8210 wk 7 discussion
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LEILA RESPONSE 1
Analysis of variance (ANOVA) is inferential statistics that tests the differences between mean scores of two or more groups across one or more than one variable (Wagner, 2020). ANOVA can be used to test for statistical significance and is based on the comparison of variance between groups (Wagner, 2020). ANOVA requires assumptions about the level of measurement, the shape of the population distribution, and the homogeneity of variance (Frankfort-Nachmias et al., 2020).
Summary of the Article
Within Reflective practices in micro teaching from the perspective of preservice teachers in Turkey, Erdemir & Yeşilçınar (2021) aimed to explore practices among teacher feedback, peer feedback and self-reflection that could help preservice teachers (PSTs) improve their practice skills in classroom. The study lasted 13 weeks with 48 PSTs and suggested improving the classroom context by increasing time on peer feedback instruction, alleviating anxiety and social barriers among PSTs, and raising peer awareness (Erdemir & Yeşilçınar, 2021). Therefore, the the study is expected to provide insights to teacher trainers on how to model reflective practices in micro teaching (Erdemir & Yeşilçınar, 2021).
Research Design
The research design used by the authors was a sequential explanatory mixed-methods research design and for the quantitative data descriptive statistics, repeated measures ANOVA and Bonferroni post-hoc comparisons were done through SPSS 21.0 (Erdemir & Yeşilçınar, 2021). The authors used one-way ANOVA because PSTs recognized the teacher as the main source of feedback and their preferences for the feedback types were compared by Repeated Measures ANOVA (Erdemir & Yeşilçınar, 2021). I think it’s the most appropriate choice because a meaningful relationship was found among the three types of feedback which were teacher, peer, and self-reflection (F(2,72) = 26.14; p < .05; η2 = .21) (Erdemir & Yeşilçınar, 2021).
Diplaying the Data and Results
The authors displayed the data through the use of tables for the given descriptive statistics of each measure. Findings underlined the presence of a difference among the perception levels of PSTs towards reflective practices (Erdemir & Yeşilçınar, 2021). The results do not stand alone because the study is limited by the lack of information of PSTs’ knowledge of peer feedback (Erdemir & Yeşilçınar, 2021). Therefore, in future studies require researchers to ensure that PSTs know how peer feedback is given (Erdemir & Yeşilçınar, 2021).
Effect Size and Meaningfulness
The authors reported the effect size was a significant result because it explained the 21% of the total variance indicating the obtained effect was large (Erdemir & Yeşilçınar, 2021). It was meaningful because the findings contribute in many ways to how reflective practices were considered through the eyes of PSTs and provide a basis for developing a model about the design of reflective practices (Erdemir & Yeşilçınar, 2021). The model needs to be in accordance with the perceptions of PSTs and the integration of the three reflective practices into micro teaching (Erdemir & Yeşilçınar, 2021).
Conclusion
It is unclear whether PSTs were hesitant to provide critical feedback due to their social drawback as the participants in the study claimed, or did not know how to evaluate their peers and give effective feedback (Erdemir & Yeşilçınar, 2021). PSTs preferred teacher feedback over peer feedback and valued self-reflection above peer feedback (Erdemir & Yeşilçınar, 2021). Thus, the study suggests that peer feedback should be improved by allocating increased time on instruction and enhancing practical knowledge in preservice teacher education (Erdemir & Yeşilçınar, 2021).
References
Erdemir, N., & Yeşilçınar, S. (2021). Reflective practices in micro teaching from the perspective of preservice teachers: teacher feedback, peer feedback and self-reflection. Reflective Practice, 22(6), 766–781. https://doi.org/10.1080/14623943.2021.1968818
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Sage Publications.
RESPONSE 2
Kristin Domville
RE: Discussion - Week 7
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Critique of Article: Research Design
In the article, Unhealthy Behavior Clustering and Mental Health Status in United States College Students, the researchers used a Latent Class Analysis (LCA) research method to examine the relationship between risk behaviors clusters with mental health status among US college students (Jao et al., 2019). An LCA is a type of multivariate statistical design. A multivariate analysis is used to calculate relationships between multiple outcome variables. Latent class analysis analyzes a population distribution by clustering or dividing the population (latent class) into categories that cannot be observed directly (Jao et al., 2019). The researchers refer to clusters that include risk behaviors such as alcohol binge drinking, cigarette/marijuana use, insufficient physical activity, and fruit/vegetable consumption. Class membership and mental health include mental health diagnoses, psychological symptoms, and self-injurious thoughts/behaviors (Jao et al., 2019).
ANOVA and Displays
The authors used an ANOVA to analyze the variance between the continuous demographic data (IE., Age) and the classes (the cluster of health risk behavior). They also used ANOVAs to analyze for statistically significant relationships between clusters of health risks and overall mental health. For example: mental health and class (cluster) membership (Frankfort-Nachmias et al., 2020; Jao et al., 2019).
Completing an ANOVA test was the most appropriate choice. The authors had access to a large data set (N=105,781) which was available from a National College Health Assessment. The researchers were able to develop research questions and analyze the available data in various ways. By clustering the health risk behaviors, the authors completed ANOVAs, which accounted for the sample size (N=105,781). The ANOVA was used to determine if there was a significant (p<.001) relationship between the demographics and the clusters. The authors used two tables to display the p-value relationship and the difference (p<.001) between the clusters and overall mental health. The article's data were shown in Table 2 and Table 3, displaying the data analyzed through the ANOVA tests. There were no pie charts, line graphs, or histograms.
Effect Size and Meaningfulness
The results of the ANOVA’s do not stand alone. The ANOVA’s are one analysis within a larger statistical design. For example, researchers used a model fit indices for one to five latent classes to determine the clusters before using the ANOVAs (Jao et al., 2019). After running the ANOVAs, the researchers ran post hoc analysis using Bonferroni correction, which reduces the chance of type I errors, and Mean Square Contingency Coefficient, which analyzes if the data sets are dependent or independent of each other (Armstrong, 2014). The ANOVAs were used within a larger research design for the analysis of appropriate data that related to research questions.
The researchers reported on effect size and displayed the data alongside the F values in two tables. This is meaningful since the researchers included effect size when developing the research design. By including effect size, they reduced the likelihood of a type II error. In this case, there was no relationship between the demographic data and the clusters of health risk behaviors or between the clusters and the overall mental health of the college students. The final results indicated that students who participated in multiple health risk behaviors (clusters) were more likely to report having decreased mental health (Jao et al., 2019). This is meaningful data for leaders and educators in higher education. Students with mental health issues can present in various ways, with many mental health diagnoses. Professors cannot be mental health practitioners, but they can be aware of how mental health impacts student learning and participation in daily life activities. Individuals who hold higher educational leadership positions should advocate for providing appropriate support for students and professors to assist academic achievement for students with mental health diagnoses adequately.
References
Armstrong, R. (2014) When to use the Bonferoni correction. Ophthalmic and Physiological Optics, 34(5), 502-508. https://doi.org/10.1111/opo.12131
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.
Jao, N. C., Robinson, L. D., Kelly, P. J., Ciecierski, C. C., & Hitsman, B. (2019). Unhealthy Behavior Clustering and Mental Health Status in United States College Students. Journal of American College Health, 67(8), 790–800. https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1234552&site=eds-live&scope=site
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