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Survey of Attitudes Toward Statistics: Factor Structure Invariance by Gender and by Administration Time

In addition to student learning, positive student attitudes have become an important course outcome for many introductory statistics instructors. To adequately assess changes in mean attitudes across introductory statistics courses, the attitude instruments used should be invariant by administration...

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Published in:Structural equation modeling 2004-01, Vol.11 (1), p.92-109
Main Authors: Hilton, Sterling C., Schau, Candace, Olsen, Joseph A.
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Language:English
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description In addition to student learning, positive student attitudes have become an important course outcome for many introductory statistics instructors. To adequately assess changes in mean attitudes across introductory statistics courses, the attitude instruments used should be invariant by administration time. Attitudes toward statistics from 4,910 students enrolled in an introductory statistics course were measured using the Survey of Attitudes Toward Statistics (SATS) both at the beginning and at the end of the semester. Confirmatory factor analysis on the covariance structure was used to determine the gender and time invariance properties of the SATS. Results indicate that the SATS is gender, time, and Gender x Time invariant with respect to factor loadings and factor correlations. Gender was invariant with respect to 3 of the 4 factor variances; variances from these same 3 factors were larger at the end than at the beginning of the course. Having established that the SATS is factorially invariant with respect to gender, time, and Gender x Time, its component scores can be used appropriately to examine mean attitude differences for these 2 variables and their interaction.
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source ERIC; Taylor and Francis Social Sciences and Humanities Collection
subjects Attitude Change
College Students
Factor Analysis
Factor Structure
Gender Differences
Higher Education
Measures (Individuals)
Statistics
Student Attitudes
Time
title Survey of Attitudes Toward Statistics: Factor Structure Invariance by Gender and by Administration Time
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