Modeling Causal Relations Between Academic and ...

Copyright 1996 by the American Psychological Association, Inc. 002l-843X/96/$3.00

Journal of Abnormal Psychology 1996. Vol. 105, No. 2,258-270

Modeling Causal Relations Between Academic and Social Competence and Depression: A Multitrait-Multimethod Longitudinal Study of Children David A. Cole, Joan M. Martin, Bruce Powers, and Ruth Truglio University of Notre Dame

The authors obtained self-reports, peer nominations, teacher ratings, and parent reports of depression and social and academic competence on 490 3rd graders and 455 6th graders near the beginning and end of the school year. Confirmatory factor analysis and structural equation modeling revealed that (a) measures showed significant convergent and discriminant validity; (b) within-wave correlations between constructs were large and significant, although the depression-social competence correlation was larger than the depression-academic competence correlation; (c) the cross-wave stability of all constructs was remarkably high; and (d) social competence at Wave 1 predicted depression at Wave 2 for 6th graders after controlling for depression at Wave 1. Depression did not predict change in either academic or social competence over time. Implications for competencebased and failure-based models of child depression are discussed.

and possible social isolation. In particular, depressed persons seek personal reassurance from others, but then tend to refuse or deny the reassurances they get. Significant interpersonal distress can ensue (Coyne et al., 1987; Hops et al., 1987), sometimes leading to the withdrawal by others from the depressed individual, possibly leaving the depressed person socially isolated. Regarding academic deficits, one might also speculate that the motivational, cognitive, and attentional problems, so often symptomatic of depression, lead to diminished school performance, lower grades, poorer teacher evaluations, and possibly lower achievement test scores than those attained by nondepressed students (Fauber, Forehand, Long, Burke, & Faust, 1987; cf. Strauss, Lahey, & Jacobsen, 1982). Of course, such deficits may be both cause and effect of depression. Depression may lead to diminished social and cognitive functioning, which in turn may elicit responses from others that exacerbate depression. As Fauber et al. (1987) noted, "this downward spiral effect then continues, with depression, social incompetence, and cognitive incompetence accelerating" (p. 170). Developmental issues are of critical importance when examining possible correlates of depression in children. For example, younger children may be more attuned to familial relationships, whereas older children may be more responsive to feedback from peers (Berndt, 1979). Older children may also be more motivated and more cognitively prepared to make complex social comparisons than are younger children (Ruble, Boggiano, Feldman, & Loebl, 1980; Ruble, Parsons, & Ross, 1976). Furthermore, interpersonal factors may simply be more salient and more important than academic factors, especially in older children. Consequently, in the current study we expected social competence to be more strongly associated with depression in older children. Empirical support for the relation of child depression to social and academic competence is often difficult to interpret. Frequently, methodological limitations such as mono-operationism artificially diminish estimates of the relations between

Deficits in social and academic competence have been implicated both as causes and as consequences of depression in children. According to a competency-based model of child depression, children internalize feedback from others about their performance in the academic and social (and other) domains. That is, children learn to regard themselves in ways that reflect the ways that others regard them (see Cooley, 1902; Mead, 1934). If children receive aversive feedback from multiple sources across multiple domains, they become cognitively cornered into adopting relatively global, negative views of themselves. Such negative self-perceptions place the child at risk for low self-esteem and possibly depression (Cole, 1990, 1991). According to a dual failure model of depression (Patterson & Capaldi, 1990; Patterson & Stoolmiller, 1991), failures in the social and academic arenas constitute major sources of negative life experiences, which directly trigger dysphoric mood and possibly depression. This process may be especially characteristic of children who are at risk for antisocial behavior (see Patterson & Capaldi, 1990). Other models suggest that social and cognitive deficits may be secondary to the onset of depression. Regarding social skill deficits, Coyne (1976) posited that depressed individuals initiate interpersonal behavioral sequences that foster social strain David A. Cole, Joan M. Martin, Bruce Powers, and Ruth Truglio, Department of Psychology, University of Notre Dame. This study was supported in part by National Institute of Mental Health Grant R29 MH47846. We wish to thank the following individuals for their invaluable assistance in the execution of this study: Laura Empey, Megan Farrell, Dennis Larson, Jennifer Maus, Maura McHugh, Suzanne O'Neill, Natalia Strawbridge, Teresa Testa, Michelle Trager, Katherine VanRooy, Renee von Weiss, Alan Ward, and Margaret Zgrabik. Correspondence concerning this article should be addressed to David A. Cole, Department of Psychology, University of Notre Dame, Notre Dame, Indiana 46556.

