The experiment employs a Latin square design and a specified statistical model to evaluate measures and test hypotheses. Analysis of variance (ANOVA) decomposes total variability into components such as overall mean, blocks, treatment, and random error. ANOVA tests whether group means are equal under a null hypothesis that all treatments have identical effects; rejection indicates differing treatment effects. The experiment compares Pair and Solo programming, with blocks for program and tool support. Statistical analysis includes model assumptions, ANOVA, treatment comparisons, effect size, and power analysis. Threats to conclusion, internal, construct, and external validity are addressed, and duration and further work are considered.
Once we have the measures, we are able to test the hypotheses through statistical inferences. The statistical model associated with a Latin square design is shown in equation (1). This design uses analysis of variance (ANOVA) to assess the components (overall mean, blocks, treatment and random error) of the model. ANOVA is based on looking at the total variability of the collected measures and the variability partition according to different components.
ANOVA provides a statistical test of whether or not the means of several groups are all equal. The null hypothesis is that all groups are simply random samples of the same population. This implies that all treatments have the same effect (perhaps none). Rejecting the null hypothesis implies that different treatments result in altered effects. In this experiment, we have two groups of means (Pair and Solo programming), which are blocked by program and tool support.
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