Becoming a Behavioral Science Researcher: A Guide to Producing Research That Matters

By Rex Bryan Kline. 2008

295 citations recorded by the source corpus.

Part 1. Promise and Problems. Introduction. Not Ready for Prime Time. What Students Say They Need. Plan of the Book. Career Paths for Behavioral Scientists. Summary. Recommended Readings. The Good, the Bad, and (the Really) Ugly of Behavioral Science Research. The Good. The Bad. The (Really) Ugly. Why? Summary. Recommended Readings. Part 2. Concepts. The Research Trinity. Trinity Overview. Design. Measurement. Analysis. Internal Validity. Construct Validity. Conclusion Validity. External Validity and Sampling. Summary. Recommended Readings. Exercises. Design and Analysis. Chapter Overview. From Question to Design. Experimental Designs. Controlled Quasi-Experimental Designs. Nonequivalent-Group Designs. Regression-Discontinuity Designs. Other Quasi-Experimental Designs. Nonexperimental Designs. Resources for Learning More. Summary. Recommended Readings. Exercises. The Truth about Statistics. Study Strategy. A Dozen Truths about Statistics. What Statistical Significance Really Means. Misinterpretations of Statistical Significance. Why Are There So Many Myths? Other Drawbacks of Statistical Tests. In Defense of Statistical Tests. Recommendations for Changing Times. Summary. Recommended Readings. Exercises. Appendix 5.1. Review of Statistics Fundamentals. Effect Size Estimation. Contexts for Estimating Effect Size. Families of Parametric Effect Sizes. Estimating Effect Size When Comparing Two Samples. Measures of Association for More Complex Designs. Effect Sizes for Dichotomous Outcomes. T-Shirt Effect Sizes, Importance, and Cautions. Approximate Confidence Intervals. Research Examples. Summary. Recommended Readings. Exercises. Appendix 6.1. Noncentrality Interval Estimation for Effect Sizes. Measurement. Chapter Scope. The Critical Yet Underappreciated Role of Measurement. Measurement Process Overview. Resources for Finding Measures. Adapting or Translating Tests. Evaluation of Score Reliability and Validity. Checklist for Evaluating Measures. Recent Developments in Test Theory. Summary. Recommended Readings. Exercises. Part 3. Skills. Practical Data Analysis. Vision First, Then Simplicity. Managing Complex Analyses (Batch to the Future). Data Screening. Summary. Recommended Readings. Exercises. Writing. Plagiarism and Academic Integrity. Writing as Learning. Getting Started. The Role of Style Guides. General Principles of Good Writing. Principles of Good Scientific Writing. Writing Sections of Empirical Studies. Effective Graphical Displays. Ready for the Big Time. Summary. Recommended Readings. Appendix 9.1. Example Results Section. Presentations. Challenges of Oral Presentations. Problems with PowerPoint Presentations. Principles for Creating Effective PowerPoint Presentations. Lessons from Multimedia Learning. Other PowerPoint Issues. Poster Presentations. Summary. Recommended Readings. Exercises. Appendix 10.1. Example Slides, Handout, and Poster. Suggested Answers to Exercises.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/9959cf7140f88621ac755dcf882aa3a3e30cd7da

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In Management Science and Operations Research

Citations in the corpus

Detecting and Deterring Insufficient Effort Responding to Surveys (2012, background): “It is widely recommended that researchers screen the data to identify and correct these inaccurate observations before their use in modeling and hypothesis testing procedures (Babbie 2001; Hartwig and Dearing 1979; Kline 2009; Smith et al. 1986; Tukey 1977; Wilkinson and the Task Force on Statistica”
Applied Psychometrics: The Steps of Scale Development and Standardization Process (2018, background): “However, constructs in psychology are not directly observable (Kline, 2009; Sawilowsky, 2007; Milfont & Fisher, 2010 among many others), thus developers have first to define a general philosophical foundation to connect the construct to a set of observable traits or behaviors (Price, 2017).”
How Big Is “Big”? Interpreting Effect Sizes in L2 Research (2014, result): “For example, in interpreting the effects of her meta-analysis of the effects of extensive reading, Wang (2010) compared her findings to those of Krashen’s (2007) meta-analysis of the same topic.”
Jamovi: An Easy to Use Statistical Software for the Social Scientists (2019, methodology): “When comparing the means of more than two groups, one-way ANOVA is used (Kline, 2009).”
Meta-analysis in Second Language Research: Choices and Challenges (2010, background): “and (e) whether size and patterns of the effects actually found in meta-analyses conform with theoretically expected predictions (Henson, 2006; Kirk, 1996; Kline, 2009).”
Longitudinal associations of student–teacher relationships and behavioural and emotional strengths on academic achievement (2017, methodology): “Different informants (i.e. students, parents and teachers) were used for different domains (i.e. relationships, strengths and academic achievement, respectively) in order to avoid mono-method bias (see, e.g. Kline, 2009).”
Effects of Inquiry-Based Science Instruction on Science Achievement and Interest in Science: Evidence from Qatar (2012, background): “Large samples may cause statistical significance even though effect sizes are low (Kline, 2009).”
Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects (2021, background): “Unfortunately, doing so is not common (Kline, 2019, pp. 65–66; Simonsohn, 2018).”
Reporting and Interpreting Effect Size in Quantitative Agricultural Education Research. (2011, background): “Hedges’s g and Glass’s delta are also commonly referenced measures to estimate effect size for independent t–tests (Kline, 2009; Rosenthal & Rosnow, 1991).”
Student engagement scale: development, reliability and validity1 (2015, methodology): “In addition, when the histogram P-P and Q-Q graphics for EFA and CFA were examined (Huck 2012; Kline 2009; Pallant 2007), it was seen that the distributions were normal.”