This lecture introduces qualitative research and qualitative analysis, overviews the lab report tasks, and summarises Lectures 1 to 7. See also http://ucspace.canberra.edu.au/pages/viewpage.action?pageId=57409703

1. Lecture 7 Survey Research & Design in Psychology James Neill, 2011 Qualitative Research Lab Report Overview Review of Lectures 1 to 7

5. Quantitatively

7. Neill (2009). Qualitative analysis [online article]

8. Taylor-Powell & Renner (2003). Analzying qualitative data. [online article] Readings

9. What is qualitative research? " All research ultimately has a qualitative grounding " - Donald Campbell

12. Pictures (e.g., video), or

13. Objects (e.g., an artifact)

15. Often recommended during earlier phases of research.

16. Researcher may only know roughly in advance what he/she is looking for.

17. Design may emerge as the study unfolds.

18. Researcher becomes subjectively immersed in measuring and interpreting the subject matter.

19. Less objective and generalisable than quantitative.

21. Participant observation

22. Focus groups

23. Interviews

24. Surveys

27. useful for complex issues

28. richer data collected

29. can be less biased than close-ended

30. useful for instrument development and validation Survey research: Close vs. open-ended questions

31. Qualitative data analysis "[Qualitative] data analysis is the process of bringing order, structure and meaning to the mass of collected data. It is a messy, ambiguous, time-consuming, creative, and fascinating process. It does not proceed in a linear fashion; it is not neat. Qualitative data analysis is a search for general statements about relationships among categories of data." Marshall and Rossman (1990, p. 111)

33. analyse frequencies and percentages

35. Read data

36. Develop themes (patterns)

37. Code

38. Analyse & illustrate themes and relationships

40. use illustrative quotes

42. Generating initial codes

43. Searching for themes

44. Reviewing themes

45. Defining and naming themes

46. Producing the report

47. Generating initial codes (Braun & Clarke, 2006) Figure 1. Data extract, with codes applied (from Clarke et al., 2006) (from Braun & Clarke (2006, p. 88)) Decisions about initial coding should based on the research question being addressed.

48. Initial thematic map (Braun & Clarke, 2006) Figure 2. Initial thematic map, showing five main themes (final analysis presented in Braun and Wilkinson, 2003) (from Braun & Clarke (2006, p. 90))

49. Developed thematic map (Braun & Clarke, 2006) Figure 3. Developed thematic map, showing three main themes (final analysis presented in Braun and Wilkinson, 2003) (from Braun & Clarke (2006, p. 90))

50. Final thematic map (Braun & Clarke, 2006) Figure 4. Final thematic map, showing final two main themes (see Braun and Wilkinson, 2003) (from Braun & Clarke (2006, p. 91))

53. Themes have not been generated from a few vivid examples (an anecdotal approach), but instead the coding process has been thorough, inclusive and comprehensive.

54. All relevant extracts for all each theme have been collated.

55. Themes have been checked against each other and back to the original data set.

56. Themes are internally coherent, consistent, and distinctive.

58. Analysis and data match each other / the extracts illustrate the analytic claims.

59. Analysis tells a convincing and well-organized story about the data and topic.

60. A good balance between analytic narrative and illustrative extracts is provided.

63. There is a good fit between what you claim you do, and what you show you have done / ie, described method and reported analysis are consistent.

64. The language and concepts used in the report are consistent with the epistemological position of the analysis.

65. The researcher is positioned as active in the research process; themes do not just ‘emerge’. to analyse any data

67. Questions used as themes

68. Weak or unconvincing analysis

69. Mismatch between data & themes

70. Mismatch between theory & themes

72. Tends to be descriptive (limited use for hypothesis testing)

73. Analyses across cases (not within)

74. Can be labour intensive

75. Subject to researcher bias

76. Can have limited generalisability

77. Difficult to establish reliability & validity Note : Many of the disadvantages depend more on poorly conducted analyses or inappropriate research questions than the method itself.

