Subjective and Objective Memory Changes in Old Age across Five Years

Subjective and Objective Memory Changes in Old Age across Five Years 1

Running head: SUBJECTIVE AND OBJECTIVE MEMORY CHANGES IN OLD AGE

Subjective and Objective Memory Changes in Old Age across Five Years

Daniel Zimprich and Tanja Kurtz

Institute of Psychology and Education

University of Ulm

Address for correspondence:

Daniel Zimprich

Department of Developmental Psychology

Institute of Psychology and Education

University of Ulm

Albert-Einstein-Allee 47

D–89081 Ulm, Germany

e-mail: [email protected]

Tel: ++49 (0)731 / 50-23071

Fax: ++49 (0)731 / 50-23072


Abstract

Typically, subjective memory assessments (be it in form of single items or questionnaires) in

old age only weakly correlate with the performance in objective memory tests at cross-

section. It thus appears as if individual differences in subjective memory assessments hardly

reflect individual differences in memory in old age. A shortcoming of cross-sectional studies,

however, is that subjective assessments may rely on different individual standards uncon-

trolled for. One solution to this problem has been to investigate subjective and objective

memory longitudinally, thereby focusing on individual differences in intraindividual changes.

Results from studies using this approach have been mixed, with some studies showing a sig-

nificantly stronger relation between changes than between levels, and other studies showing

no such significant difference. Using data from the Zurich Longitudinal Study on Cognitive

Ageing (N = 236), we find that five-year-changes in subjective assessments of memory capac-

ity and memory changes correlate with objective memory changes .54 and .44, respectively.

These correlations are significantly stronger than at cross-section. After controlling for age,

depressive affect, and subjective health at T1, correlations are slightly attenuated, but the

basic findings remain the same.


Subjective and Objective Memory Changes in Old Age across Five Years

Introduction

Is a person’s self-judgment of her memory associated with her objective memory per-

formance? This straightforward question is motivated by the observation that objective

memory performance declines into old age [e.g., 1] whereas self-judgments of memory ex-

pressed as memory complaints increase into old age [e.g., 2]. Given these findings, one might

conjecture that, especially in old age, there is a relation between a declining objective memory

performance and a person’s self-judgment of her memory. Cross-sectional studies have, in-

deed, provided support for such a relation, but the size of the relation is small at best. For ex-

ample, Pearman and Storandt [3] found that objective measures of episodic memory were vir-

tually unrelated to subjective memory. Similarly, Zelinski et al. [4] reported only a modest as-

sociation between memory performance and self-ratings of memory in a nationally repre-

sentative sample of the oldest old. Recently, previous cross-sectional studies examining the

subjective-objective memory relation were combined in a meta-analysis [6]. Across studies,

the average correlation between objective and subjective memory was .15.1)

Although this

correlation is not nil, it is too small to support the idea that subjective memory might serve as

a (personal) diagnosticum for objective memory [7].

The fact that the relation between subjective and objective memory is small in the

general population (of older adults) does not rule out the possibility that there are subpopula-

tions in which this relation is stronger. One might, for instance, assume that older persons

who show a more pronounced decline in memory are more accurate in their subjective

memory judgments. However, Kliegel et al. [8], for example, have shown that objective

memory performance was unrelated to subjective memory complaints in elderly diagnosed

with Aging-Associated Cognitive Decline [AACD; 9]. Reversing the coin, one may suspect

that in those elderly who report pronounced memory complaints accuracy is higher with re-


gard to objective performance. However, Mascherek et al. [10], for example, have demon-

strated that in a sample of memory clinic outpatients cognitive functioning was unrelated to

cognitive complaints. Instead of defining subpopulations a priori, there are statistical analysis

approaches that aim at dividing a total sample into subsamples. Kliegel and Zimprich [11]

used a mixture-regression approach to examine cognitive complaints in older adults, which

led to two distinct subgroups. In the smaller subgroup, cognitive performance and memory

emerged as significant predictors of subjective cognitive complaints, also after controlling for

depressive symptoms and neuroticism. Although such findings are promising, one limitation

is that results based on data-driven analytic approaches are difficult to replicate.

