2. Scales and Indices

3. While trying to capture the complexity of a phenomenon We try to seek multiple indicators, regardless of the methodology we use: Qualitative Qualitative : we prepare a sequence of questions and then ask more questions that help us clarify the issue of investigation Quantitative Quantitative: we construct several questionnaire items that help identify the concept

4. Composite Measures In quantitative research are the Sequence of items that Target the same issue Within the same questionnaire To achieve a fuller representation of the concept under investigation

5. Index Babbie (2004, p. 152): A type of composite measure that summarizes and rank-orders several specific observations and represents some more general dimensions * In other words: * In other words: it combines several distinct indicators of a construct into a single score  generally is a sum of scores of such indicators

6. Index Example: a) Example: a) your first exam contained 67 objective multiple-choice questions. The number of correct answers you received is the index of your understanding of the subject. b) b) your first project in this class has a checklist of issues to be addressed while you are working on it. The number of checkmarks you make on it once completing the project is your index of how ready it is for submission.

7. Index Neuman ( 2000, p. 177 ): “Base your answers on your thoughts regarding the following four occupations: long-distance truck driver, medical doctor, accountant, telephone operator. Score each answer 1 for yes and 0 for no: 1. Does it pay a good salary? 2. Is the job secure from lay-offs 3. Is the work interesting and challenging? 4. Are its working conditions good? 5. Are there opportunities for career advancement? 6. Is it prestigious or looked up to by others? 7. Does it permit freedom in decision-making?”

8. Index Construction : Establish the face validity : - Do your items pertain to the population? - Are your items general or specific? - Do the items provide enough variance? Examine bivariate relationships (logical consistency between all items) Examine multivariate relationships (correspondence between one group of items measuring the same thing and another group of items measuring the same thing)

9. Index Scoring What is your measurement range? Is there an adequate number of cases for each index point? Is there a need to assign weights to items? * * If unweighted, each of your items has the same value for the concept, so  sum up * * Weighting changes the theoretical definition of the construct, as some items matter more than others

10. Scale Babbie (2004, p. 152): A type of composite measure composed of several items that have a logical or empirical structure among them * In other words: * In other words: allows to measure the intensity or direction of a construct by aligning the responses on a continuum

11. Scale Exist in a variety of types Five most widely known are: Likert scale - Likert scale - Bogardus Social Distance scale - Thurstone scale - Guttman scale - Semantic Differential scale

12. Likert Scale Neuman (2000, p. 183)

13. Semantic Differential Scale Babbie (2004, p. 171)

14. Bogardus Social Distance Scale This social distance scale was taken from http://garnet.acns.fsu.edu/~jreynold/bogardus.pdf

15. Guttman Scale Neuman (2002, p. 191)

16. Thurstone Scale Neuman (2000, p. 187)

17. Scale Scoring Response frequencies could be used to identify the intensity (direction, potency, etc.) of a construct index Often, if several scales are used to identify a construct, the responses are summed and averaged in order to receive an index.

18. Validation Validation Internal validation: * Item analysis: * Item analysis: An assessment of whether each of the items included in a composite measure makes an independent contribution or merely duplicates the contribution of other items in the measure ( Babbie, 2004, p. 164 ) Is conducted through a variety of statistical techniques: - Regression - Factor Analysis

19. Validation Validation External validation: * * The process of testing the validity of a measure by examining its relationship to other presumed indicators of the same variables ( Babbie, 2004, p. 165 ) Is conducted by - trying it on a population with apparent traits - statistical procedures of establishing concurrent and predictive validity (often simple correlations)

20. Bad Index vs. Bad Validators Fails the Internal Validation: Item analysis can show presence of inconsistent relationships between the items Item analysis can show that the contribution of an item is insufficient The overall model is not supported by the data you collected * either or * Generally means that you need to either go back and re-think your theory or look for more relationships between the items in your model

21. Bad Index vs. Bad Validators Fails the External Validation: The index does not adequately measure the variable in question The validation items do not adequately measure the variable  thus, do not provide a sufficient testing power * * Generally means that you need to go back and re- examine you measure before blaming it on the validators

22. Missing Data not a good idea Try to guess from previous responses what value to insert ( not a good idea ) creates threats to validity Substitute the average score for cases where data are present ( creates threats to validity ) reduces the size of the usable data Eliminate all cases for which any information is missing ( reduces the size of the usable data )

23. Sampling

24. Non Probability Non Probability Do not know the size of the population from which the sample was drawn. Therefore, do not know how representative are their responses, controlling for their social-demographic characteristics.

25. Non Probability Non Probability Purposive Snowball Quota Selected Informants

26. Probability Probability Do know the size of the population from which the sample was drawn. Do know how representative are their responses, controlling for their social- demographic characteristics.

27. Probability Probability Simple Random Systematic Stratified Multistage Probability Proportionate to Size Disproportionate with Weighting

28. Probability Probability Bias: Effect of theoretically relevant characteristics on responses. Population, Study Population, Sampling Frame, Sampling Unit Sampling Error