Craft of Research

1. Craft of Research Week 3: Literature Reviews and Methodologies

2. Literature Review Key Objectives • Gain and maintain readers’ attention • Demonstrate knowledge on topic • Set the context. What does the reader need to know about the topic. How much should you cover? • Claim the relevance of your chosen topic • Identify an area that needs to be addressed by research (gap in knowledge) • Introduce present research

3. BEFORE Writing a Literature Review • Consider the following questions: • Why is this problem important? • How does the study relate to previous work in the area? How will it build upon this work? • What are the primary and secondary hypotheses and objectives of the study? • How do the hypotheses and research design relate to one another? • What are the theoretical and practical implications of the study?

4. Pair and Share from your projects • Consider the following questions: • Why is this problem important? • How does the study relate to previous work in the area? How will it build upon this work? • What are the primary and secondary hypotheses and objectives of the study? • How do the hypotheses and research design relate to one another? • What are the theoretical and practical implications of the study?

5. Basic Organization of Literature Reviews • General Background (1 to 2 paragraphs) • Addressing Specific Area (3+ paragraphs) • Purpose of Current Research (1 to 2 paragraphs) Culminates to current research questions/aims/hypotheses

6. Literature Review Content • Establishing a Territory • Claiming centrality • General background • Reviewing previous research • Identifying a Niche • Indicating a gap • Highlighting a problem • Raising general questions • Proposing general hypotheses • Presenting justification • Addressing the Niche • Introducing/announcing present research descriptively or purposefully • Presenting research questions/hypotheses • Clarifying definitions • Summarizing methods • Announcing principal outcomes • Stating value of present research • Outlining structure of the paper

7. Example of Literature Table

8. https://thelogicofscience.com/2016/01/12/the-hierarchy-of-evidence-is-the-studys-design-robust/https://thelogicofscience.com/2016/01/12/the-hierarchy-of-evidence-is-the-studys-design-robust/

9. My strategies • What types of articles should you look for when starting a lit review? • Start with Review Articles and Meta-analyses • Original articles by the big names in the field. What is the historical context? • What studies are you building off of? When discussing give more detail. How to find articles… • What are the key words? Are there other words that mean the same thing also used? • Start with newer articles and find citations to dig deeper into.

10. Reading the results • p-values • t-tests • ANOVA’s • Post hoc tests and alpha inflation • Correlations • Regressions • Confidence Intervals • Effect Sizes • Power analysis • Not Going to cover but be aware of • Bayesian Statistics • Structural Equations • ICA’s and PCA’s • Dealing with missing data • ANCOVAs • Chi-squared tests (likelihood) • Partial correlations

11. t-tests and p-values • Measures whether or not something is different (Group1 vs Group2) can be used with 2 factor levels only. Differences are in relation to overall variability. • p-value: What is the likelihood that your findings are due to chance? (Scale from 0 to 1). • Arbitrarily set at 0.05 or a 1 in 20 chance. So if someone reports that their results are statistically significant with a p = 0.05 then they are saying…

12. ANOVA – Analysis of variance • Measures whether or not something is different and can be used with 2 or more factor levels. Differences are in relation to overall variability. • Within Subjects factors, Between subjects factors, Mixed design (2x3) • Main and Interaction Effects • Post-Hoc Analysis

13. Post-hoc analysis and alpha inflation • Post-hoc analysis are only done with ANOVAs to tell when there is a main effect with 3 or more levels of analysis, or when there is an interaction effect. • Alpha inflation 1- (1-0.05)^# of tests… So ten tests means alpha now equals 0.4. • Bonferonni correction: alpha/# of tests.

14. Figure 2 A shows the results for letter height. A significant interaction effect was revealed for size x pace, and pace x group. No other interaction effects were revealed. For the pace x group interaction effect, post hoc analysis revealed that across both sizes, persons with PD decreased letter height at the fast pace, while HOAs did not. What is driving this the 1cm size or the 2 cm size for PD individuals? How many comparisons did I make? What should my alpha level be now? Which should not be displayed as significant. (Bonferonni correction: alpha/# of tests.)

15. Correlations • Pearson’s r: ranges from -1 to 1. • Measures covariance of two variables divided by their product of their standard deviations. • Degree to which a pair of variables are linearly related • Does not imply causation

17. Regressions: How well does one variable predict the other? • Is essentially a test of correlation. R^2 how much variance is explained by the predictor variable? How well does it predict? • Can use multiple variables… How do we determine what variables are the most important for predictions? • Linear Regressions (scalar variables), Logistic Regression (Categorical variables, See chi-square tests).

18. Table 2: Linear Regression. Factors associated with time spent exercising (minutes/week).

19. Table 3: Logistic regression showing factors that predict those who meet exercise recommendations (>150 per week).

20. Effect Sizes • Cohen’s d (effect size of mean differences ANOVA, t-tests) = difference in means divided by the pooled standard deviation. • Small = 0.3, medium =0.5, large = 0.7 • Pearsons r • Small = 0.1, medium = 0.3, large = 0.5 • Hedges g (meta analysis) – takes into account sample size from each study • Same as Cohen’s d • Effect size calculator (https://www.uccs.edu/lbecker/)

21. Power Analysis • How big of a sample size do you need to detect an effect of a given size with a given degree of confidence • Power or beta is usually set to 80% or 0.8. (Again this is an arbitrary number). • There are many different calculators out there. • GPower

22. Danger of type II error: n=10

23. Power Analysis: Repeated Measures ANOVA • Β= 0.8, α = 0.05, • r and Cohen’s d will be estimated using the results from other studies for optimistic estimate. Conservative estimate using a smaller r and Cohen’s d.

24. Power Analysis: Multiple Regression • Computed with Power and Precision: Power (1- β) = 0.8, α = 0.05 • No studies have examined the relationship between working memory or auditory habituation and UPDRS motor scores. • The smallest covariate r found for individual measures in UPDRS is r = 0.6 (overall motor score: r = 0.89). • Optimistic: (covariate r = 0.6, predictor r =0.5) suggests that we need 16 participants. • Conservative: (covariate r = 0.5predictor r =0.3) suggests that we need 60 participants.

25. Confidence Intervals • A range of values that you can be certain contains the true mean of the population. • Large sample sizes will give you smaller confidence intervals.

26. Reading the results • P-values • T-tests • ANOVA’s • Post hoc tests and alpha inflation • Correlations • Regressions • Confidence Intervals • Effect Sizes • Power analysis • Not Going to cover but be aware of • Bayesian Statistics • Structural Equations • ICA’s and PCA’s • Dealing with missing data • ANCOVAs • Chi-squared tests (likelihood) • Partial correlations