A different approach to examine the relation between subjective and objective memory

relies on longitudinal data [e.g., 12, 13]. In longitudinal data individual differences between

persons are controlled for. This seems even more important in terms of subjective assessments

of one’s own memory functioning. One of the main intricacies in conjunction with these (and

other) self-judgments is that different persons might rely on different standards to arrive at a

judgment. Thus, the subjective-objective memory relation may be low because people use dif-

ferent standards to subjectively evaluate their memory. Longitudinally, instead of focusing on

between-person differences, between-person differences in within-person change can be ex-

amined. The critical outcome in longitudinal studies then consists of the between-person rela-

tion of objective memory performance changes and subjective memory changes across time.2)

Several longitudinal studies have used this approach, although with different out-

comes. Taylor et al. [14] found no statistically significant relation between subjective and ob-

jective memory changes. However, their study may have been underpowered due to a rela-

tively small sample size. By contrast, using a latent change model, Zimprich et al. [13] report-

ed a correlation of r = .50 between four-year changes in subjective complaints and changes

in memory performance, which was significantly larger than the correlation at cross-section (r

= .25). Martin and Zimprich [12] showed that four-year cognitive complaints changes and


uid intelligence changes were strongly correlated (r = .64), while at cross-section the correla-

tion was small (r = .11). Similarly, Parisi et al. [15] found that, across five years, changes in

subjective memory were related to changes in objective memory performance (r = .44).

Across a longer time span, Mascherek and Zimprich [16] reported that twelve-year changes in

memory complaints were correlated to changes in memory performance (r = .39). To sum-

marize, it appears as if the subjective-objective memory relation is stronger when longitudinal

changes are examined rather than cross-sectional differences. More recently, however, Pear-

man, Hertzog, and Gerstorf [17] reported that no correlation could be estimated between sub-

jective and objective memory changes. This was due to the unanticipated finding that there

was no statistically significant variance in subjective memory changes across six years in a

relatively large sample (N = 504)—which was subject to considerable attrition, though. There

are, however, some methodological limitations that may in part explain this result. The au-

thors applied an age-convergence model, which represents a mixture of a cross-sectional and

a longitudinal model. Although these models are useful in combining the longitudinal data of

multiple age cohorts in describing the average trajectory of a variable (if convergence as-

sumptions are met, see [18, 19]), it is not clear how such converge assumptions could be test-

ed for random effects. Moreover, the measure of subjective memory consisted of three 3-point

Likert-type scale items and the authors did not report a reliability estimate of their composite

score. Alternatively, the outcome variable could have been treated as being ordered-

categorical [see 20]. Although in a second set of analyses, these three items (plus an addition-

al item) were used as indicators of a latent variable, the according factor loadings were not re-

ported, making it difficult to judge the quality of the so-defined latent variable.

The goal of the present study was to add to the literature on the subjective-objective

memory relation in old age. We used two measures of subjective memory, established strong

longitudinal measurement invariance for the memory and subjective memory, and modeled

the individual Likert-type scaled items tapping subjective memory as ordered-categorical. In


these respects, our study is methodologically more precise than previous studies. The five-

year longitudinal data we use offer the possibility to include memory performance and two

measures of subjective memory, namely, subjective memory capacity and subjective memory

change. In accordance with previous studies from our lab [e.g., 12, 13, 16], a latent variable

approach will be used. Compared to mixed models, latent change models also allow for test-

ing the properties of the measurement instruments. A key concern in modeling longitudinal

changes in psychological variables is whether indicators of an underlying latent construct

mean the same thing across time. In order to ensure that the same psychological construct op-

erates in the same way at different measurement occasions, strong measurement invariance

has to be established [21]. Meredith and Horn [22], in particular, have argued that the im-

portance of measurement invariance as a tool in developmental psychology can hardly be

overstated—even more so, one might add, if the “signal” compared to “noise” is small like,

for example, in single item indicators. In the analyses presented below, the measurement of

memory complaints was based on individual Likert-type scaled items, which served as indica-

tors for latent variables. If such Likert-type scaled items are factor-analyzed as if they were

continuous or interval-scaled, there may be a critical mismatch between the information rep-

resented by the numbers assigned to the Likert-type scales and the nature of the factor model

parameters on which statistical tests are based. Besides the limitations arising from such a

levels-of-measurement perspective, another problem associated with Likert-type scaled items

is that, frequently, they show departures from both univariate and multivariate normality. Pre-

vious studies have shown that this typically results in considerable negative bias of parameters

and standard errors [24]. A methodologically more sound approach is to treat Likert-type

scaled items as ordered-categorical [25], which we did in our investigation of the subjective-

objective memory relation.