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depression and competence. Researchers often examine their variables one at a time, essentially ignoring the multivariate structure of their data base (e.g., Armsden, McCauley, Greenberg, Burke, & Mitchell, 1990; Huntley & Phelps, 1990; Spirito, Hart, Overholser, & Halverson, 1990). Univariate statistics may underestimate effect sizes. Two studies reported both univariate and multivariate effect size estimates, revealing noteworthy differences between the two approaches. Cole (1990) and Patterson and Stoolmiller (1991) reported zero-order correlations between depression and social competence ranging from —.08 to -.54 and between depression and academic competence ranging from -.13 to —.48. Multivariate estimates, however, were generally much stronger (—.53 to -.83). Another methodological problem is monomethodism, which can lead to overestimation of true relationships. Frequently, self-report measures of one construct are correlated with selfreport measures of other constructs, a practice that can inflate estimates of the true interconstruct correlation. Many studies report both monomethod and heteromethod correlations between measures of depression and measures of academic or social competence (e.g., Armsden et al., 1990; Cole, 1990; Fauber et al., 1987; Lefkowitz & Tesiny, 1980; Panak & Garber, 1992; Patterson & Stoolmiller, 1991; Wierzbicki & McCabe, 1988). The mean monomethod correlation across these studies was .43 for social competence and .36 for academic competence. The mean heteromethod correlation (across the same studies) was only .21 for social competence and .19 for academic competence. Indeed, every one of these studies reported monomethod correlations that were stronger than heteromethod correlations. The biasing effects of monomethodism would seem to be substantial. Problems of convergent and discriminant validity in the assessment of childhood disorders may exacerbate biases caused by mono-operationism and monomethodism. An almost commonplace finding is that multiple measures obtained from different sources (e.g., children vs. teachers vs. parents) do not converge onto a common construct (Kazdin, French, & Unis, 1983; Kazdin, French, Unis, & Esveldt-Dawson, 1983; see Achenbach, McConaughy, & Howell, 1987, for a review). Almost as prevalent are studies demonstrating that measures of ostensibly different child characteristics correlate so strongly with one another as to call into question the distinction between these constructs (e.g., Ollendick & Yule, 1990; Wolfe et al., 1987). For these reasons it becomes especially incumbent on child psychopathology researchers to demonstrate the integrity of their measures and the distinguishability of constructs in studies designed to test models of child psychopathology. Without such proof, we may only be testing tautologies and modeling the effects of method variance. In addition to these limitations is the remarkable paucity of longitudinal studies designed to estimate the stability of our constructs over time and to examine the causal relations among them. A few noteworthy exceptions, however, deserve some attention. Studies of the stability of childhood depression over time paint a relatively consistent, albeit limited, picture. Generally speaking, correlations ranged from .40 to .78 and tended to increase as the test-retest interval decreased. Wierzbicki and McCabe (1988) reported a 1-month correlation of approximately .78 for two samples of 8- to 14-year-olds. Panak and Garber (1992) reported 3-

month stability of .70, 7-month stability of .62, and 10-month stability of .50 for a sample of third, fourth, and fifth graders. Edelsohn et al. (1992) reported a correlation of .43 for first graders assessed 4 months apart. In a sample of older adolescents, Lewinsohn et al. (1994) found that self-reported depression at one point in time correlated .40 with a second assessment 13.8 months later. Finally, Reinherz, Frost, and Pakiz (1991) found 3-year stability of .42 fora sample of 17- to 19-year-olds. This collection of results should be regarded cautiously, however, in that all relied on a single self-report measure of depression administered at several points in time. As such, these stability coefficients may be deflated because of mono-operation bias or inflated because of monomethodism (or both). Longitudinal studies designed to assess causal precedence of depression and competency-related variables are even rarer. Limiting our review to research that attempted to control for Time 1 depression while predicting Time 2 depression, we found three relevant studies. In a sample of elementary school students, Panak and Garber (1992) found that self-reported social rejection predicted subsequent self-reported depression 7 months later. In a study of 8- to 14-year-olds, Wierzbicki and McCabe (1988) found that paper-and-pencil social competence scales predicted subsequent paper-and-pencil report of depression 1 month later. In both of these studies, however, the methods used to measure social competence were very similar to the method used to assess depression (i.e., either children's self-report or parents' administration of questionnaires to children), potentially opening the door to monomethod bias. A third study by Lewinsohn et al. (1994) obtained information on depressive diagnoses at several points in time using semistructured clinical interviews. Self-reported social competence related to current and previous episodes of depression but did not predict future depression. This supports the possibility that social skill deficits may be symptomatic of or pursuant to depression, but not necessarily contributing causally to the onset of depression. The purpose of the current study was fourfold. First, using four sources of information (children's self-reports, peer nominations, teachers' ratings, and parents' reports) and confirmatory factor analysis, we estimated the convergent and discriminant validity of our measures of three constructs (depression, social competence, and academic competence). Second, as part of this same analysis, we estimated the within-wave correlations between these three constructs (controlling for shared method variance), in order to compare the current results with those from previous cross-sectional studies. Third, we estimated the cross-wave stability of our constructs (again controlling for shared method variance), in order to compare the current findings with those of previous monomethod analyses. Our fourth goal was to examine the extent to which each of our Wave 1 constructs predicted each of our Wave 2 constructs (controlling for Wave 1 constructs and controlling for shared method variance). We conducted each of these tests twice, once for third graders and once for sixth graders, expecting greater construct and predictive validity for the older children.

