Investigating the factorial structure and the construct validity of the Survey of Attitudes Toward Statistics (SATS): A European University Study

Author(s):

Tasos BarkatsasVasilis Gialamas Theodoros Bechrakis

Conference:

ECER 2009

Network:

22. Research in Higher Education

Format:

Paper

Session Information

22 SES 06.5, Studying in Higher Education

Paper Session

Time:

2009-09-29

13:30-15:00

Room:

HG, HS 33

Chair:

Oscar Holguin-Rodriguez

Contribution

The construct validity of the Survey of Attitudes Toward Statistics (SATS) was investigated by examining its factorial structure and by determining its gender invariance properties. The scale was administered to Social and Political Studies undergraduate students of a large European University. Construct validity evidence for the SATS was assessed using parcels-based CFA correlations, group differences and multiple regression analysis. The four-factor model was adopted and used for subsequent analysis for both empirical and theoretical reasons. All correlations among subscales were high except for the correlation between the Difficulty and the Value subscales. Male and female students’ attitudes toward statistics courses differ slightly in the Affective and the Cognitive competence subscales but not in the Value subscale. In addition, the results showed no differences between male and female students in the expected statistics course achievement. Several researchers (Araki & Shultz, 1995; Elmore & Lewis, 1991; Elmore, Lewis, & Bay, 1993; Elmore & Vasu, 1986; Harvey, Plake, & Wise, 1985; Onwuegbuzie, 1995; Roberts & Bilderback, 1980; Schau et al, 1995; Schutz, Drogosz, White, & DiStefano, 1998; Wise, 1985; Woehlke, 1991; Woehlke & Leitner, 1980; Zimmer & Fuller, 1996) have studied the factors which have an impact on students’ performance in statistics courses, including attitudes toward statistics. Roberts & Bilderback (1980) developed the Statistics Attitude Survey (SATS). SATS was designed to be one-dimensional comprising 33 homogeneous items. Since then two other well-known survey instruments regarding attitudes toward statistics have been developed: the Attitudes Toward Statistics Scale (ATS, Wise, 1985) and the Survey of Attitudes Toward Statistics Scale (SATS, Schau et al, 1995). The ATS was designed to measure two attitude components in contrast to the one-dimensional structure of the SAS. The first component measured students’ attitudes toward the usefulness of statistics in their field of study. The second component measured students’ attitudes toward the statistics courses they were attending. The SATS instrument incorporates 4 subscales which have been designed to measure negative and positive attitudes about statistics. The four subscales are the following: the Affect subscale, the Cognitive Competence subscale, the Value subscale, and the Difficulty subscale. The aims of the study were to investigate the SATS construct validity by: • Investigating the factorial structure of the questionnaire. • Determining the gender invariance properties of the SATS

Method

Instrument The SATS translated into Greek (Anastasiadou & Papadimitriou, 2002) version was used in the study. The SATS questionnaire consists of 28 items on a seven point Likert scale. The questionnaire comprises four subscales: the Affect subscale, the Cognitive Competence subscale, the Value subscale and the Difficulty subscale. Other variables for which data were collected were gender, year of study, perceived computer competence, Year 12 mathematics achievement and self-predicted achievement in university statistics courses. Analysis of Data Initially, a CFA using individual items was conducted. The reliability coefficients ranged from .63 to .85. The lowest estimate of Cronbach was obtained for the difficulty subscale, a result consistent with the subscale with the lowest estimates reported in both the Schau et al. (1995) and Cashin & Elmore (2005) studies. Construct validity evidence for the SATS was assessed using parcels-based CFA correlations, group differences and multiple regression analysis.

Expected Outcomes

The two factors Affective and Cognitive competence appear to positively correlate with some of the other factors under consideration. Affect on the other hand, was not related to course completion for female students and was only weakly related to course completion for male students. Furthermore, this study confirmed the structure equivalence for male and female students (Hilton, Schau & Olsen, 2004). All correlations among subscales were low except for the correlation between the Cognitive and the Affect subscales, a result confirming the original Schau et al. (1995) study’s results. Finally, it was found that male and female students’ attitudes toward statistics courses differ slightly in the Affective and the Cognitive competence subscales but not in the Value subscale. In addition, the results showed no evidence of differences between male and female students in the expected statistics course achievement.