Apart from objective memory performance, which cross-sectionally appears to be only

weakly related to subjective memory measures, there are other variables that may influence


subjective memory at cross-section. Typically, at cross-section, depressive affect and neuroti-

cism are correlated with subjective memory [e.g., 8, 11]. These relations may be explained by

the fact that a person’s affective state colors the subjective evaluation of her memory perfor-

mance [cf. 26]. A reversed causal mechanism has also been suggested, where depressive af-

fect is considered a reaction to subjectively perceived memory performance deficits [cf. 27].

Longitudinally, Zimprich et al. [13] have shown that changes in depressive affect are related

to subjective memory changes. We will not go into detail regarding the affect-subjective

memory linkage here, but for the purposes of the present study, we included depressive affect

as a control variable. In addition, we included subjective health as a general indicator of an

individual’s concerns and attitudes about health and illness (of which memory problems

might form one part) as a control variable

Methods

Data come from the Zurich Longitudinal Study on Cognitive Aging (ZULU), a study

conducted in the Zurich, Switzerland, metropolitan region. At first measurement occasion, the

sample comprised 364 older adults born between 1925 and 1940. On average, participants

were 73 years old at T1 (SD = 4.4 years), 46% were female. The majority of the sample was

married and resided with others. On average, participants reported about 13 years of formal

education. There were no signs of cognitive impairment—as indicated by a mean value of

28.93 of the Mini Mental Status Examination [MMSE; 28]—or pronounced depressive af-

fect—as indicated by a mean value of 1.63 of a short form of the Geriatric Depression Scale

[GDS; 29]. Subjective health was mostly judged as “good.” In addition, participants did not

report any severe hearing or vision difficulties as judged on a 6-point Likert-type scale (M =

4.6). For a more detailed description regarding the recruitment process of the baseline sample,

see [30]. At the second measurement occasion about 18 months after T1, 336 participants re-

turned for a second assessment. Finally, about five years (5.24 years, ranging from 5.07 to

5.49 years) after T1, 236 older adults participated in a third assessment (T3).


In the analyses reported below, we focus on five-year longitudinal changes between

T1 and T3.3)

Hence, we only included those N = 236 older adults who had complete data for

T3. These 236 individuals were, on average, 72.6 years old at T1 (SD = 4.4 years), 45% were

female. Although an extensive missing data analysis is beyond the scope of the present paper,

those 236 older adults who participated at T3 differed from those 127 who did not return at T3

in that they were slightly younger (Cohen’s d = 0.20), somewhat better educated (d = 0.25),

showed a slightly better memory performance at T1 (average d = 0.25), and reported less

symptoms of depressive affect (d = 0.32). With respect to subjective memory complaints,

there were no statistically significant differences at T1 (ds = 0.11 and 0.08 for capacity and

change). Thus, as is typical in longitudinal studies on cognitive aging, the longitudinal ZULU

sample represents a positive selection of the original sample, although effect sizes were, in

general, small.

Measures

Memory

Story Recall (SR). This task consisted of story A of the Logical Memory subtest of the

German version of the Wechsler Memory Scale-Revised [WMS-R; 31]. The 66-word story

was read by an experimenter during 60 seconds. Participants were asked to listen closely and,

when the story was finished, to immediately recall as many details as possible in any order.

Scored was the number of correctly recalled propositions (possible range: 0-25). Test-retest

reliability of the story recall test is .79 [31]. While at T2 story B was used, at T3 story A was

administered again.

Picture Recall (PR). This task consisted of 12 pictures taken from the Nuremberg Age

Inventory [Nürnberger-Alters-Inventar; 32]. For each item, a picture of a simple object was

displayed for 2.75 seconds and participants were required to name the shown object aloud

(e.g., “apple”). Followed by a pause of one second, the next picture was displayed. Immedi-

ately after presentation of all 12 pictures, participants were asked to recall as many of the ob-


jects as possible. Scored was the number of correctly recalled objects (possible range: 0-12).