Method Participants At Wave I (Fall 1993), 1,011 elementary school students participated in this study. Of these, 945 students also participated at Wave 2, approx-

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imately 6 months later. Students attended either third or sixth grade in one of nine public schools in a midsize midwestern school district. This sample was obtained from a larger pool of students (n = 1,240), after excluding children in self-contained special education classrooms, children with such poor reading or attentional skills that they could not complete the questionnaires, children who refused to participate, and children whose parents did not grant informed consent. Other participants included 49 teachers and 565 parents who completed questionnaires at both waves. Classes ranged in size from 16 to 28 (M = 21.1, SD = 6.2). The final sample consisted of 490 third graders and 455 sixth graders (49.2% were girls; 50.8% were boys). The sample was racially heterogeneous, including White (66.1%), African American (29.8%), Hispanic (1.7%), multi-ethnic (1.8%), and "other" (0.5%) children. The mean age was 8.37 years (SD = .51) for the third graders and 11.36 years (SD - .56) for the sixth graders. Family size (i.e., the number of people living at home) ranged from 3 to9(Af = 5, SD - 1.1). Approximately 36.7% of the children had at least one parent with a previous divorce. Parents'education levels ranged from 10 to 20 years (M= 12.9, SD = 2.6), and annual family incomes ranged from less than $10,000 to more than $90,000 (Mdn = $35,000). One pattern of missing data involved children who participated at Wave 1 but did not participate at Wave 2 (n = 66). In 54 of these cases, the child no longer attended a school in the participating school district. The remainder did not participate because they were chronically absent or refused to participate at Wave 2. Univariate comparisons (i.e., Welch's tests) between Wave 2 participants and Wave 2 "dropouts" revealed only one difference on demographic or psychosocial measures. Wave 2 dropouts tended to be regarded by peers as being somewhat less socially competent (p < .04). The other major pattern of missing data involved children for whom we obtained self-report, teacher report, and peer nomination data at both waves but received no parent questionnaires at either Wave 1 or Wave 2 (n = 380). Univariate comparisons between the parent-participant and the parent-nonparticipant groups also revealed very few significant differences. Children in the parent-nonparticipant group were regarded as less academically competent by self-report, peer nomination, and teachers ratings (ps < .001).

Measures At each of two waves, we conducted multitrait-multimethod assessments of third and sixth graders. We obtained self-report, peer nomination, teacher rating, and parent report measures of depression, social competence, and academic competence (i.e., 12 measures in all). Self-reports. The Children's Depression Inventory (GDI; Kovacs, 1981, 1982) is a widely used 27-item self-report measure of children's depressive symptoms. Each item contains three statements, scored 0, 1, or 2 in order of increasing severity. Psychometric studies of the GDI suggest that the measure has relatively high levels of internal consistency; test-retest reliability; and predictive, convergent, and construct validity, especially in nonclinic populations (Carey, Faulstich, Gresham, Ruggiero, & Enyart, 1987; Kazdin, French, & Unis, 1983; Kazdin, French, Unis, & Esveldt-Dawson, 1983; Kovacs, 1985;Lobovits&Handal, 1985; Mattison, Handford, Kales, Goodman, & McLaughlin, 1990; Saylor, Finch, Spirito, & Bennett, 1984; Smucker, Craighead, Craighead, & Green, 1986; Worchel, 1990). In the current study, one item (the suicide item) was dropped because of the concerns of school administrators. Cronbach's alpha for this 26-item GDI was .90. Harter's (1985) Self-Perception Profile for Children (SPPC) is a measure of children's self-evaluations of personal competence. This selfreport inventory contains six subscales, a global self-worth scale and five competence scales. Only the Academic Competence and Social Acceptance subscales were used in the current study. Responding to each item is a two-step process. First, children indicate whether the chosen state-

