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Objectives

Slides to accompany Weathington, Cunningham & Pittenger (2010), Chapter 9: A Model for Research Design. Objectives. Importance of good design Independent variable Dependent variable Confounding variables Generating and evaluating hypotheses. Importance of Good Design.

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Objectives

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  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

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