SAGE Journal Articles
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The focus of this article is on testing the equality of treatment means in a one-way analysis of variance setting with count response. The count data is further subject to misclassification, resulting in undercount of the event. Four asymptotically optimal tests are developed after correcting for the underreporting of the count. A simulation study is conducted to compare how these tests compete with each other with respect to level and power. The impact of ignoring undercount on the level of the tests is also investigated through this study. A comparison of these tests with the normal theory-based inference is also done after appropriate transformation of count data to a continuous measurement.
Questions to Consider
1. Summarize the article and explain how the count data is further subject to misclassification. What are the consequences on research outcomes when there is an undercount of an event?
Cognitive Domain: Comprehension
Difficulty Level: Medium–Hard
2. Compare and contrast these tests with the normal theory-based inference methods.
Cognitive Domain: Analysis
Difficulty Level: Medium–Hard
3. What were the outcomes as these tests were compared with each other with respect to level and power?
Cognitive Domain: Analysis
Difficulty Level: Medium–Hard
This study proposes an approach for determining appropriate sample size for Welch’s F test when unequal variances are expected. Given a certain maximum deviation in population means and using the quantile of F and t distributions, there is no need to specify a noncentrality parameter and it is easy to estimate the approximate sample size needed for heterogeneous one-way ANOVA. The theoretical results are validated by a comparison to the results from a Monte Carlo simulation. Simulation results for the empirical power indicate that the sample size needed by the proposed formulas can almost always achieve the desired power level when Welch’s F test is applied to data that are conditionally nonnormal and heterogeneous. Two illustrative examples of the use of the proposed procedure are given to calculate balanced and optimal sample sizes, respectively. Moreover, three sample size tables for two-, four-, and six-group problems are provided, respectively, for practitioners.
Questions to Consider
1. Summarize the author’s approach for determining appropriate sample size for Welch’s F test when unequal variances are expected.
Cognitive Domain: Comprehension
Difficulty Level: Medium
2. What do the simulation results for the empirical power indicate regarding sample size needed and the desired power level, specifically when Welch’s F test is applied to data that are conditionally nonnormal and heterogeneous?
Cognitive Domain: Knowledge, Analysis
Difficulty Level: Hard
3. When the variances are unknown or possibly unequal, what do the authors propose as a method to bring about better results?
Cognitive Domain: Analysis
Difficulty Level: Hard
The authors assess the suitability of repeated-measures regression (RMR) to analyze multilevel data in four popular multilevel research designs by comparing results of RMR analyses to results of analyses using techniques known to produce correct results in these designs. The findings indicate that RMR may be suitable for only a small number of situations and that repeated-measures ANOVA, multivariate repeated-measures ANOVA, and multilevel modeling may be better suited to analyze multilevel data under most circumstances. The authors conclude by offering recommendations regarding the appropriateness of the different techniques given the different research designs.
Questions to Consider
1. Compare and contrast the RMR with the repeated-measures ANOVA, and discuss the outcomes of this study. On what do the authors base their recommendations?
Cognitive Domain: Analysis
Difficulty Level: Hard
2. Summarize the differences between the repeated-measures ANOVA, multivariate repeated-measures ANOVA, and multilevel modeling. How do these compare to the RMR?
Cognitive Domain: Knowledge, Analysis
Difficulty Level: Hard
3. In what circumstances is the RMR preferred over ANOVA?
Cognitive Domain: Comprehension
Difficulty Level: Medium–Hard
As local governments across the United States adapt to economic shifts, workforce reshaping, and continued demand for services, training to confront these challenges has become more important. However, training resources are limited, investment in these programs is not always prioritized, and evaluating outcomes is difficult. This study analyzes data from a local government leadership development program to examine training impacts over time. It focuses on leadership skills and the ways in which individual’s self-assessments change over time. The findings indicate that although leadership training is an important factor in the development of both conceptual and interpersonal leadership skills, the long-term effects of training on these two types of skills vary significantly. Understanding the training effect decay associated with leadership skills development can help human resource managers and public organizations strategically plan, evaluate, and invest in these training activities to better prepare their workforce to meet future challenges.
Questions to Consider
1. Why did the authors have to use a dependent sample t-test instead of an independent sample t-test? What are the advantages of this approach?
Learning Objective: One variable with two related groups
Cognitive Domain: Evaluation
Difficulty Level: Easy
2. Interpret the results of the dependent sample t-tests for conceptual leadership and interpersonal leadership. (a) Conceptual leadership significantly increased. (b) Interpersonal leadership significantly increased. (c) Neither conceptual nor interpersonal leadership significantly increased. (d) Both conceptual and interpersonal leadership significantly increased.
Learning Objective: Significance testing
Cognitive Domain: Synthesis
Difficulty Level: Easy
3. Table 4 shows that seven different groups received training. If the authors wanted to compare scores across groups they could conduct: (a) one-way ANOVA, (b) independent t-tests, (c) dependent t-tests, (d) two-way chi-square.
Learning Objective: Experimental design
Cognitive Domain: Analysis
Difficulty Level: Medium
Objective: Attention Deficit Hyperactive Disorder is a childhood-onset psychiatric condition that often continues into adulthood. Stimulant medications are the mainstay of treatment; however, additional approaches are frequently desired. In recent years, mindfulness meditation has been proposed to improve attention, reduce stress, and improve mood. This study tests the feasibility of an 8-week mindfulness training program for adults and adolescents with ADHD.
Method: Twenty-four adults and eight adolescents with ADHD enrolled in a feasibility study of an 8-week mindfulness training program.
Results: The majority of participants completed the training and reported high satisfaction with the training. Pre–post improvements in self-reported ADHD symptoms and test performance on tasks measuring attention and cognitive inhibition were noted. Improvements in anxiety and depressive symptoms were also observed.
Conclusion: Mindfulness training is a feasible intervention in a subset of ADHD adults and adolescents and may improve behavioral and neurocognitive impairments. A controlled clinical study is warranted (J. of Att. Dis. 2008; 11(6) 737–746).
Questions to Consider
1. What were the significant effects observed this study? Interpret these effects in context of the pre–post design of the study.
Learning Objective: One variable with two related groups
Cognitive Domain: Comprehension
Difficulty Level: Medium
2. Using your text-book power analysis, the sample sizes represented in Table 2 are best suited for detecting: (a) null effects, (b) small effects, (c) medium effects, (d) large effects.
Learning Objective: Power analysis
Cognitive Domain: Analysis
Difficulty Level: Medium
3. If the researchers thought the effects of their intervention would only result in a small effect size, how many people should they have tried to recruit for their study? (a) 23. (b) 200. (c) 34. (d) 16.
Learning Objective: Power analysis
Cognitive Domain: Evaluation
Difficulty Level: Easy