This document provides an overview and summary of a lecture on survey research and design in psychology. It discusses key topics like the research process, survey design, data analysis, reliability and validity, sampling, and reporting results. Assessment involves a lab report on survey-based research and a final exam testing knowledge of research methods and statistical analysis techniques.

1. Lecture 12 Survey Research & Design in Psychology James Neill, 2011 Survey Research & Design in Psychology: Review & Summary

3. Research process

4. Survey design

5. MLR, ANOVA, ES & Power

8. Unit aims & outcomes

11. Survey design

12. Sampling

13. Interpreting & communicating results

15. Correlations

16. Factor analysis

17. Qualitative analysis

18. Reliability

19. MLR

20. Advanced ANOVA

21. Research process

22. Research process 1. Establish need for info/ research 2. Problem definition/ Hypotheses 3. Research design 4. Sampling/ Data collection 5. Data analysis 6. Reporting

23. Funnel model

24. Survey design

26. Question types (objective/subjective, open/closed)

27. Response formats (e.g., dichotomous, frequency, Likert, multiple response, idiographic)

28. Levels of measurement (LOM)

29. Reliability & validity

30. Sampling (probability/non-probability)

31. Modes of administration (self-report (f2f, mail, web), interview (f2f/phone))

32. Items should measure different aspects of the latent (underlying) construct Latent Construct Measured Construct

33. Latent Construct Poor items will create ‘brown sludge’ (noisy (unreliable) measure) Measured Construct

34. Describing data

37. Dispersion

38. Skewness

39. Kurtosis

43. Correlations

45. Building block for FA & MLR

46. Non-parametric correlations (e.g., Point bi-serial, Phi/Cramer's V )

48. Non-linearity

50. Factor analysis

53. Sample size (min. 5 cases: 1 variable)

55. Scree plot

56. Theoretical structure

57. Interpretation of factor loadings

60. Primary loadings > .5?

61. Cross-loadings < .3?

62. Diff. btw primary & cross-loadings > .2?

63. Sufficient items per factor

65. Compare model across groups

67. > .7 = good

69. Regression-weighting

70. Qualitative analysis

72. Theory testing; Data reduction (synthesise meaning)

74. Qualitative (Thematic analysis)

75. Inferential statistical decision making tree

77. No. of IVs and DVs

79. See Inferential statistics decision-making tree

80. Multiple linear regression

82. F (significance of R )

83. Change in R 2 (Hierarchical)

86. Linearity

87. Homoscedasticity

88. Multicollinearity

89. Multivariate outliers

91. Hierarchical

92. Forward

93. Backward

94. Stepwise

97. Size of each predictor (β - standardised)

98. When IVs are correlated, interpret zero-order vs. partial correlations

100. ANOVA

104. IVs must be categorical

105. Significant results may indicate need for followup or post-hoc tests

107. 1-way repeated measures ANOVA

108. 2-way factorial ANOVA

109. Mixed design ANOVA (Split-plot ANOVA)

110. ANCOVA

111. MANOVA

113. Normally distributed DVs ( for each level of the IVs )

114. Homogeneity of variance (b/w subjects)

117. Power, effect size, significance testing, publication bias & academic integrity

119. Sig. test results are influenced by power esp. if particularly high or low.

122. η 2 , η p 2 , d

123. Publication bias & academic integrity Publication bias (low power; favouritism of sig. findings; funnel plots) Academic integrity - “Integrity is doing the right thing, especially when no one is watching”.

124. Lab report

126. Use example HD lab report and sample write-ups as guides

127. Demonstrate your knowledge via independent thinking/work

128. Tell a story

130. Title Page

131. Abstract

132. Body

133. References

134. Appendices

136. No waffle – cut to the chase – only review literature or argument relevant to the RQs / hypotheses

137. State clear RQs and/or hypotheses – One per test/analysis/effect

139. Present relevant details efficiently (avoid getting bogged down in extraneous detail)

141. Materials or Instrumentation

144. Consider LOM assumptions

145. Caution in use of overall scores Overall Score not valid Overall Score valid 1 3 2 1 3 2

147. Varimax/Oblimin?

148. 2-6 factors?

149. 5-30 items removed?

151. Correlations between factors

152. Internal consistency for each factor

158. Effect sizes ( η p 2 , d )

160. Qualitative or quantitative approach?

161. Table of main themes (names, description, example quotes, frequency/%)

162. Bar graph?

163. Thick description?

164. Sub-samples?

166. Draw conclusions about the RQs & hypotheses in light of the results.

167. Discuss key strengths & limitations of the study. ( Balanced criticism )

168. Draw out implications and recommendations

170. Methods : How could the research design (e.g., instrumentation) be improved?

171. Practice : Implications for students and universities e.g., for improving satisfaction?

173. Use for content which would break the flow , but which is relevant to understanding the study e.g., the EFA correlation matrix.

175. Use headings (e.g., Appendix A, B, C etc.) and possibly titles e.g., Appendix A: Bivariate correlations amongst the university student satisfaction items

177. Submit ONE DOCUMENT containing the coversheet, lab report and appendices

178. NO EXTRA ATTACHMENTS

179. Upload via the Moodle drop-box

180. You can re-submit up until the due date (after that, resubmission will incur late penalties)

182. Keep an eye on your student email and Moodle Announcements .

183. Final exam

185. Style, structure and content will be similar to the quizzes

186. ~ 120 multi-choice questions in 180 mins (plenty of time).

187. Practice exams are available

190. Evaluation & feedback

192. UC takes these results very seriously – ~40% response rate

194. Tutorials?

195. Textbooks?

196. Assessment?

197. Website(s)?

198. Software – SPSS?

199. Workload?

201. Free and open source software.

202. http://www.openoffice.org/product/impress.html