Psychometric Intelligence and Achievement: A Cross-Lagged Panel Analysis.

TL;DR: In this paper, a cross-lagged panel analysis of WISC-III and achievement test scores of 289 students assessed for special education eligibility with a test-retest interval of 2.8 years was conducted.

About: This article is published in Intelligence.The article was published on 2007-01-01 and is currently open access. It has received 179 citations till now. The article focuses on the topics: Academic achievement & Intelligence quotient.

Summary

Introduction

• There has been considerable debate regarding the causal precedence of intelligence and academic achievement.
• Their conclusions were also weakened by reliance on group administered IQ and achievement tests.

1.1. Participants

• Participants were 289 students (192 male and 97 female) twice tested with theWechsler Intelligence Scale for Children-Third Edition (WISC-III; Wechsler, 1991) for determination of eligibility for special education services.
• Students were diagnosed by multidisciplinary evaluation teams according to state and federal guidelines governing special education classification.
• Only 3 students were retested within one year and only 14 within two years.

1.2. Instruments

• The WISC-III is an individually administered test of intelligence for children aged 6 years through 16 years, 11 months that was standardized on a nationally representative sample (N=2200) closely approximating the 1988 United States Census on gender, parent education (SES), race/ethnicity, and geographic region.
• In addition, the WISC-III provides four factor-based index scores: Verbal Comprehension (VC), Perceptual Organization (PO), Freedom from Distractibility (FD), and Processing Speed (PS) (M=100, SD=15).
• Even if a constraint was imposed to allow the model to be identified, the second-order model would have been statistically equivalent to the first-order model and, therefore, non-informative.
• Psychometric intelligence or ability in this study contained variance attributable to the first-order factors (VC and PO) as well as variance from the unmodeled second-order g factor.
• In math, separate calculation and reasoning subtests (M=100, SD=15) were available for all academic achievement instruments.

1.3. Procedure

• Two thousand school psychologists were randomly selected from the National Association of School Psychologists membership roster and invited via mail to participate by providing test scores and demographic data obtained from recent special education triennial reevaluations.
• Data were voluntarily submitted on 667 cases by 145 school psychologists from 33 states.
• Of these cases, 289 contained scores for the requisite eight WISC-III and four academic achievement subtests.
• These 289 cases were provided by 67 school psychologists from 27 states.

1.4. Analyses

• There were no serious departures from univariate normality (Onwuegbuzie & Daniel, 2002).
• EQS (Bentler, 2002; Bentler & Wu, 2002) was used for model estimation, and robust maximum likelihood solutions with Satorra and Bentler (1994) correction to chi-square and standard error estimates were requested.
• Watkins and Canivez (2001) demonstrated factor invariance across time for these same WISC-III subtests.
• Because the primary interest of the study was the structural relations among the time 1 and time 2 factors and because a better fitting CFA model would provide a better baseline model for that purpose, structural relations were tested based on the original CFA model.
• Similarly, if reading and math achievement did not influence each other, M5 would not provide a significantly worse fit than M3.

2. Results

• Descriptive statistics for the WISC-III IQ and factor index scores across test and retest occasions are presented in Table 1, the correlations between IQ and achievement tests at both times in Table 2, and the correlations between IQ and achievement tests across time in Table 3.
• According to these criteria, the data fit M1, M2, and M3 quite well.
• Additionally, several statistically significant coefficients in M3 (Read1→VC2, PO2, and Math2) were negative, which made little theoretical sense, and there was an out-of-bound standardized path coefficient (N1.0 for Math1→Math2).
• M4 was significantly worse than M2 and M5 was significantly worse than M3.
• The final simplified, longitudinal, crosslagged model of IQ and achievement across time is presented in Fig. 2.

3. Discussion

• There has been considerable debate regarding the separateness of psychometric IQ and academic achievement.
• Following this logic, impairments in reading would, over time, result in deleterious effects on IQ scores, subsequently making IQ a poor predictor of achievement among students with learning disabilities (Fletcher, Coulter, Reschly, & Vaughn, 2004; Siegel, 1989).
• Thus, results cannot be generalized to dissimilar students.
• Finally, the use of reevaluation cases means that those students whowere no longer enrolled in special education were not reevaluated and thus not part of the sample.

Figures

Table 1 Wechsler Intelligence Scale for Children-Third Edition and achievement scores of 289 students twice tested for special education eligibility

Fig. 7. Model 5. Note. Suffix of 1 indicates time 1 and suffix of 2 indicates time 2 testing. VC=WISC-III Verbal Comprehension and PO=WISC-III Perceptual Organization. Parameter estimates are standardized. Solid path coefficients are statistically significant, pb .05. Dotted path coefficients are not statistically significant.