ment is "really like me" or "sort of like me." Items are scored on 4point rating scales such that high scores reflect greater self-perceived competence. Three-month test-retest reliabilities are high (.70 to .87; Harter, 1982). In general, the full SPPC shows a highly interpretable factor structure (Harter, 1985). In the current study, Cronbach's alpha for the academic and social scales were .79 and .81, respectively. Peer nominations. The Peer Nomination Index of Depression (PNID; Lefkowitz & Tesiny, 1980) consists of 13 questions about depressive symptoms (e.g., "Who often looks sad?") and asks that classmates nominate peers from the same classroom. In the current study, students made their nominations by shading in "bubbles" beside classmates' names on an optical scan sheet. Item scores consisted of the proportion (0 to 1) of classmates who nominated a given student for a particular depressive characteristic. Summing the items produces scale scores ranging from 0 (no nominations on any item) to 13 (100% nominations on every item). The PNID correlates significantly with self-reported depression, teacher-rated depression, and peer-nominated social status (Lefkowitz & Tesiny, 1984, 1985). Construct validity is evident from previous multitrait-multimethod analyses (Cole & Carpentieri, 1990). The instrument has good internal consistency (.85 in Lefkowitz & Tesiny, 1985; .88 in the current study). A slightly revised version of the Peer Nominations Measure of Competence (PNMC; see Cole, 1990, 1991; Cole & White, 1993) was used to assess peers' impressions of children's competencies in each of five domains. Only the social and academic subscales were used in the current study. Examples of items are "Who gets really good grades in most subjects?" (academic competence) and "Who has lots of friends in your class?" (social competence). One positive item and one similarly phrased negative item were used to assess competence in each domain. On an optical scan sheet, children shaded in "bubbles" to select classmates for each of the questions. Students obtained two scores for each domain. One score (the proportion of negative nominations they received from classmates) was subtracted from the other score (the proportion of positive nominations). Thus, higher scores indicated that the child received more positive than negative nominations in a particular domain. In previous research, the PNMC subscales have manifested a high degree of stability and have been negatively associated with a wide variety of maladaptive behaviors and outcomes (Cole, 1991; Cole & Carpentieri, 1990). In previous research on 750 fourth-grade students and their teachers, the academic and social competence scales of the PNMC were used as part of a multitrait-multimethod design (Cole, 1990). In a confirmatory factor analysis, the scales loaded .89 and .83 onto their respective trait factors and only . 11 and .38 onto the method factor, reflecting strong evidence of convergent and discriminant validity. Teacher's ratings. The Teacher's Rating Index of Depression (TRID; Cole, 1995) consists of the 13 PNID items reworded for use by teachers about a child. Each item is associated with a 1 to 4 rating scale (1 = n ever to 4 = often). The TRID demonstrated very good convergent, discriminant, and construct validity in a confirmatory factor analytic study of elementary school students (Cole, 1995). In the current study, the TRID manifested a high degree of internal consistency (Cronbach's a - .93). Harter's (1985) Teacher's Rating Scale of Child's Actual Behavior (TRS) is a 15-item report of teachers' appraisals of children's competencies. The inventory is of similar form and content to the SPPC described above. Only the social and academic competence scales were used in the current study. On the TRS, teachers report how they perceive children's competencies, not how they believe children perceive their own competencies. In pilot work for the current study, TRS subscales had good test-retest reliability (correlations ranged from .67 to .73 over a 4-month interval). In the current study, Cronbach's alphas for the academic and social TRS subscales were .95 and .93, respectively. Parent report. The parent form of the GDI consists of the original GDI items, reworded for use by a parent about the child. In nonclinic samples of 8- to 16-year-olds, Wierzbicki (1987) noted that a parent

MODELING COMPETENCE AND DEPRESSION form of the GDI has a strong 1-month test-retest reliability (r = .75) and adequate internal consistency (Spearman-Brown coefficient = .80). The parent CDI correlates significantly not only with other parent-administered child depression assessments (r = .64), but also with the CDI administered to children about themselves (rs = .37. .59. and .66 in different samples). In the current study the suicide item was dropped from the survey. Internal consistency of the 26-item inventory was .86. To measure parental perceptions of children's academic and social competence, we reworded the TRS for use by parents about their own children (as recommended by Harter, 1985). In pilot work for the current study, subscales of this Parent Report Scale (PRS) manifested relatively strong test-retest reliability estimates (ranging from .71 to .80 over a 4-month interval). In the current study, Cronbach's alphas for the academic and social subscales were .88 and .86, respectively.

Procedures Wave 1 occurred approximately 6 to 10 weeks into the Fall semester of the 1993-94 school year. Wave 2 assessments occurred 6 to 8 weeks prior to the end of the following Spring semester (approximately 6 months later). Doctoral psychology students and advanced undergraduates administered the CDI, SPPC, PNID, and PNMC to participating students one classroom at a time during the regular school day. One research assistant read the questionnaires aloud, requiring all students to proceed at approximately the same pace. Two or three additional research assistants circulated among the students and answered questions when they arose. The presentation of questionnaires was counterbalanced by classroom to control for order effects. Example items from the measures were enlarged and displayed on posters in front of the class, serving as additional aids during the presentation of instructions. Students who could not or elected not to participate were allowed to work quietly at their desks during the 45-min questionnaire administration time. Teachers received the TRID and TRS at the same time that the children completed their questionnaires. Substitute teachers did not participate; regular teachers completed the surveys on their return. Teachers received $30 each for their participation. Parents received the Parent CDI and the PRS by mail at about the same time that teachers and children received their inventories. Completed parent questionnaires were returned directly to the university in self-addressed, stamped envelopes. Research assistants made phone calls and sent postcards as reminders whenever necessary. Parents could either receive $10 for their participation or have $ 10 donated to the school for the purchase of educational materials.

Results Preliminary Analyses We were concerned that instruments designed to measure different constructs could contain some items that have very similar content. Such item overlap could inflate estimates of the relations between constructs in subsequent analyses. To reduce the effects of item overlap, we conducted a series of factor analyses designed to identify such items. We then deleted them from the calculation of composite scores. The first analysis involved items from the CDI and the SPPC scales, for which a single covariance matrix was constructed, pooling across the two grade levels. These data were subjected to principal axis factoring with oblique rotation. On a priori grounds, we extracted one factor for each domain of competence represented by the SPPC and one factor for depression.

