Gender Differences in Factors Affecting Science Performance of Eighth Grade Taiwan Students

Abstract

This study was to analyze the effects of parental educational level (P.EDU), science attitude (ATT), and valuing science (VAL) on science performance after establishing gender invariance in a representative sample of the Taiwanese eighth grade population drawn from the 2007 Trends in International Mathematics and Science Study (TIMSS). The official TIMSS five plausible values in scoring science performance were used as outcome variables, while independent variables were collected from student questionnaires. The effects of P.EDU, ATT, and VAL on science performance were estimated by a multigroup structural equation model. Results from multi-group analysis supported gender invariance. After establishing gender invariance, a considerable effect was detected between P.EDU and Taiwanese eighth grade science performance that was indirectly related through ATT and VAL with the direct effects being stronger than the indirect effects, and the indirect effects of ATT being stronger than VAL in both genders. Prospective impediments to multigroup invariance are suggested to be utilized when comparing different groups.

Development of Marine Science Affect Scale for Junior High School Students in Taiwan: Testing for Measurement Invariance

“…The values of skewness for the males and females ranged from 0.11 to 0.498 and 0.091 to 0.515, respectively, and the corresponding values of kurtosis ranged from −1.106 to −0.776 and −0.910 to −0.623. No items showed absolute values of skewness or kurtosis greater than the cutoffs of 3 or 8 recommend by Kline (2005) and Tsai et al (2015), respectively. This indicates that the values fell within acceptable ranges of normal distribution.…”

“…Confirmatory factor analysis (CFA) was also used in this study to evaluate construct validity. A series of multigroup CFA (MGCFA) analyses using Mplus 7 (Muthén & Muthén, 1998, based on the MLR (maximum likelihood estimation with robust standard errors) estimation procedure (Muthén & Muthén, 1998Tsai & Yang, 2012Tsai, Yang, & Chang, 2015) for conducting measurement invariance across gender, were also conducted.…”

“…If the goodness-of-fit of the CFA model fit well, then the construct validity and appropriateness of hypothetical latent variables were also established (Tsai et al, 2015). In the second step, multigroup analysis was conducted to test for gender measurement invariance.…”

“…The chi-square statistic indicates sensitivity to sample size (Bollen, 1989;Schermelleh-Engel, Moosbrugger, & Müller, 2003); therefore, other substitutive indices were used in this current study. According to recommendations in the literature (Beauducel & Wittmann, 2005;Fan & Sivo, 2005;Tsai et al, 2015), CFI, RMSEA, and SRMR can be used as indicators for model assessment. The chi-square statistic was also used to examine the results.…”

“…Assessing measurement invariance is valuable when developing a measurement or questionnaire, especially in cross-cultural or group investigations, because it allows the researcher to determine whether participants from different groups or cultures attribute the same meanings to questionnaire items (Chen, 2008;Cheung & Rensvold, 2002;Cheung & Rensvold, 2000;Milfont & Fischer, 2010;Tsai, Yang, & Chang, 2015;Tsai & Yang, 2012). Measurement invariance does not mean that all subjects are equal with respect to the variable in question, rather that they are equal with respect to the instrument in question (Tsai et al, 2015). Thus, measurement invariance is an important property developed to compare the mean level of a certain construct or trait among different groups, because interpretation of the mean differences may be problematic unless the underlying constructs are the same across groups (Wu & Yao, 2006).…”