Fig. 1. Conceptual model of a cross-lag design for IQ and achievement measured on two sequential occasions.

Fig. 2. Final simplified, longitudinal, cross-lagged model of IQ and achiev indicates time 2 testing. Nonsignificant loadings deleted for simplicity. PC= Arrangement, BD=Block Design, VO=Vocabulary, OA=Object Assembl comprehension, Calc=math calculation, Reas=math reasoning, VC=Verb between errors of SM1 and SM2, Comp1 and Comp2, and Reas1 and Reas2 measures are free. Parameter estimates are standardized.Fig. 3. Model 1. Not VC=WISC-III Verbal Comprehension and PO=WISC-III Perceptual Organ

Table 2 Correlation matrices for Wechsler Intelligence Scale for Children-Third Edition and achievement subtest scores at time 1 (lower triangle) and time 2 (upper triangle)

Table 3 Correlation matrices for Wechsler Intelligence Scale for Children-Third Edit

Table 4 Fit indices for five hypothesized IQ–achievement models

Fig. 3. Model 1. Note: Suffix of 1 indicates time 1 and suffix of 2 indicates time 2 testing. VC=WISC-III Verbal Comprehension and PO=WISC-III Perceptual Organization. Parameter estimates are standardized. Solid path coefficients are statistically significant, pb .05. Dotted path coefficients are not statistically significant.

Fig. 4. Model 2. Note. Suffix of 1 indicates time 1 and suffix of 2 indicates time 2 testing. VC=WISC-III Verbal Comprehension and PO=WISC-III Perceptual Organization. Parameter estimates are standardized. Solid path coefficients are statistically significant, pb .05. Dotted path coefficients are not statistically significant.

Fig. 5. Model 3. Note. Suffix of 1 indicates time 1 and suffix of 2 indicates time 2 testing. VC=WISC-III Verbal Comprehension and PO=WISC-III Perceptual Organization. Parameter estimates are standardized. Solid path coefficients are statistically significant, pb .05. Dotted path coefficients are not statistically significant.

Fig. 6. Model 4. Note. Suffix of 1 indicates time 1 and suffix of 2 indicates time 2 testing. VC=WISC-III Verbal Comprehension and PO=WISC-III Perceptual Organization. Parameter estimates are standardized. Solid path coefficients are statistically significant, pb .05. Dotted path coefficients are not statistically significant.

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Psychometric intelligence and achievement: a cross-lagged panel analysis" ?

The present study addressed this debate with a cross-lagged panel analysis of WISC-III and achievement test scores of 289 students assessed for special education eligibility with a test–retest interval of 2. 8 years. 

Q2. What are the future works mentioned in the paper "Psychometric intelligence and achievement: a cross-lagged panel analysis" ?

Regardless, the present study supports the view that intelligence, as measured by the VC and PO dimensions of theWISC-III, influences or is related to future achievement whereas reading andmath achievement do not appear to influence or are not related to future psychometric intelligence. With due consideration of these caveats, the present study provides evidence that psychometric intelligence is predictive of future achievement whereas achievement is not predictive of future psychometric intelligence. 

Q3. Why was the structure of the model tested?

Because the primary interest of the study was the structural relations among the time 1 and time 2 factors and because a better fitting CFA model would provide a better baseline model for that purpose, structural relations were tested based on the original CFA model. 

Q4. How many school psychologists were invited to participate in this study?

Two thousand school psychologists were randomly selected from the National Association of School Psychologists membership roster and invited via mail to participate by providing test scores and demographic data obtained from recent special education triennial reevaluations. 

Q5. What is the way to measure IQ?

Using structural equation modeling to remove the biasing effect of measurement error, this current crosslagged panel analysis found that the optimal ability–achievement model reflected the causal precedence of psychometric IQ on achievement. 

Q6. What is the longitudinal design for estimating the relationship of IQ and achievement?

In the absence of true experiments, longitudinal designs where both IQ and achievement tests are repeated across time have been recommended for estimating the relationship of IQ and achievement. 

Q7. What was the purpose of the study?

ACFAmodel constraining the factor loadings for WISC-III factors [VC and PO] and achievement factors [reading and math] to be equal across time 1 and time 2 was examined to test this factorial invariance hypothesis. 

Q8. How many variables were found to be univariate?

Univariateskewness of the 24 variables (12 at time 1 and 12 at time 2) ranged from − .31 to .54 and univariate kurtosis ranged from − .41 to 2.12 (Mardia's normalized multivariate kurtosis=5.88). 

Q9. What is the model fit for the achievement factor?

if intelligence was causally related to achievement as suggested by Jensen (2000), then M2 would not be significantly worse than M1 in terms of overall model fit and M2 would provide a better modeldata fit than M3.