References

Anastasiadou, S., & Papadimitriou J. (2002) [In Greek]. Applying data analysis methods in teaching in order to determine university students’ attitudes toward Statistics. Data Analysis Bulletin, 1(1), 65-74. Araki, L. T., & Shultz, K. S. (1995, April). Student attitudes toward statistics and their retention of statistical concepts. Paper presented at the annual meeting of the Western Psychological Association, Los Angeles. Bradley, D. R., & Wygant, C. R. (1998). Male and female differences in anxiety about statistics are not reflected in performance. Psychological Reports, 80, 245-246. Browne, M. W., & Cudek, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage Publications. Buck, J. L. (1985). A failure to find gender differences in statistics achievement. Teaching of Psychology, 12, 100. Carmines, E. G., & McIver, J. P. (1981). Analyzing models with unobserved variables: Analysis of covariance structures. In G. W. Bohmstedt & E. F. Borgatta (Eds.), Social measurement (pp. 65-115). Beverly Hills, CA: Sage. Cashin, S. E.,& Elmore, P. B. (2005). The Survey of Attitudes toward Statistics Scale: A Construct Validity Study. Educational and Psychological Measurement, 65, 509-524. Del Vecchio, A. M. (1994). A psychological model of statistics course completion. Unpublished doctoral dissertation, University of New Mexico, Albuquerque. Eccles, J. S., & Wigfield, A. (1995). In the mind of the actor: The structure of adolescents’ achievement task value and expectancy-related beliefs. Personality and Social Psychology Bulletin, 21, 215–225. Elmore, P. B., & Lewis, E. L. (1991, April). Statistics and computer attitudes and achievement of students enrolled in applied statistics: Effect of a computer laboratory. Paper presented at the annual meeting of the American Educational Research Association, Chicago. Elmore, P. B., & Vasu, E. S. (1986). A model of statistics achievement using spatial ability, feminist attitudes, and mathematics-related variables as predictors. Educational and Psychological Measurement, 46, 215-222. Elmore, P. B., Lewis, E. L., & Bay, M. L. G. (1993, April). Statistics achievement: A function of attitudes and related experiences. Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA. Elmore, P. B., & Vasu, E. S. (1980). Relationships between selected variables and statistics achievement: Building a theoretical model. Journal of Educational Psychology, 72, 457-467. Harvey, A. L., Plake, B. S., & Wise, S. L. (1985, April). The validity of six beliefs about factors related to statistics achievement. Paper presented at the annual meeting of the American Educational Research Association, Chicago. Hilton, S. C., Schau, C., & Olsen, J. A. (2004). Survey of Attitudes Toward Statistics: Factor structure invariance by gender and by administration time. Structural Equation Modelling, 11, 92-109. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. Onwuegbuzie, A. J. (1995). Statistics test anxiety and female students. Psychology of Women Quarterly, 19, 413-418. Roberts, D. M., & Bilderback, E.W. (1980). Reliability and validity of a statistics attitude survey. Educational and Psychological Measurement, 40, 235-238. Schau, C., Stevens, J., Dauphinee, T. L., & Del Vecchio, A. (1995). The development and validation of the survey of attitudes toward statistics. Educational and Psychological Measurement, 55, 868-875. Schram, C. M. (1996). A meta-analysis of gender differences in applied statistics achievement. Journal of Educational and Behavioral Statistics, 21(1), 55-70. Schutz, P. A., Drogosz, L. M., White, V. E., & DiStefano, C. (1998). Prior knowledge, attitude, and strategy use in an introduction to statistics course. Learning and Individual Differences, 10(4), 291-308. Ware, M. E., & Chastain, J. D. (1991). Developing selection skills in introductory statistics. Teaching of Psychology, 18(4), 219-222. Waters, L. K., Martelli, T. A., Zakrajsek, T., & Popovich, P. M. (1988). Attitudes toward statistics: An evaluation of multiple measures. Educational and Psychological Measurement, 48, 513-516. Wise, S. L. (1985). The development and validation of a scale measuring attitudes toward statistics. Educational and Psychological Measurement, 45, 401-405. Woehlke, P. L., & Leitner, D.W. (1980). Gender differences in performance on variables related to achievement in graduate-level educational statistics. Psychological Reports, 47, 1119-1125. Zimmer, J. C, & Fuller, D. K. (1996). Factors affecting undergraduate performance in statistics: A review of the literature. Paper presented at the annual meeting of the Mid- South Educational Research Association, Tuscaloosa, AL. (ERIC Document Reproduction Service No. ED406424).

Author Information

Tasos Barkatsas

Monash University

Education

Churchill

Vasilis Gialamas

National University of Athens, Greece

Theodoros Bechrakis

Panteio University of Social and Political Sciences, Greece

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