WAIS-III measurement invariance: Data from Estonian standardization
Kätlin Anni1, Meelis Käärik2, René Mõttus1,3
1Institute of Psychology, University of Tartu, Tartu, Estonia.
The Clinical Neuropsychologist
|August 29, 2020
Summary
The Estonian Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) shows measurement invariance across sex and age, but not educational levels. This means the intelligence test functions similarly for different sexes and ages but not educational groups.
Area of Science:
- Psychometrics
- Cognitive Assessment
Background:
- Measurement invariance (MI) is crucial for comparing individuals across groups using psychological instruments.
- The Estonian version of the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) was recently adapted.
Purpose of the Study:
- To evaluate the measurement invariance (MI) of the Estonian WAIS-III across sex, age groups, and educational levels.
- To assess if the WAIS-III measures the same psychological constructs consistently across these demographic variables.
Main Methods:
- Confirmatory factor analysis (CFA) was used to establish the baseline factor model for the Estonian WAIS-III.
- Multi-group confirmatory factor analysis (MG-CFA) was applied to test for measurement invariance across sex, age, and education.
Main Results:
- The four-factor model was supported by CFA.
- Partial measurement invariance was found across sexes and age groups, with significant mean differences noted.
- Measurement invariance was not tenable across educational levels, indicating potential differences in test interpretation.
Conclusions:
- The Estonian WAIS-III demonstrates partial measurement invariance across sex and age groups, supporting its use for comparisons within these demographics.
- The lack of invariance across educational levels suggests caution when interpreting WAIS-III scores for individuals with different educational backgrounds.
Related Concept Videos
One-Way ANOVA: Equal Sample Sizes
3.9K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.9K
Wechsler's Contribution to Measures of Intelligence
1.9K
David Wechsler, a psychologist who worked with World War I veterans, developed a significant IQ test in 1939 called the Wechsler-Bellevue Intelligence Scale. This test was innovative because it combined several subtests that measured both verbal and nonverbal skills, reflecting Wechsler's belief that intelligence is a global capacity involving purposeful action, rational thinking, and effective interaction with the environment. This test later evolved into the Wechsler Adult Intelligence...
1.9K
Measures of Intelligence
8.1K
Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
8.1K
One-Way ANOVA: Unequal Sample Sizes
6.5K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.5K
Estimating Population Mean with Unknown Standard Deviation
8.6K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
8.6K
Estimating Population Mean with Known Standard Deviation
9.4K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.4K