Test-retest reliability of the picture recall task is .67 [32]. At T3, the same set of pictures was

used as at T1.

Verbal Memory (VM). Verbal memory was assessed by five consecutive trials of a

word list recall task. The task comprised 27 meaningful, but unrelated two- to three-syllabic

words that were taken from the Handbook of German word norms [33]. The 27 words ap-

peared on a computer screen at a rate of two seconds each and participants were required to

read them aloud. After the presentation of all 27 words, participants were asked to recall as

many words as possible. This procedure was repeated five times, with the order of word

presentation being different for each trial. At each trial, the number of correctly recalled

words was scored (possible range: 0-27). For the purposes of the present study, we selected

the third verbal memory score, that is, the number of words recalled at the third study-recall

cycle.4)

Word lists were identical at T1 and T3.

Subjective Memory

Subjective memory was measured using items of two subscales from the abridged

Metamemory in Adulthood questionnaire [MIA; 34; 35]. In ZULU, the abridged MIA sub-

scales tapping memory capacity and memory change were administered.

Memory Capacity (MCap). The subscale Memory Capacity of the abridged MIA is

designated to assess a person’s knowledge of her/his memory capacity as evidenced by pre-

dictive report of performance on given task. A sample item is “I am good at remembering

conversations that I have had.” In total, the subscale comprises 12 items, which participants

are asked to answer on a five-point Likert-type scale ranging from 1 = ‘agree strongly’ to 5 =

‘disagree strongly.’ For the purposes of the present study, we selected those six items that

showed the highest factor loadings.5)

The six items selected correspond to items number 49,

52, 88, 100, 104, and 106 in the original MIA [34]. Items were reversed such that for all items

higher scores indicate a larger subjective memory capacity. A sum score of the six items


could range between 6 and 36.

Memory Change (MCha). The subscale Memory Change of the abridged MIA

measures a person’s perception of memory abilities as generally stable or subject to long-term

decline. A sample item is “The older I get the harder it is to remember clearly.” In total, the

subscale comprises 10 items, which participants are asked to answer on a five-point Likert-

type scale ranging from 1 = ‘agree strongly’ to 5 = ‘disagree strongly.’ For the purposes of the

present study, we selected those six items that showed the highest factor loadings. The six

items selected correspond to items number 16, 28, 30, 56, 58, and 89 in the original MIA [34].

Items were reversed such that for all items higher scores indicate a more pronounced subjec-

tive memory decline. A sum score of the six items could range between 6 and 36.

Control Variables

Depressive Affect (DA). Depressive affect was measured using the short form of the

Geriatric Depression Scale [GDS; 29]. The short form of the GDS contains 15 questions (e.g.,

“Do you feel that your life is empty?”), which participants are asked to answer using a yes-no

response format. Participants’ answers were combined to a sum score (possible range: 015),

where higher scores indicate more pronounced depressive affect. In the present sample,

Cronbach’s alpha was .79 at T1.

Subjective Health (SH). Subjective health was measured using a single item at T1. Par-

ticipants were asked to rate their health on a six-point Likert-type scale, ranging from 1 =

‘poor’ to 6 = ‘excellent.’ Both depressive affect and subjective health were measured at T1

and T3. However, only T1-values were included in the analyses. Doing so, individual differ-

ences in the objective-subjective memory relations due to initial individual differences in de-

pressive affect and subjective health were controlled for. Of course, there might also be a

change in depressive affect and subjective health across five years, which, however, would be

more difficult to model because there is only one manifest indicator of both variables. Alt-

hough possible [e.g., 20], this was beyond the scope of the present article.


Analysis Approach

We used latent change models [cf. 36] to address the goals of the present study. In la-

tent change models, the level of a latent construct (e.g., memory performance at T1) and the

change of this latent construct over time (e.g., memory performance changes between T1 and

T3) are estimated. More precisely, if (1) the indicators at T1 and T3 load on one latent varia-

ble and the unstandardized factor loadings of the indicators and the intercepts are invariant

over time (strong factorial invariance), and (2) a second latent variable with equal factor load-

ings is introduced for the indicators at T3, the variance of this second latent variable captures

interindividual differences in latent change over time. By modeling change on the latent level,

change is modeled uncontaminated by measurement error (see Figure 1).