261

All of the SPPC items loaded substantially onto the appropriate competence factor. Likewise, the majority of the CDI items loaded substantially onto only a general depression factor. We did find six CDI items, however, that loaded onto one of the competence factors (e.g., "I do not have any friends," and "I do very badly in subjects I used to be good in"). These items were deleted from the composite CDI score. An identical approach was taken in a second analysis of the parent CDI and the PRS. Each of the PRS items loaded substantially onto only one of the competence factors. Again, most of the parent CDI items loaded only onto a general depression factor. Six of the parent CDI items, however, cross-loaded onto one of the competence factors, the same items that cross-loaded in the children's version. These items were dropped from subsequent analyses. In a third factor analysis, we examined items from the TRID and the TRS. Once again a very interpretable solution emerged. This time only three of the TRID items cross-loaded onto one of the competence factors: "This student plays alone," "This student looks lonely," and "This student says he/she can't do things." We dropped these items from the TRID composite. Finally, we factor analyzed the PNID and PNMC items with very similar results. Only two of the PNID items cross-loaded onto one of the competence factors. We eliminated these items from the PNID composite. Use of Participants With Partially Missing Data Correlations, means, and standard deviations were computed separately for four groups of children: third graders with complete data (n = 280), third graders missing parent data (n = 210), sixth graders with complete data (« = 285), and sixth graders missing parent data (n = 170). These correlation matrices appear in the Appendix. Allison (1987) described a powerful new procedure for use with participants with partially missing data. The procedure is appropriate when a relatively large number of children have the same pattern of missing data. In the current study, third- and sixth-grade children with missing parent data constituted such groups. Joreskog and Sorbom (1993) outlined the details of this procedure using the LISREL 8 program. Briefly, the procedure is a two-group analysis in which one group contains complete data and the second group contains partial data. In the first group, all model parameters are estimated. In the second group, a subset of these parameters are estimated as allowed by the particular pattern of missing data. Parameters that are estimated in both groups are constrained to be equal across groups. The acceptability of such cross-group constraints constitutes an actual test of an assumption underlying the more traditional use of listwise deletion: the assumption that the data are "observed at random" (i.e., that the pattern of missingness is unrelated to scores on the nonmissing variables). Indeed, all of the models that follow did fit the data well, supporting the observed-at-random assumption. Within- Wave Confirmatory Factor Analysis Combining a correlated disturbances approach to multitraitmultimethod designs (Kenny & Kashy, 1992; Marsh. 1989)

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COLE, MARTIN, POWERS, AND TRUGLIO self-report peer nomination teacher rating parent report self-report peer nomination

-CX

KX

teacher rating parent report self-report peer nomination teacher rating parent report Figure 1. Generic within-wave path diagram for a multitrait-multimethod confirmatory factor analysis, allowing for correlated disturbances between measures involving similar methods.

with the previously described procedure for handling missing data, we conducted a confirmatory factor analysis' using maximum likelihood estimation. The fundamental model is depicted in Figure 1. To estimate convergent validity, we allowed each measure to load onto one trait factor (Depression, Social Competence, or Academic Competence), as depicted by the straight single-headed arrows between the boxes and the latent variables in Figure 1 (i.e., arrows a through 1). To test discriminant validity, we fixed at zero all loadings of variables onto factors that they were not designed to measure. To account for shared method variance, we allowed correlations between error terms for measures that used similar methods, as depicted by the curved double-headed arrows between the smaller circles in Figure 1. Finally, in order to estimate the within-wave associations between Depression, Social Competence, and Academic Competence, we allowed correlations between the latent variables, as depicted by the curved double-headed arrows between the larger circles in Figure 1 (i.e., arrows q, r, and s). Table 1 contains goodness-of-fit statistics for this model (and all subsequent models as well). A model typically provides a good fit to the data when the Goodness-of-Fit Index (GFI) is greater than .90, the standardized Root Mean Square of the Residuals (RMR) is small, and the Root Mean Square Error of Approximation (RMSEA) is small. The chi-square provides a statistical test of whether the difference between the actual and reconstructed covariance matrices are significant. With large sample sizes, however, statistical significance can reflect discrep-

ancies that are actually very small. The current model provided a good fit to the data derived from Wave 1 and Wave 2 at both grade levels (see top panel of Table 1). These models fit the data well without cross-loadings of variables onto factors they were not designed to measure, giving support to the discriminant validity of these measures. All of the factor loadings in each of the models were significant, providing support for the convergent validity of the measures (see top panel of Table 2). Several aspects of these loadings were noteworthy. First, most of the loadings were substantially larger than the zero-order correlations between manifest measures of the same construct, a fact that highlights the effects of mono-operation bias on zero-order estimates of convergent validity. Second, factor loadings for peer, teacher, and parent measures were moderate to large. Self-report measures, however, were only in the small-to-moderate range. Third, factor loadings for self-report and peer nomination measures were generally larger for the sixth graders than for third graders. 1

Box's tests of homogeneity of variance-covariance matrices were conducted to examine possible gender differences within grade level. Although these tests were statistically significant (ps < .05), the magnitude of the differences was small. The standardized root mean squared of the residuals was .041 for third graders and .050 for sixth graders. Given that gender differences in depression are rare in preadolescent populations, we conducted all subsequent analyses in the current study without separating boys and girls.

