. Participants

with higher positive explicit affect had higher distances to their parents, but we do not

know whether this was the cause or the consequence of moving away. These uncertainties

need to be addressed in future longitudinal research designs.

4.2 Future Directions

The present study suggested that implicit cognitions have predictive value for moving

behavior. Although the IPT and IPANAT are considered valid and reliable measures of

implicit self-esteem and affect, future research might benefit from using indirect measures

based on response interference (e.g., IAT: Greenwald et al. 1998) or affect misattribution

(the Affect Misattribution Procedure: Payne et al. 2005), in order to gain deeper insights

not only into the predictive value of implicit cognitions on parent–child proximity, but also

for moving behaviors in general.

5 Conclusion

Parent–child proximities seem to be not only based on fundamental sociocultural and

socioeconomic needs (e.g., income, life standards, relatives), but also on psychological

aspects such as explicit and implicit affect. In line with the dual-process model of social

behaviors (i.e., conscious behaviors are controlled by the reflective system, whilst spon-

taneous behaviors by the impulsive system; Strack and Deutsch 2009), implicit evaluations

showed incremental validity over and above explicit evaluations in predicting moving

behavior. To summarize, applying measures of implicit cognitions might be beneficial for

all scientific disciplines examining geographical distances between individuals (e.g.,

environmental, gerontology, health, or ecological studies), in order to gain a deeper

understanding of the psychological underpinnings responsible for ‘‘spreading one’s

wings’’.

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