SAGE Journal Articles
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Classroom demonstrations help students better understand challenging concepts. This article introduces an activity that demonstrates the basic concepts involved in analysis of variance (ANOVA). Students who physically participated in the activity had a better understanding of ANOVA concepts (i.e., higher scores on an exam question answered 2 months after the classroom demonstration) than did students who simply observed the activity.
Questions to Consider
1. How did the authors demonstrate the concepts of a single factor with multiple levels? Did this represent the concept well?
Cognitive Domain: Comprehension
Difficulty Level: Easy–Medium
2. What way did the authors demonstrate within-group variability? Did the students appear to understand this? Do you believe it demonstrated the main points well?
Cognitive Domain: Comprehension
Difficulty Level: Medium
3. Explain how the authors demonstrated the concept of the F ratio. Was this accurately depicted? Were the main points made well?
Cognitive Domain: Comprehension
Difficulty Level: Medium
Observation-oriented modeling is a novel approach toward conceptualizing and analyzing data. Compared with traditional parametric statistics, observation-oriented modeling is more intuitive, relatively free of assumptions and encourages researchers to stay close to their data. Rather than estimating abstract population parameters, the overarching goal of the analysis is to identify and explain distinct patterns within the observations. Selected data from a recent study by Craig et al. were analyzed using observation-oriented modeling; this analysis was contrasted with a traditional repeated measures ANOVA assessment. Various pitfalls in traditional parametric analyses were avoided when using observation-oriented modeling, including the presence of outliers and missing data. The differences between observation-oriented modeling and various parametric and nonparametric statistical methods were finally discussed.
Questions to Consider
1. What are some of the strengths of this approach toward conceptualizing and analyzing data?
Cognitive Domain: Comprehension, Analysis
Difficulty Level: Medium
2. Summarize the process of conceptualizing the data in this approach. How does it differ from the ANOVA?
Cognitive Domain: Analysis
Difficulty Level: Medium–Hard
3. Explain and discuss some of the differences between observation-oriented modeling and various parametric and nonparametric statistical methods.
Cognitive Domain: Comprehension, Analysis
Difficulty Level: Medium–Hard
This study proposes robust means modeling (RMM) approaches for hypothesis testing of mean differences for between-subjects designs in order to control the biasing effects of nonnormality and variance inequality. Drawing from structural equation modeling, the RMM approaches make no assumption of variance homogeneity and employ robust estimation/rescaling strategies in order to alleviate reliance on normality. A Monte Carlo simulation is conducted to compare the type I error rate and the power of the proposed six RMM test statistics to five ANOVA-based statistics, the latter of which have also employed trimmed means and Winsorized variances to enhance robustness. Various simulation factors manipulated include variance inequality, sample-size pairings with group variances, degree of nonnormality, alpha level for hypothesis tests, and effect size. Results show that the proposed RMM methods are indeed superior to the traditional ANOVA-based methods.
Questions to Consider
1. Compare and contrast the RMM approaches for hypothesis testing to the traditional ANOVA-based methods.
Cognitive Domain: Analysis, Comprehension
Difficulty Level: Hard
2. What was used to compare the type I error rate, and how well were the comparisons made?
Cognitive Domain: Comprehension
Difficulty Level: Medium
3. How did the RMM test statistics compare regarding empirical power estimates to those from ANOVA-based methods? Explain.
Cognitive Domain: Comprehension, Analysis
Difficulty Level: Hard
The purpose of this study was to investigate job satisfaction among hospice interdisciplinary team members, which included social workers, nurses and other professionals (that is, home health aides and spiritual care providers). Interdisciplinary team members (N = 76) from four hospices in the Midwest participated in this study. One-way ANOVA revealed that significant differences in satisfaction resulted in the areas of distributive justice, autonomy, and opportunity between social workers, nurses and other interdisciplinary team members.
Questions to Consider
1. Why did the authors conduct one-way ANOVAs instead of a series of t-tests?
Learning Objective: Design with more than two groups
Cognitive Domain: Knowledge
Difficulty Level: Medium
2. The eta-squared for autonomy differences between job titles is 0.18. This be considered a(n): (a) small effect, (b) medium effect, (c) large effect, (d) inappropriate statistic.
Learning Objective: Effect size
Cognitive Domain: Comprehension
Difficulty Level: Easy
3. After conducting the F tests the authors discuss the means between different groups; however, to be sure that these differences were significant they should have conducted: (a) post-hoc tests, (b) power analysis, (c) confidence intervals, (d) additional ANOVAs.
Learning Objective: Post-hoc comparisons
Cognitive Domain: Comprehension
Difficulty Level: Easy
Objective: This study examined to what extent differences exist in pre-college characteristics and academic performance between Black male student-athletes and their student-athlete peers.
Method: Data provided by the Florida Department of Education’s PK-20 Education Data Warehouse (EDW) were analyzed as a function of group membership (gender and race), using descriptive analysis, cross-tabulations, and a one-way ANOVA. The sample included 513 cases, with White females comprising 36.3% of the sample, White males 24.3%, Black females 15.5%, and Black males 14.3%. Student-athletes’ academic performance was operationalized using four continuous variables (grade point average, course credit hours enrolled, course credit hours earned and credit hours enrolled/earned ratio) and one dichotomous variable (degree completion).
Results: Findings suggest that Black males earned 72% of the credit hours they attempted, which was less than all other examined groups. Within Black males, differences between socio-economic groups were also found. Individuals identified as high socio-economic status (SES) earned approximately 82% of credit hours enrolled, compared with those identified as low SES, which earned 67% of credit hours attempted. Between-group differences were also found when examining college readiness and percentage of degrees completed.
Contributions: This study contributes to the extant literature on student-athletes at community and 2-year colleges by providing insight into the potential impact individual characteristics have on academic performance outcomes for Black male student-athletes. The author also provides thoughtful consideration concerning how institutions and policy changes can positively affect these outcomes.
Questions to Consider
1. The authors report F(3, 509) = 5.776. What do these degrees of freedom mean? How were they calculated?
Learning Objective: Degrees of freedom
Cognitive Domain: Knowledge
Difficulty Level: Hard
2. Which type of post-hoc test did the authors use to follow up their analyses? (a) Tukey’s, (b) LSD, (c) Scheffe, (d) Bonferroni.
Learning Objective: Post hoc comparisons
Cognitive Domain: Application
Difficulty Level: Easy
3. If the authors conducted a one-way ANOVA with all of the groups presented in Table 1, the degrees of freedom would be? (a) 3, (b) 4, (c) 15, (d) 16.
Learning Objective: Design
Cognitive Domain: Analysis
Difficulty Level: Medium