Science Education News (SEN) Journal 2018 Science Education News Volume 67 Number 1 | Page 28

ARTICLES

and longitudinal differences at the 95 % confidence interval using ESCI software( Cummings, 2011). Each cohort was then examined for data normality, with box plot outliers being deleted from the cohort.

The analytical methods employed were used to examine the contribution of the trial science exam( independent variable) to the SC science exam( dependent variable) in cross sectional and longitudinal comparisons. This relationship is a proxy measure of the influence that the different lesson plans had upon the memory formed of science during classroom learning( the trial science exam) and its subsequent recall and application during the SC science exam. Bivariate and stepwise multivariate regressions using SPSS 17 were used to examine these relationships( Pallant, 2010), with the multivariate regressions being used to explore the contribution of additional contributors( reading, writing, numeracy and literacy scores) and isolate the contribution of each significant contributor to the SC science exam in the Postconventional and Postmeta cohorts. All regressions had settings of two-tailed a =. 05, the prediction interval for the mean was set at 95 %, missing multivariate data was excluded listwise and the stepping probability criteria for data entry was F =. 05 and F =. 10 for data removal. Regression and residual casewise diagnostics for outliers was set at 2 standard deviations. The data coherency of all regressions was validated using split regressions( Snee, 1977) using the SPSS random number generator with a fixed value of 552804. The f 2 effect sizes of multivariate contributors were determined using ClinTools( Devilly, 2007) and then converted to their Cohen’ s d effect size equivalent. Differences in the correlation coefficients and the d effect sizes were examined at the 95 % confidence interval using ESCI software, with the achieved power of the difference in bivariate slopes determined using G * Power( v. 3.1.3)( Faul, Erdfelder, Buchner, & Lang, 2009).

generate normality.

Figure 1 Difference in means of trial and SC science exam scores.

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Figure2 Difference in means of multivariate scores.

RESULTS

Descriptive data

Generally, the mean exam scores for all cohorts did not differ at the 95 % confidence interval, indicating that science classes with comparable student characteristics were identified for inclusion into each cohort( see figures 1 and 2). The single difference detected was in the mean SC science exam score for the Preconventional vs. Postmeta comparison, suggesting that learning characteristics of these two cohorts were significantly different. However, as their mean trial science exam score was comparable, as were the reading, writing, numeracy and literacy scores of the Postconventional vs. Postmeta comparison, this one difference in descriptive data is minor given the overall trend of the descriptive data being equal. This equality of means was broadly extended to the normality of the data for regression analysis and the residuals of the regressions. The single exception was the removal of three data sets from the Postconventional cohort to

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