1 / 14

Research Design Strategies: Enhancing Quality and Validity

Explore the significance of good research design in eliminating biases, understanding variables, and generating hypotheses for robust outcomes. Learn about independent and dependent variables, confounding factors, and hypothesis evaluation.

fitzgerald-gardner
Télécharger la présentation

Research Design Strategies: Enhancing Quality and Validity

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Slides to accompany Weathington, Cunningham & Pittenger (2010), Chapter 9: A Model for Research Design

  2. Objectives • Importance of good design • Independent variable • Dependent variable • Confounding variables • Generating and evaluating hypotheses

  3. Importance of Good Design • Good science requires good research design • Important to understand and try to prevent common sources of bias at each phase of the research process • Figure 9.1 • Especially beware of demand characteristics and self-fulfilling prophecies

  4. Independent Variable Issues • Subject vs. manipulated • Age vs. amount of caffeine • Between- vs. within-subjects • Testing participant in one condition vs. across multiple conditions (or over time) • Fig. 9.2

  5. Between- vs. Within-Subjects IV

  6. Dependent Variable Issues • Seek reliable and valid DVs • Reduce bias when possible • Single- and double-blind procedures • Consider use of placebo or control group • Cover stories and deception may help strengthen your manipulation • Should follow-up with a manipulation check

  7. Confounding Variables • We need to carefully consider possible sources of confusion • Confounding variables • Common example is a carryover effect • Previous experiences influencing future behavior/responses to stimuli

  8. Hypothesis Details • Hypotheses state relationships between/ among variables (Table 9.1) • Directional vs. nondirectional • As mathematical statements • Null and alternative/research versions are necessary • H0 true until evidence suggests false (innocent until shown guilty)

  9. Errors in Hypothesis Testing • Hypothesis testing relies on probabilities and is conditional • Trying to achieve a certain degree of confidence that your data do not support Ho • Type I error: Ho is true, but we reject it • False alarm • Type II error: Ho is false, but we retain it • Miss

  10. Evaluating Hypotheses • Is the evidence strong enough to reject Ho? • Can consider effect size (d) as relative difference between two M • Larger ES = stronger IVDV relationship • Influenced by between- and within-groups variance (Fig. 9.4)

  11. Figure 9.4

  12. What is Next? • **instructor to provide details

More Related