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MODELING COMPETENCE AND DEPRESSION

Table Goodness-of-Fit Indices for All LISREL Models Standardized Type of model MTMM models Wave 1 in Grade 3 Wave 2 in Grade 3 Wave 1 in Grade 6 Wave 2 in Grade 6 Stability models Depression in Grade 3 Depression in Grade 6 Social Competence in Grade 3 Social Competence in Grade 6 Academic Competence in Grade 3 Academic Competence in Grade 6 Causal models Depression and Social Competence at Grade 3 Depression and Social Competence at Grade 6 Depression and Academic Competence at Grade 3 Depression and Academic Competence at Grade 6

IFI

GFI

PNFI

RMR

RMSEA

x2

0.99 0.98 0.94 0.95

.97

0.87 0.87 0.86 0.87

.045 .057 .053 .055

.034 .037 .043 .041

200.79 214.04 236.51 227.74

0.97 0.99 1.02 1.01 0.99 0.98

.98

.98

0.97 1.00 1.02 1.02 1.00 1.00

.062 .050 .034 .046 .049 .037

.038 .028 .000 .000 .030 .038

99.78 79.78 36.11 47.99 83.46 97.69

1.00

.97

0.87

.054

.007

231.56

0.99

.97

0.88

.048

.018

257.48

0.97

.96

0.86

.054

.030

325.87

0.98

.96

0.87

.041

.027

302.86

.96 .96 .96 .99 .99 .99 .98

Note. IFI = incremental fit index; GFI = goodness-of-fit index; PNFI = parsimony normed fit index; RMR = root mean square of the residuals; RMSEA = root mean square error of approximation; MTMM = multi-trait, multi-method. For the MTMM models, df - 99; for the stability models, rf/'= 46; and for the causal models, df= 172.

In a multitrait-multimethod design such as this, examination of correlations between error terms reveals the degree to which a particular method of measurement affects the observed scores on a given instrument. As shown in the bottom panel of Table 2. error terms associated with self-report measures were more highly correlated than were the error terms of other measures. Correlations between self-report error terms ranged in magnitude from .24 to .39. This means that the correlation between two self-reports measures overestimates the actual relation between the underlying constructs to this extent (all other things being equal). It is noteworthy that the magnitude of the correlations between error terms was quite consistent across waves and grade levels. Of particular interest are the estimates of within-wave correlations between the Depression, Social Competence, and Academic Competence factors. Unlike correlations between manifest variables, these correlations are not attenuated because of mono-operation ism, nor are they inflated because of monomethodism. As shown in the middle panel of Table 2, the strongest correlations were between Depression and Social Competence (rs ranged from -.74 to -.81). Correlations between Depression and Academic Competence were noticeably smaller (ranging from -. 5 3 to - .69) and tended to be somewhat smaller for the older children. Correlations between Social and Academic Competence ranged from .47 to .69 and also tended to be smaller for older children.

Stability Cross-wave stability of the Depression, Social Competence, and Academic Competence factors was assessed in three separate

models, generically presented in Figure 2. Path a represents the stability of the construct over time. Paths w, x, y, and z represent the degree to which error variance of measures at Wave 1 are correlated with error variance in the same measures at Wave 2. This model provided a good fit to the data in all cases, as shown in the middle panel of Table 1. Among both third and sixth graders, the model fit well for Depression, Social Competence, and Academic Competence. Particularly noteworthy were the remarkably strong stability coefficients for all three constructs at both grade levels (see Table 3). For third graders, stability estimates ranged from .93 to .97. For sixth graders, estimates ranged from .90 to .96. Depression was slightly less stable for sixth graders than for third graders. To the extent that higher stability means less change, we would expect it to be more difficult to predict change in depression for third graders than for sixth graders. Error terms associated with each measure were also somewhat stable over time (see Table 3). For third graders, crosswave correlations between error terms ranged from .18 to .51. For sixth graders, cross-wave correlations error terms ranged from . 17 to .49. These coefficients represent the degree to which monomethodism could bias stability estimates (all other things being equal).