Because for subjective memory we used single items as indicator variables, these vari-

ables were analyzed employing factor analysis for ordered-categorical variables. Note that if

ordered-categorical items are factor-analyzed as if they were interval-scaled, there may be a

mismatch between the information represented by the numbers assigned to the Likert-type

scales and the nature of the factor model on which statistical tests are based [23]. Moreover,

ordered-categorical variables frequently show departures from normality. Previous studies

have shown that this typically results in negative bias of parameters [37], even more so in the

multiple-groups or longitudinal case [24]. Factor analysis models for ordered-categorical vari-

ables date back to Bartholomew [38] and Muthén [39], among others. Millsap and Yun-Tein

[25] have extended these models for the multiple groups case. Their approach will be used in

the present study—with the necessary adaptations for longitudinal data.

All analyses were conducted using Mplus, Version 6 [40], employing a robust

weighted least squares (WLSM) estimator adjusted for means. Goodness-of-fit of models was

evaluated using the Satorra-Bentler rescaled 2-test. In addition, we report the Comparative

Fit Index (CFI) and the Root Mean Square Error of Approximation (RMSEA). Values of the

CFI above .90 are considered to be adequate, whereas for the RMSEA values less than .06 in-


dicate a good model fit [41]. Preliminary work of Yu [42] indicates that, with respect to the

RMSEA, it seems preferable to increase the cut-off value to .08 in conjunction with ordered-

categorical data.

Results

In reporting our results, we focus on the final models estimated.6)

Descriptive statistics

of the analysis variables are shown in Table 1. After having established strong measurement

invariance for all indicators of memory, subjective memory capacity (MCap), and subjective

memory change (MCha) across time, a latent change score model was estimated. This model

showed an acceptable fit (2 = 966, df = 443, p < .01, CFI = 0.979, RMSEA = 0.071 [90% CI

= 0.0650.077]).7)

- - - Insert Table 1 about here - - -

On average, subjectively assessed memory capacity (MCap) of the 236 older participants de-

creased significantly across the five years of longitudinal follow-up (M = –0.18). In terms of

effect size, this factor mean change reflected a small effect (Cohen’s d = –0.21), indicating

that participants perceived their memory capacity as having become somewhat smaller at T3

compared to T1. In contrast, the factor mean of subjectively assessed memory change (MCha)

increased significantly over time (M = 0.37), implying that participants perceived a more pro-

nounced memory decline at T3 compared to T1. In terms of effect size, this mean increase

corresponded to a small to medium effect (Cohen’s d = 0.43). Eventually, the factor mean of

memory performance decreased significantly over time (M = –0.32), which reflected a medi-

um effect (Cohen’s d = –0.47). Importantly, the variances of all change factors were statisti-

cally significant (p < .01), indicating that there were reliable individual differences in subjec-

tive and objective memory changes. With regard to the aims of the present study, that is, ana-

lyzing correlated changes in subjective and objective memory across five years, we found that

change in subjectively assessed memory capacity (MCap) was positively related to change in


objective memory performance (r = .54, 90% CI = .326 to .751, p < .01). This result indicates

that persons who judged their memory capacity as lower at T3 than at T1 were also those per-

sons who showed a more pronounced decline in objective memory performance. Similarly,

the correlation between changes in subjectively assessed memory change (MCha) and chang-

es in objective memory performance was statistically significant (r = –.44, 90% CI = –.667 to

–.212, p < .01). This negative correlation implies that participants who subjectively perceived

a more pronounced memory decline at T3 compared to T1 also showed a more pronounced

decline in objective memory functioning. Finally, the correlation between changes in MCap

and MCha was also statistically significant (r = –.76, 90% CI = –.845 to –.668, p < .01), im-

plying that participants who perceived their memory capacity as being lower at T3 than at T1

also perceived the decline of their memory as having increased. To summarize, the change

correlations were all in the medium to large effect size. Their size can, however, better be

evaluated when they are compared to cross-sectional correlations at T1, which were r = .13

(90% CI = –0.17 to .278, n.s.) for subjective memory capacity and objective memory perfor-

mance, r = –.073 (90% CI = –.215 to .069, n.s.) for subjective memory change and objective

memory performance, and r = –.72 (90% CI = –.774 to –.663, p < .01) for MCap and MCha.

