The study by Carpenter, Crawford, and Walden assessed the differences between solo and team-taught experiences of a research course among the college students. The study aimed to assess the effectiveness of team teaching especially in research and statistics courses. The researchers collected both pre and post survey data based on student self-reported perceptions regarding their performances, comfort with the teaching approach, relationship between work and the course, and interest in the course materials.
The pre-survey data was collected from the students prior being exposed to the teaching and learning environment that adopted team teaching approach and the results compared with scores of college students that went through the same course but using a solo-taught approach (Carpenter et al. 2007). The study findings indicated that there were no differences in performance for the two groups, however, comfort and the perceptions of relationship between work and the course were statistically different between the groups.
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Why the T-test?
The authors used the Independent Sample T-test approach to identify if the scores reported by the students from each of the two groups (the solo-taught and team-taught groups) varied in terms of their means. In this case, the t-test approach is plausible as it evaluates if two means are statistically different from each other, and this is the best way to evaluate the effectiveness of the team-teaching approaches within a learning environment (Wilcox, 2017). Tentatively, if the mean scores are statistically different from each other it tells that the teaching approach has some influence on the teaching outcome; otherwise, it was not effective.
Suitability of the T-test technique
The Independent Sample T-test was the most suitable technique in this study. For instance, its choice is guided by the study goal, the type of samples, and the nature of data collected. In this case, the goal was to compare two independent groups in terms of the reported scores on several aspects related to research and statistics course. Therefore, this is the first aspect that makes the T-test qualify for this study – there are two independent groups (samples) that need to be compared (Koch, 2011).
Additionally, the data collected from the students on the perceptions on the various aspects of interests were continuous indicating that the means calculated could be measured from a ratio scale. Further, the observations from the students in each group were independent and this ensured that there was no relationship between the subjects in each of the two samples. On the other hand, the distribution of the scores was normally distributed as it was from a randomized sample subjects. According to Punch (2013) all these assumptions must be met to successfully use this parametric hypothesis testing technique – hence it was suitable.
Did the authors display the data?
The authors did not display the raw data from the field, rather they displayed the frequencies of computed data on all the variables of interest such as instructional type and degree program. Also, the authors presented the means and standard deviations for age and paid experience, as well as, the other constructs of interests for both pre and post surveys, T-test findings (T-values) for pair of constructs compared, and their respective P-values. In this case, it was easier to track the percentage change (for the means) for each of the variables assessing the teaching approach, identify the t-value observed, and p-value used to reject or accept the null hypothesis.
Do the results stand alone?
The study results are standalone indicating that they are plausible, meaningful, and easily understandable. First, the results have achieved the study goal – assessing the group mean differences in attempt of evaluating the efficacy of team teaching. Still, the results can be easily understood and evaluated for any violation of statistical hypothesis testing. There are no striking issues noted with violation of hypothesis testing assumptions and all the conclusions are consistently drawn from the study results.
References
Carpenter, D. M., Crawford, L., & Walden, R. (2007). Testing the efficacy of team teaching. Learning Environments Research , 10 (1), 53-65. DOI 10.1007/s10984-007-9019-y
Wilcox, R. R. (2017). Introduction to robust estimation and hypothesis testing . Amsterdam; Heidelberg: Elsevier, Academic Press.
Koch, K.-R. (2011). Parameter estimation and hypothesis testing in linear models . Berlin: Springer.
Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches . sage.
