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Related Concept Videos

Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Hypothesis Test for Test of Independence01:16

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The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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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.
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Self-report inventories are objective personality assessments that use multiple-choice items or numbered scales, typically ranging from 1 (strongly disagree) to 5 (strongly agree). They are often called Likert scales after Rensis Likert. These inventories are widely used due to their ease of administration and cost-effectiveness. One of the most prominent examples is the Minnesota Multiphasic Personality Inventory (MMPI), initially developed in the 1940s to assess abnormal personality traits.

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Related Experiment Video

Updated: Jul 24, 2026

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
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A revised Alcohol Expectancy Questionnaire: factor structure confirmation, and invariance in a general population

W H George1, M R Frone, M L Cooper

  • 1Department of Psychology, University of Washington, Seattle 98195, USA.

Journal of Studies on Alcohol
|March 1, 1995
PubMed
Summary

The Alcohol Effects Questionnaire-3 (AEQ-3) shows moderate fit and invariance across groups for measuring alcohol expectancies. However, discriminant validity requires improvement for reliable use.

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Area of Science:

  • Psychosocial research on alcohol use and abuse.
  • Development and validation of psychological assessment instruments.

Background:

  • The alcohol expectancy construct is key in understanding alcohol use.
  • Previous instruments include the 90-item Alcohol Expectancy Questionnaire (AEQ) and the 40-item Alcohol Effects Questionnaire (AEQ-2).

Purpose of the Study:

  • To evaluate the AEQ-3, a modified AEQ-2 with a six-point scale.
  • To confirm its factor structure and assess invariance across gender and race subgroups.

Main Methods:

  • Administered the AEQ-3 to a large general population sample (N = 1,260).
  • Utilized confirmatory factor analyses to test an eight-factor model.
  • Assessed model invariance across race and gender.

Main Results:

  • The eight-factor model demonstrated a moderately good fit to the data.
  • The model's fit was largely invariant across race and gender subgroups.
  • Inadequate discriminant validity was identified through factor intercorrelations and modification indices.

Conclusions:

  • The AEQ-3 is cautiously recommended for measuring alcohol expectancies.
  • Further recommendations and limitations for its use are discussed.