In a second step, age, depressive affect, and subjective health were entered into the

model as predictors of both the initial levels as well as the latent changes in subjective and ob-

jective memory performance. This extended model also achieved an acceptable model fit (2

= 1208, df = 514, p < .01, CFI = 0.966, RMSEA = 0.076 [90% CI = 0.0700.081]). Depres-

sive affect had a statistically significant effect on the initial level in MCha ( = .42) as well as

on the initial level of MCap ( = –.37). Thus, persons who reported more symptoms of de-

pressive affect reported a more pronounced perceived memory decline as well as less memory

capacity at T1. Age showed a statistically significant effect on baseline objective memory per-

formance ( = –.35), implying that older persons showed a lower performance on objective

memory tasks. Together, the three control variables accounted for 19%, 13%, and 14% of var-


iance in MCha, MCap, and memory performance, respectively. None of the control variables

had a statistically significant effect on changes in MCha, MCap, or memory performance. Af-

ter controlling for the effects of age, depressive affect and subjective health, correlations be-

tween changes in subjective and in objective memory were only minimally altered and, if at

all, tended to increase. Change in perceived memory capacity was still positively related to

change in objective memory performance (r = .57, p < .01). The correlation between change

in MChap and objective memory performance, as before, was negative (r = –.47, p < .01).

The correlation between changes in MCha and MCap was virtually the same as in the previ-

ous model (r = –.76, p < .01). Moreover, the correlations at T1 were practically the same as in

the model without control variables. Note that the variances of the three change factors

(which, after accounting for the control variables, reflect, in fact, residual variances) were still

statistically significant, implying that participants differed reliably even after holding constant

the effects of age, depressive affect, and subjective health. The model is shown in Figure 1.

- - - Insert Figure 1 about here - - -

Discussion

In line with previous studies [e.g., 12, 16], the results of the present study indicate that

changes in subjective memory are correlated with changes in objective memory. More im-

portantly, these correlations among changes are substantial (corresponding to strong effects in

Cohen’s terms) and they are significantly larger than at cross-section. These findings support

the approach of focusing on longitudinal changes in subjective memory rather than on cross-

sectional subjective memory differences. As we have argued elsewhere [see 13], in order to

make a subjective judgment about one’s own memory performance, one needs a standard of

comparison. This point of reference, however, is likely to differ between persons. Different

types of comparison processes in one sample of older adults might attenuate correlations be-

tween subjective memory and objective memory performance judgments based on the former.

These individual differences in the standards used to evaluate one’s memory are controlled for


in an analysis of changes—if one assumes that these standards are relatively stable within a

person.

Two details of our results seem to support this assumption. First, both subjective

memory capacity (MCap) and subjective memory change (MCha) changed significantly over

the five-year period. If participants had changed their memory evaluation standards over time

in the sense of, for example, becoming more lenient over time, one would have expected to

see no change of subjective memory over time. Second and, in our view, more important,

whereas the correlations of subjective memory changes with objective memory changes were

much stronger than the cross-sectional correlations at T1, the correlation between MCap and

MCha were virtually the same both at cross-section (r = .72) and longitudinally (r = .76).

This is exactly what one would expect if both MCap and MCha are formed using the same

standards within a person. More specifically, because both are subjective judgments, one

would anticipate equal correlations of individual differences at cross-section and of individual

differences in changes if both are based on individually different, but stable standards within

persons. To the best of our knowledge, our study is the first that also examined the commu-

nality of changes between different facets of subjective memory.