79. Results are generally accessible to educated general public.

80. Useful method for working within participatory research paradigm, with participants as collaborators

82. Can highlight similarities & differences across the data set.

83. Can generate unanticipated insights .

84. Allows for social as well as psychological interpretions of data.

85. Can be useful for producing qualitative analyses suited to informing policy development .

88. Clerical/administrative

89. Skilled manual

90. Unskilled manual

91. Unemployed/other

93. Protective

94. Understanding

95. Values

98. Read through all responses

99. Develop list of common categories

100. Try not to have too many categories - look to combine similar categories

101. To increase reliability and validity , at least 2 researchers develop coding separately, then work together on final coding, code separately, compare responses and analyse inter-rater reliability

104. 14 response themes were identified. 4 new variables were created (resp1, resp2, resp3, resp4) and codes 1 to 14 were used to code each case's responses.

105. Content analysis: Multiple response analysis

107. Up to two written responses were allowed (resp1 and resp2).

108. The qualitative responses were coded into four categories: Parents, Friends, Career, Avoid Work. Content analysis: Multiple response analysis

109. Content analysis: Multiple response analysis

110. Content analysis: Multiple response analysis

111. Content analysis: Multiple response analysis

113. The next two most popular reasons were “avoid work” and “parents” cited by 25% and 24% of respondents respectively. “Career” was cited by 16% of respondents. Content analysis: Multiple response analysis

115. Explain rationale for choice of analysis method

116. Explain development of the thematic categories

117. Explain coding

119. Theme names and descriptions, possibly with illustrative quotes

120. Inferential statistics and effect sizes (if appropriate)

122. Trustworthiness / rigour of the qualitative findings.

123. Consistency between qualitative and quantitative findings.

124. Contributions of findings to theory and practice .

126. Code then analyse the data , seeking to identify patterns as answers to the research question(s) .

127. Describe your decision-making to provide a rigorous ‘audit-trail’

128. Student questions ?

129. Lab report guidelines & marking criteria

133. EFA (15%)

134. MLR (10%)

135. Advanced ANOVA (10%) Discussion ( 30% ) Present at least one of each of these 4 types of analyses.

136. Overall research question(s) Your lab report should be guided by at least one central, clearly stated research question : e.g., To what extent do university students' time management skills account for their degree of satisfaction with university teaching and learning ?

138. Create at least one hypothesis or research question for each major analysis.

140. To what extent do self-reported time management skills predict students' satisfaction with university education and teaching? (MLR – RQ)

142. those who are coping with university are more satisfied

143. there is no interaction between gender and coping. (Factorial ANOVA - Hypothesis)

146. Satisfaction – ? factors

147. Time management - ? factors

149. Include internal consistency, creation composite scores, and correlation between factors. Descriptive stats for factors can be covered later, e.g., in the ANOVA.

151. Include correlations, R 2 and regression coefficients

153. Mixed ANOVA

154. ANCOVA

155. MANOVA

157. Student questions ?

158. Review of Lectures 1 to 7

161. Designing good questions

162. Response formats

164. Properties of the normal distribution

167. Types and purposes

169. Composite scores

170. Factor correlations What we've covered

172. Thematic analysis vs. content analysis What we've covered

174. ANOVA (W10-11)

175. Effect sizes and power (W12)

176. Review (W14)

177. Lab report due (W15) What's next?

179. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology , 3 (2), 77-101. doi:10.1191/1478088706qp063oa

180. Braun, V. and Wilkinson, S. 2003: Liability or asset? Women talk about the vagina. Psychology of Women Section Review , 5 , 28-42.

181. Francis, G. (2007). Introduction to SPSS for Windows: v. 15.0 and 14.0 with Notes for Studentware (5th ed.). Sydney: Pearson Education.

182. Howell, D. C. (2007). Statistical methods for psychology (6th ed.). Belmont, CA: Wadsworth.

183. Marshall, C. & Rossman, G. B (1989). Designing qualitative research . Newbury Park, CA: Sage.

184. Neill, J. T. (2009). Qualitative analysis . (Wilderdom) http://wilderdom.com/courses/surveyresearch/assessment/labreport/QualitativeAnalysis.html

185. Taylor-Powell, E. & Renner, M. (2003). Analyzing qualitative data . University of Wisconsin-Extension. http://learningstore.uwex.edu/assets/pdfs/G3658-12.PDF

187. Free and open source software.

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