Bad to good : achieving high quality and impact in your research / edited by Arch G. Woodside.
Uloženo v:
| Vydáno: |
Bingley :
Emerald,
2016.
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| On-line přístup: | |
| Další autoři: | |
| Témata: | |
| Médium: | Elektronický zdroj E-kniha |
Obsah:
- Front Cover; Bad to Good: Achieving High Quality and Impact in Your Research; Copyright Page; Contents; List of Contributors; Dedication; Preface; Chapter 1 Moving away from Bad Practices in Research toward Constructing Useful Theory and Doing Useful Research; Introduction; A Profile of Bad Practices Appearing in Most Journal Manuscript Submissions; Bad Practice: Theory and Analysis Mismatch; Embracing the Complexity Turn; Bad Practice: Testing for Fit Validity Only; Not Testing for Predictive Validity; Bad Practice: Ignoring Cases with Associations Contrary to Significant Main Effects; Bad Practice: Reporting Findings Using t, p, F, r, and R2Bad Practice: Focusing on Net Effects in Regression Models; Bad Practices: Relying on Verbal Self-Reports Only and Using Five- or Seven-Point Scale Responses to Measure Variables in Mental Processes; Bad Practice: Not Studying Behavior Dynamically; Doing Only Cross-Sectional Survey Studies; Bad Practice: Interviewing One Person per Group (Firm, Household, or Organization); Bad Practice: Useable Response Rates Less Than 50% and Measuring Nonresponse Bias; Bad Practice: Symmetric (Variable) Only Modeling; Bad Practice: Using a Void-Treatment Control Group in Experiments Not Using a Placebo Control Group; Bad Practice: Doing Laboratory Experiments Only; Not Doing Field Experiments; Bad Practice: Use of Mushy (Soft, Squishy) Questions to Measure Thinking and Behavior; Failure to Collect/Report Real-Life Contextual Data; Bad Practice: The Study of One Dependent/Outcome Variable at a Time; Bad Practice: Advocacy Hypothesis Construction and Testing; Bad Practice: Stepwise Regression Analysis; Bad Practice: Failure to Plan to Include a Replication in the Study or to Invite Other Scholars to Attempt to Replicate FindingsBad Practice: Including Non-Significant Terms in Regression Models; Bad Practice: Using Median Splits; Conclusion; References; Chapter 2 Embrace Complexity Theory, Perform Contrarian Case Analysis, and Model Multiple Realities; Introduction: Beyond Rote Applications of Regression Analysis; Complexity Theory Tenets; A Simple Antecedent Condition May Be Necessary But a Simple Antecedent Condition is Rarely Sufficient for Predicting a High or Low Score in an Outcome ConditionA Complex Antecedent Condition of Two or More Simple Conditions Is Sufficient for a Consistently High Score in an Outcome Condition _ The Recipe Principle; A Model That Is Sufficient Is Not Necessary for an Outcome Having a High Score to Occur
- The Equifinality Principle; Recipes Indicating a Second Outcome (e.g., Rejection) Are Unique and Not the Mirror Opposites of Recipes of a Different Outcome (E.G., Acceptance)
- The Causal Asymmetry Principle