As convincing as our results may be, previous studies using the longitudinal approach

in examining subjective and objective memory have led to mixed findings [14, 15, 17]. Of

course, studies differ in sample size, sample composition, methodology, analysis approach,

longitudinal time span, number of measurement occasions, instruments, etc. In what follows,

we focus on some of these aspects that we find most important. The measurement of subjec-

tive memory is essential if one intends to model changes over time. Many psychological ques-

tionnaires have been developed with the aim to tap individual differences at cross-section. In-

dividual differences in changes, however, are typically much smaller, at least across the time

intervals of most longitudinal studies (for example, the variance of changes in MCha was only

about 33% of the variance of MCha at T1), which makes it harder to detect them. This prob-


lem is aggravated by using short questionnaires or single items. Although there are models for

modeling change in single ordered-categorical items [see 20], they can not capture change as

precisely as models based on a continuous variable. Against this background, the finding of

Pearman et al. [17] that there were no individual differences in subjective memory changes

across six years can possibly be attributed to the way subjective memory was measured (three

variables with three ordered categories). Because, in principle, it should always be possible to

find reliable individual differences in change (i.e., a statistically significant variance of

change)—if only the measurement is fine-graded enough.8)

Of course, sample size is also an

issue due to the statistical power it brings in the analysis. Hence, in some previous studies

power might have been too low to find a statistically significant correlation between subjec-

tive and objective memory changes [e.g., 14]. Although in the study by Pearman et al. [17] the

sample at T1 was large, attrition rate was high. For example, in the dementia-free sample, 406

person participated at T1, while at T3 and T4 there were 192 and 123 participants, respective-

ly. Thus, more than half of the sample did not contribute any longitudinal information and less

than one-third provided six-year changes data. Apart from sample size, the number of spacing

of measurement occasions does have an impact on detecting change [see 43]. Across relative-

ly short longitudinal intervals, where less change is expected, the issues of measurement and

change become even more important. Measurement becomes more precise when latent varia-

bles and changes in latent variables are modeled [36]. Although mixed models are also latent

variable models, they do not include a measurement model. As a consequence, measurement

invariance [21], i.e., the degree of which latent variables are measuring the same underlying

latent construct across time, can not be examined in mixed models. The latent change models

we employed represent the simplest form of more sophisticated mixed models, namely, sec-

ond-order growth curve models, where measurement models and growth curve models are

combined. As others have argued [see 22], especially in longitudinal studies it seems vital to

verify that instruments remain the same while individuals change—and not vice versa.


Although subjective memory changes are linked to objective memory changes in our

study, for both variables this is not all there is. There remained large parts of unexplained var-

iance in change, even after controlling for age, subjective health, and depressive affect, imply-

ing that there are other explanatory variables. Candidate variables for future research are those

variables that have been shown to affect subjective memory at cross-section, for example,

neuroticism [see 8, 11]. Another route worth following might consist of combining mixture

models with growth models, thus aiming at subsamples changing differentially with regard to

the subjective-objective memory relation. Finally, future longitudinal studies might also in-

clude participants with known memory problems or dementia, because these individuals are

affected by a real and pronounced memory decline, which should increase correlated change

in subjective and objective memory.

As stated in the introduction, the notion that subjective memory and objective memory

are related, which might offer the possibility to use the former as a (personal) diagnosticum of

the latter [7], appears to be unrealistic due to their weak cross-sectional relation. However,

longitudinally, changes in subjective memory were more strongly related to changes in objec-

tive memory, both sharing 22% to 32% of variance in our study. For practical purposes, the

amount of overlap between the two found in our study is still too low to be of concrete diag-

nostic use—let alone that one would have to wait several years. However, our finding that

longitudinal changes in the two subjective memory measures are as strongly correlated (shar-

ing 58% of variance) like at cross-section shows that there appears to be a personal standard

in judging one’s memory in old age and that this personal standard seems to be relatively sta-

ble over time. Thus, from an applied perspective, the changes between two judgments of

one’s subjective memory (which take individually varying, but relatively stable personal

standards into account) may be more indicative of memory problems than a single judgment.

To summarize, our results show that there is a link between subjective memory and objective

memory—although this link is more complicated than originally hypothesized.


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Author Note

Parts of the preparation of this article were supported by the Swiss National Science

Foundation, Grant SNSF-100013-103525 and Grant SNSF-100014-122613/1.


Footnotes

1) For other forms of memory, e.g., prospective memory, similar findings have been reported

[see 5].