Structural Mode/ing With Latent Variables Two structural models were constructed, as depicted in Figure 3. The first model examines reciprocal predictive relations between Depression and Social Competence (paths m and n in the upper diagram). The second model tests reciprocal predictive relations between Depression and Academic Competence

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COLE, MARTIN, POWERS, AND TRUGLIO

Table 2 Standardized Confirmatory Factor Analysis Estimates for Wave 1 and 2 Data for Third and Sixth Graders Third graders Parameter

Sixth graders

Wave

Wave

Wave

Wave

1

2

1

2

Factor loadings Depression Self Peer Teacher Parent Social Competence Self Peer Teacher Parent Academic Competence Self Peer Teacher Parent

.37 .58 .54 .51

.48 .66 .61 .48

.52 .69 .66 .64

.36 .70 .73 .49

.30 .50 .72 .44

.34 .61 .78 .47

.50 .66 .76 .61

.52 .81 .75 .60

.33 .65 .79 .83

.48 .80 .75 .72

.58 .81

.60 .89 .80

.72 .71

.74

Factor correlations Depression with Social Depression with Academic Social with Academic

-.81

-.74

-.79

-.81

-.65

-.69

-.59

-.53 .47

.68

.69

.54

Correlations between errors Self-report Depression with Social Depression with Academic Social with Academic Peer nomination Depression with Social Depression with Academic Social with Academic Teacher rating Depression with Social Depression with Academic Social with Academic Parent report Depression with Social Depression with Academic Social with Academic

-.38

-.31

-.24

-.29

-.39

-.24

-.31

-.36

.35

.35

-.18

-.06

-.17

-.03

.35

.28

.29 -.18

.28 -.20

.03

.02

.21

.08

-.19

-.19

-.24

-.21

-.05

-.12

-.14

-.01

.06

-.03

.02

.03

-.28

-.29

-.19

-.26

-.17

-.10

-.05

-.08

.06

.07

.03

.05

(paths p and q in the lower diagram). Not depicted in these path diagrams are the measurement models (i.e., the relations between manifest variables and latent variables). As in the previous models, all four methods were used to measure all con-

structs. Furthermore, correlated errors were allowed between all pairs of measures that used the same method (within and across waves). These models fit the data well for both grade levels as indicated by large GFIs, small RMSEAs, and small residuals. Of particular interest are paths m, n, p, and q, which represent the degree that one construct at Wave 1 predicts a second construct at Wave 2, while controlling for the second construct at Wave 1. These path coefficients appear in Table 4. Several findings are noteworthy. First, the influence of Depression at Wave 1 on Social and Academic Competence at Wave 2 was negligible, after controlling for competence at Wave 1. Second, Social Competence at Wave 1 did significantly predict Wave 2 Depression for sixth graders, after controlling for Wave 1 Depression (z = 2.81, p < .005). Third, Academic Competence at Wave 1 was unrelated to Depression at Wave 2, after controlling for Wave 1 Depression.

Discussion Four major findings emerge from the current study. First, our measures of Depression, Social Competence, and Academic Competence showed greater evidence of convergent and discriminant validity than has been apparent in many previous studies. Second, Depression correlated strongly with Academic Competence in both waves; however, its correlation with Social Competence was even stronger, especially in sixth grade. Third, we found latent variable representations of all three constructs to be extremely stable over a 6-month period. Finally, in the sixth-grade sample we found evidence consistent with a social competence deficit model of child depression. Evidence did not support an academic competence deficit model or a model in which depression diminished social or academic competence. Each of these points is elaborated below.

Convergent and Discriminant Validity Results of our multitrait-multimethod analysis revealed much higher levels of convergent and discriminant validity than has previously been reported, at least for measures of depression in children (Achenbach et al., 1987; Kazdin, French, Unis, & Esveldt-Dawson, 1983). We must note, however, that our study differs from others in two critical ways. First, we compared each measure to its underlying latent variable (i.e., a factor), not to other somewhat fallible manifest measures of the factor. As previously reported (Cole, 1987; Cole, Howard, & Maxwell, 1981), mono-operational approaches to validiation often underestimate the actual validities of particular instruments. Second, the current study examines the discriminant validity of depression measures relative to social and academic competence, whereas other studies have compared depression measures to measures of other affective and behavioral disorders such as anxiety, anger, or aggression (e.g., Norvell, Brophy, & Finch, 1985; Ollendick & Yule, 1990). Measures of child depression may well possess discriminant validity with respect to some constructs, but they lack discriminant validity with respect to others. Incumbent on developmental psychopathology researchers is the demonstration of discriminant validity re-

265

MODELING COMPETENCE AND DEPRESSION

Figure 2. Generic two-wave path diagram modeling the stability of a single latent variable while controlling for shared method variance.

garding the constructs that are relevant to the particular question at hand.

Construct Intercorrelations In analyses conducted within each wave of data, we found strong and significant relations between depression, social competence, and academic competence. Nevertheless, depression correlated more strongly with social competence than with academic competence. Multivariate estimates of correlations between constructs in the current study were generally much stronger than those reported in other studies that relied on single operationalizations or univariate statistics (e.g., Armsden et al., 1990; Fauber et al., 1987; Wierzbicki & McCabe, 1988). Indeed, the current correlations were even larger than correlations between pairs of measures that used similar methods. Results of the current study, however, were very comparable to previous studies that also obtained multivariate measures of association (e.g., Cole, 1990; Patterson & Stoolmiller, 1991). Clearly, strong correlational support (exclusively on the basis of cross-sectional data) emerges for both the competence-based model (Cole, 1990, 1991) and the dual failure model of depression (Patterson & Capaldi, 1990).

