1. Thinking Like a Psychologist Part III

2. Evaluating the Research Each research study makes its own contribution More importantly, how does each study relate to the others and to the theory being tested? Connectivity = old + new –A good adaptation to a theory not only explains the new findings but accounts for previous findings

3. Evaluating the Research Don’t jump to conclusions!!! –Media is notorious for doing this Previous findings are not to be discarded because of one new finding. –Replication, replication, replication –Evaluate the methods used Is someone trying to say correlation = causation?

4. Evaluating the Research Converging Evidence –Multiple research studies reveal similar findings. Convergent Validity –Measures that are predicted to be related, are. Diverging Evidence –Research that is contrary to the typical findings. Divergent Validity –Measures that are predicted to be unrelated, aren’t.

5. Evaluating the Research When designing research: –Read the available literature –Examine the shortcomings (addressed and unaddressed) –Review researcher recommendations –Note the variables examined Is there another angle to examine? –Note the predominate findings and explanations

6. Evaluating the Research Meta-analysis –Compilation of multiple research findings from multiple studies examining the same research question Find all relevant articles Evaluate the articles for inclusion Code the data to be included Examine the outcomes

7. Evaluating the Research Events and phenomena typically do not have one cause. Although a research study may only examine one or two variables, that does not mean those the only ones involved. –Multiplicity of Causation –Interaction Effects

8. Evaluating the Research Sample size is important in evaluating the results –n = 25 vs. n = 300 –A smaller sample size is not as representative of the population –Increasing sample size and number of samples, decreases error.

10. Samples and Populations Central Limit Theorem –As sample size increases, the distribution approaches normal –Mean of the sample means is equivalent to the population mean –Standard deviation of the sample means is the standard error

11. Population X µ = 15 Sample1 M = 5 Sample 3 M = 12 Sample 6 M = 22 Sample 5 M = 10 Sample 2 M = 13 Sample 7 M = 16 Sample 8 M = 19 Sample 4 M = 9 The math: 9 + 5 + 12 + 22 + 19 + 16 + 13 + 10 =106 106/8 = 13.25

12. Central Tendency Mean, Median, Mode –Mean All of the scores summed and divided by the number of scores (M = Σx/n); a.k.a. the average –Median The values separating the upper half of the scores from the lower half. –Mode The score that occurs most frequently