2) Note that the longitudinal approach implicitly assumes that within a person the standards

for evaluating one’s memory remain constant over time. Although this assumption may

seem unrealistic, it is not indispensable for the longitudinal approach to be more “success-

ful” than the cross-sectional approach. It is sufficient to adopt a weaker assumption, name-

ly, that within-person changes of memory evaluation standards are smaller than between-

person differences in memory evaluation standards at cross-section. We return to this issue

in the discussion section.

3) The data from the second measurement occasion (T2) were not used in the present study,

because this would have increased the number of analysis variables considerably, while at

the same time adding only marginally to the change variance (e.g., in a linear change

model, T2 would only contribute 9% to the change variance).

4) The reasons for selecting the third repetition of the verbal memory score were that (1) it

hard a larger variance than the score of the first repetition, implying that it differentiated

better between persons, and (2) it had the highest average correlation with the four scores

of the other repetitions, thus representing verbal memory best.

5) The motivation for doing so was twofold. For the subjective memory measures, we ap-

plied factor analysis for ordered-categorical variables, that is, factor analysis based on in-

dividual items, and we intended to limit the total number of analyses variables. Had we

used all 13 items from the Capacity and all 10 items from the Change subscales, the vari-

ance-covariance matrix to be analyzed (including the three memory measures) would have

contained 351 variances and covariances (or polychoric correlations in case of the or-

dered-categorical subjective memory items) at each measurement occasion. Thus, the

number of elements in the variance-covariance matrix would have been larger than the


sample size—which we wanted to avoid. In addition, it seems obvious that the communal-

ity of single items is smaller than that of sum scores or scale scores, which, among other

things, is due to their lower reliability. The selection of six items per subscale, in our view,

constituted a reasonable compromise.

6) More detailed analyses are available from the authors upon request.

7) To compare, a model of configural invariance produced 2 = 871, df = 383, p < .01, CFI =

0.981, RMSEA = 0.074, whereas a model of weak measurement invariance obtained 2 =

882, df = 395, p < .01, CFI = 0.981, RMSEA = 0.072.

8) In Figure 1 in Pearman et al. [17] it appears as if, indeed, the measurement of subjective

memory was not very fine-graded. In addition, there appears to be a floor effect in the

sense that many participants did not report any memory problems at all. Note that in our

study there were no floor or ceiling effects in the subjective memory measures (see Table

1).


Table 1: Descriptive Statistics of Analysis Variables

Variable Mean Std Range

Age (T1) 72.67 4.42 6580

DA (T1) 1.41 1.63 010

SH (T1) 4.90 0.70 36

MCap T1 19.74 4.54 931

MCha T1 18.08 5.68 630

SR T1 14.64 4.19 223

PR T1 6.97 1.61 312

VM T1 12.96 3.74 427

MCap T3 18.88 4.47 730

MCha T3 20.01 5.74 832

SR T3 13.50 3.82 321

PR T3 6.88 1.69 311

VM T3 12.28 4.53 326

Note. N = 236, DA = Depressive Affect, SH = Subjective Health, MCap = Subjective

Memory Capacity (Sum score of six items) , MCha = Subjective Memory Change (Sum score

of six items), SR = Story Recall, PR = Picture Recall, VM = Verbal Memory, T1 = First

Measurement Occasion, T3 = Third Measurement Occasion. Note that in our analyses, not the

sum scores of MCap and MCha were analyzed, but the individual items. The sum scores are

shown here for descriptive purposes only.


Figure Caption

Figure 1. Latent Change Model of Subjective and Objective Memory (MCap = Subjective

Memory Capacity, MCha = Subjective Memory Change, Mem = Objective Memory Perfor-

mance, MCap = Change in Subjective Memory Capacity, MCha = Change in Subjective

Memory Change, Mem = Change in Objective Memory Performance, DA = Depressive Af-

fect, SH = Subjective Health, SR = Story Recall, PR = Picture Recall, VM = Verbal Memory,

Variables in Grey are Ordered-Categorical, Numbers refer to Item Numbers of the MIA, Pa-

rameters are standardized)

Subjective and Objective Memory Changes in Old Age across Five Years

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