Construct Stability Although we have many estimates of test-retest stability of particular measures of depression, we know very little about the stability of the underlying construct. Zero-order correlational estimates range from .78 to .42 for test-retest intervals of 1 to 13 months (e.g., Reinherz et al., 1991; Wierzbicki & McCabe, 1988). In the current study, however, the 6-month stability of the underlying depression construct was much higher: .95 for third graders and .90 for sixth graders. Social and academic

competence were also highly stable (.93 to .97). Perhaps it goes without saying, but high levels of construct stability imply that relatively little true change has occurred. Furthermore, where there is little change, the attempt to predict change probably is a frustrating enterprise. No doubt stability will diminish as the test-retest interval increases, allowing greater opportunity for

Table 3 Standardized LISREL Estimates From Three Separate Stab Hit v A nalvses Parameter

Third graders

Sixth graders

.95

.90

.46 .19 .39 .51

.49 .39 .26 .47

.93

.94

.44 .19 .28 .47

.37 .15 .27 .46

.97

.96

.32 .28 .18 .20

.34 .17 .19 .24

Depression analysis Construct stability — Depression Error stability Self-report Peer nomination Teacher rating Parent report Social Competence analysis Construct stability — Social Competence Error stability Self-report Peer nomination Teacher rating Parent report Academic Competence analysis Construct stability—Academic Competence Error stability Self-report Peer nomination Teacher rating Parent report

266

COLE, MARTIN, POWERS, AND TRUGLIO

Wave 1

Academic Competence

Wave 2 Academic Competence

One possibility is that other social factors, such as the influence of family, may be more important than peer relations for younger children (Berndt, 1979). Another possibility is that older children have developed a more refined set of social comparison skills (Ruble et al., 1980; Ruble et al., 1976). Not only are older children cognitively more capable of making social comparisons, but they are more motivated to do so as well. Third, we found that academic competence at Wave 1 did not predict change in depression at Wave 2 after controlling for Wave 1 depression. Bearing in mind the dangers inherent in affirming the null hypothesis, we merely note that the current results do not support a dual failure model of depression. Two interesting possibilities still exist, however. One is that other domains of competence or incompetence (e.g., athletic prowess or behavioral misconduct) may relate to depression where academic competence did not. Such possibilities are central to Cole's (1990) competence-based model of depression. A second possibility is that academic incompetence affects depression indirectly, perhaps through its impact on social acceptance. Social relations may provide more frequent opportunities for more personal feedback, thus rendering it a more proximal cause of depression.

Caveats Figure 3. Two path diagrams modeling causal relations between depression, social competence, and academic competence.

psychosocial variables to have an impact on depression. Future longitudinal studies should perhaps focus on somewhat longer time intervals, at least when it conies to relatively stable constructs like depression.

Causal Inferences Our study provides three preliminary pieces of information regarding the direction and magnitude of causal relations between depression and social or academic competence. First, depression at Wave 1 did not predict social or academic competence at Wave 2 after controlling for Wave 1 competence. In other words, models suggesting that children's fundamental level of social or academic competence deteriorates because of depression are not supported by the current study. Such competencies, at least as represented by these latent variables, appear to be so highly stable over a 6-month period as to render causal modeling difficult at best. This does not preclude the possibility, however, that performance may suffer on particular academic tests or in specific social situations as a consequence of depression. From the current study, we merely assert that children's underlying level of social and academic competence is not affected by depression over a 6-month interval. Second, we found for sixth graders that social competence at Wave 1 was significantly negatively related to depression at Wave 2, after controlling for Wave 1 depression. This finding is consistent with the position that social skill deficits put children at risk for subsequent depression. The question immediately arises: Why does this not appear to be true for third graders?

Certain shortcomings and limitations of the current study suggest avenues for future research. First, we encourage caution in generalizing beyond the scope of this study. Only third and sixth graders participated, and only 6 months elapsed between time points. Extending such investigations into adolescence and following participants for longer periods of time could reveal qualitatively different patterns of results, especially given the fact that correlates of depression are different in adolescence (e.g., Nolen-Hoeksema, 1987). Second, even though children's self-reports had the lowest validity coefficients in this study, we still believe it is premature to denigrate children's self-reports in studies of depression. Selfreports may represent aspects of the depressive experience that are not easily measured by teacher, peer, or even parent reports. Children's self-esteem, feelings of hopelessness, and thoughts about suicide may be difficult to assess by anything other than self-report. Conversely, outwardly manifest signs of depression

Table 4 Standardized LISREL Estimates From Two Longitudinal Analyses Third graders

Parameter

Sixth graders

Model 1 Depression 1 -» Social Competence 2 Social Competence 1 -*• Depression 2

.04 .16

.13 -.25*

.00 -.01

.14 -.04

Model 2 Depression 1 -»• Academic Competence 2 Academic Competence 1-»• Depression 2 */> o* ^ P-

1

NO r*l NO P* g «N — *N (•"•! — r-

0

(N

O r-

(N

NO

SgggggSSM —i i i i i i i i

•O

= Wave 2.

— — IN oo NO O NO »*•> >n

(~

1