GSSR Research Methodology and Methods of Social Inquiry socialinquiry.wordpress

1. GSSR Research Methodology and Methods of Social Inquiry www.socialinquiry.wordpress.com November 15, 2011 Representation of Units of Analysis. Sampling

2. Population & Sample Population: everybody whom we want to generalize to. - no strict rules to follow; the researcher must rely on logic and judgment. The population is defined in keeping with the objectives of the study. Census study: data are gathered on every member of the population. Parameters: If we measure the entire population & calculate a value (e.g. mean, st. deviation), we do not refer to this as a statistic; parameter of the population Problems: time-consuming; expensive.

3. Sampling • .

4. Sampling The sample should reflect the characteristics of the population from which it is drawn. Sampling Frame: the listing of the (reachable) population from which we draw the sample. Sample: the group of people who we select to be in our study. Statistic: when we look across responses for entire sample, we use a statistic • nonrespondents; dropouts. Sampling methods: method by which units of observation are selected random (probability) or non-random sampling

5. Random Sampling Basic requirements of a random sample: Every case has: 1) the same chance of being selected 2) the chance of selection does not change (i.e. constant probability). The case that is selected (theoretically) is put back in the pot = “sampling with replacement”. - allows calculating the probability that we have represented the population well; • automatically eliminates selection bias in large N studies; • allows to estimate sampling error

6. Basic Terms N = number of cases in the sampling frame n = number of cases in the sample NCn = number of combinations (subsets) of n from N f = n/N = the sampling fraction Simple random sampling Stratified random sampling (proportional/quota random sampling) Systematic random sampling Cluster (Area) random sampling Random route sampling Multi-stage random sampling See www.socialresearchmethods.net/kb/sampprob.php

7. Nonrandom Samples - selected cases donot have an equal chance of selection (some people have a greater, but unknown, chance than others to be selected)

8. 1. Convenience sampling 2. Purposive sampling - likely to overweight subgroups in the population that are more readily accessible. Modal Instance Sampling - sampling the most frequent case (i.e. "typical" case) Expert Sampling - sample of persons with known/demonstrable experience & expertise in some area: "panel of experts;" - often, used in combination with modal sampling, as means of validation of the former.

9. Quota Sampling: non-random selection of people according to some fixed quota. a. Proportional quota sampling: want to represent the major characteristics of the population by sampling a proportional amount of each. Ex: Population has 40% women, 60% men. We want N = 100. Problem: decide the specific characteristics on which to base the quota. b. Non-proportional quota sampling: specify the minimum no. of sampled units we want in each category. Goal: have enough cases to assure that you will be able to talk about even small groups in the population. - non-probabilistic analogue of stratified random sampling (i.e. used to assure that smaller groups are adequately represented in the sample).

10. Heterogeneity (diversity) Sampling • want to include all opinions or views, without concern about representing these views proportionately. Ex: brainstorming (including concept mapping)

11. Snowball Sampling • begin by identifying someone who meets the criteria for inclusion in your study; then ask them to recommend others who they may know who also meet the criteria; Respondent Driven Sampling (RDS) • combines "snowball sampling" with a mathematical model that weights the sample to compensate for the fact that the sample was collected in a non-random way. Heckathorn, Douglas D. 1997." Respondent-Driven Sampling: A New Approach to the Study of Hidden Populations." Social Problems. http://www.respondentdrivensampling.org/

12. Selection Bias • distortion of analysis, resulting from data collection method; • can occur at various(multiple) points in the research design Selecting on the DV • allow for the possibility of at least some variation on DV! If we do not take into account other instances when DV takes other values, we can learn nothing about the causes of the DV. King et al. discussion of Porter’s (1990) cross-national work on competitive advantage for contemporary industries and firms (see p. 133-134 in King et al. 1994) Historical records as data source: History differentially selects what it ‘keeps’ according to a set of rules that are not always clear from the record.

13. Target population --|> Frame population: Coverage error • Frame population --|> Selected sample: Sampling error • Selected sample --|> Collected sample: Non-response error Coverage error & non-response error as the most serious errors in both qualitative and quantitative research

14. Indeterminate Research Designs Research design: plan that shows, through the discussion of the causal model (theoretical) & the data, how we expect to make inferences. A research design should not be indeterminate! 1. More Inferences than Observations (units of observations/cases) Rule: One fact (observation) cannot give independent information about more than another fact.

15. Ex: a study with 1 observation (units), and 2 causal (IV) variables, cannot determine which, if any of the hypotheses is correct. Y = expected value of the DV Regression equation: Y = X1*B1 + X2*B2 + e Prediction equation E(Y) = X1*B1 + X2*B2 Where E(Y) = expected value of Y; here E(Y) = 35. In practice, we never know this, because of the randomness inherent in Y (i.e. because of e)

16. E(Y) = X1*B1 + X2*B2 35 = 3* B1 + 5*B2 This equation has no unique solution. B1 = 10, B2 =1; B1 = -10, B2 = 13

17. DV: successful joint collaboration on capital-defense projects (high-tech weapon system)King et al. Pp. 119-122