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

Objectives Importance of good design Independent variable Confounding variables Generating and evaluating hypotheses

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 You may want to consider discussing/working through each phase of Figure 9.1 with the class (next two slides can help)

Figure 9.1, part 1

Figure 9.1, part 2

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

Between- vs. Within-Subjects IV Figure 9.2

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 Single-blind = participant does not know the hypothesis (reduces demand characteristics) Double-blind = data collector and participant both do not know the actual hypotheses Placebo/control group option helps to clearly identify the effects of the manipulated IV vs. ordinary effects/processes A manipulation check = test of whether manipulation is working as desired

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

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)

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 Consider recreating Table 9.2 on board – discuss alpha and beta and their relationships Type I error like convicting an innocent person Type II like setting a criminal free (fail to be sufficiently convinced that Ho is wrong and the criminal is really guilty as described by H1)

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) d formula is equation 9.1 Between-groups variance = difference among group means (true diffs between mu plus effect of random sampling or measurement errors). Can increase this by strengthening your IV manipulation and in ensuring the groups to be compared are as different as possible Within-groups variance = variability among scores within a group (natural variation and measurement error). Can be dealt with by using homogeneous samples and reliable tests

Figure 9.4 Highlight the influence of both between- and within-groups variance on effect size

What is Next? **instructor to provide details