Following question is based on the following paragraph. Read following paragraph and answer five questions that follow. A researcher carried out regression analysis. She wanted to predict Organization Commitment (OC) using Job Satisfaction (JS), Work Environment (WE), Leadership (L), and Employee Engagement (EE). On a sample of 100 respondents, she measured all the variables using psychological scales. JS and EE correlated 0.80; VIF for both of them is 7. She consulted a colleague who suggested to drop either JS or EE and indicated problem of imperfect measurement of variables. She dropped JS and carried out multiple regression. Overall model has F = 24 (p < .01) and R2 = 0.36.
What is the solution for problem of imperfect measurement of variables ?
Use structural equations modeling
Imperfect measurement means that observed scores contain measurement error (reliability is below 1). In ordinary regression such error attenuates correlations and biases the regression weights.
Structural equation modeling (SEM) handles this directly: its measurement model defines each construct as a latent variable measured by several indicators and separates true-score variance from error variance, and its structural model then estimates the relations among these error-free latent variables.
Using standardized scales improves measurement but still leaves observed scores with error; moderated regression tests interaction effects; and VIF only diagnoses multicollinearity. None of them corrects for measurement error.
Hence, the answer is Use structural equations modeling.
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