1. SURVEY RESEARCH

2.  Purposes and general principles Survey research as a general approach for collecting descriptive data Surveys as data collection methods used in conjunction with other types of designs (e.g., experimental research) General purposes of conducting surveys Defining your objective/s

3.  Key elements of effective survey research design Designing your survey instrument/s: content and format considerations Evaluating your survey instrument/s: pilot testing and refinement Collecting your data: sampling and response rate considerations

4.  What are the key goals of the survey?  What kinds of information will you require to address the questions that you have posed?  What will you make of the responses – hypothetical scenarios

5.  Deriving questions based on theory and research questions  Asking questions that permit induction and allow for assessment of sampling biases  Trade-offs between comprehensiveness and response rates Establishing a conceptual framework

6. Deciding on question stem content, wording, and format  Operationalizing your terms  Reducing stem length through use of alternative formats  Language use and complexity  Use of open-ended versus closed-ended response formats

7.  Categorical  Ordinal  Interval and ratio  Which Level to Choose? methods of analysis sophistication of techniques level of information reactions of respondents reliability post-hoc conversions Deciding on response formats

8.  Matching item stems and response formats  Likert scales Matching item stems and response options Number of response options Weighting of items Response anchors and category intervals  Semantic differential scales Choice of adjectives

9. Conducting pilot studies  Panel sessions  Send-out pilots  Cognitive process interviews  Structuring pilot studies: seeking information about – Overall format Clarity and understandings Time taken to complete Potential sources of bias

10. Determining your population/s and target sample/s  Relationship with research questions  Checking relevance of samples  Probability-based sampling Aimed to ensure that individuals have the same chance of being included in the sample Simple random sampling procedures (e.g., random number generation) Stratified random sampling (e.g., identify subpopulations of interest)  Non-probability-based sampling Convenience sampling (grabbing anyone willing) Snowball sampling (ask respondents to nominate others who might be willing to participate)

11. Determining minimal numbers of responses required  Statistical power considerations: Effect size Variability Tests used Sample size  Rules of thumb for determining sample adequacy

12.  Anonymity versus confidentiality  Strategies for maximising response rates: it really depends on the target sample Length Convenience Recompense Format Rationale Recruitment Wording  Special considerations in using Web-based questionnaires  Explicating possible sources of bias in reporting survey results

13. Causal-Comparative Designs

14. Key steps in designing causal- comparative research  Identifying a research problem  Selecting target and comparison groups to study  Collecting data on targeted independent and dependent variables AND relevant background variables  Analysing and interpreting differences

15. Selecting a relevant research problem  Some examples of questions suitable for causal-comparative research: Do children with ADHD have difficulties in recognizing facial expressions representing different emotions? Do females have difficulties in reading road maps? Do people with a science background prefer quantitative over qualitative research methods?

16. Defining your groups  Strategies for controlling the threat of differential selection: Matching on characteristics of relevance Using statistical techniques such as analysis of covariance to “equalize” on pre-existing differences Eliminating cases with specific characteristics Analysing subgroups

17. Collecting and interpreting data  Collecting data on target independent and dependent variables  Collecting data on relevant background characteristics: What kinds of alternative hypotheses might people put forward for your results? What is it important to rule out in interpreting the results?  Considering plausible alternatives in your interpretation