Sampling (conclusion) & Experimental Research Design Readings: Baxter and Babbie, 2004, Chapters 7 & 9

IssuesIssues in Non-probability sampling IssuesIssues in Non-probability sampling n Bias? n Is the sample representative? n Types of sampling problems: u Alpha: find a trend in the sample that does not exist in the population u Beta: do not find a trend in the sample that exists in the population

Principles of Probability Sampling n each member of the population an equal chance of being chosen within specified parameters n Advantages u ideal for statistical purposes n Disadvantages u hard to achieve in practice u requires an accurate list (sampling frame or operational definition) of the whole population u expensive

Types of Probability Sampling n 1. Simple Random Sample u With replacement u Without replacement: link link n 2. Systematic Sample (every “n”th person) With Random Start Systematic Sample Systematic Sample u Urban studies example) rban studies example)rban studies example) n 3. Stratified Sampling: u Sampling Disproportionately and Weighting n 4. Cluster Sampling

Examples of sampling issues & techniques n Survey about football (soccer) market (soccer) n Rural poverty project and sampling issues projectsamplingprojectsampling

Postpone: Techniques for Assessing Probability SamplingProbability Postpone: Techniques for Assessing Probability SamplingProbability We will discuss these in connection with Chapter 11 material: n Standard deviation n Sampling error n Sampling distribution n Central limit theorem n Confidence intervals (margin of error)

Introduction to Experimental Design n Recall discussion of experiments in lecture on Research Ethics u Milgram experiment (on obedience) Milgram u Stanford prison experiment about how prisons as institutions communicate roles and shape actions (still photo from video on right showing research subjects dressed as prison guard & prisoners) Stanford prison experiment Stanford prison experiment

Trends in Experimental Social Research n types of subjects & reporting style (naming vs. anonymity) n deception & risk n debriefing

Neuman (2000: 239) Single & double Blind Experiments

Key Notions / Terms n Treatment, stimulus, manipulation (independent variable) n observable outcome (dependent variable) n Experimental Group n Control group n pretest (measurement before treatment) n posttest (measurement after treatment)

Neuman (2000: 226) Random Assignment

Neuman (2000: 226) Comparison with Random Sampling

Neuman (2000: 227) How to Randomly Assign

Experimental Design Notation n O= observation n X= treatment n R= random assignment

Some Common Types of Design

Three common types of experimental design: Classical pretest-post test –design Three common types of experimental design: Classical pretest-post test –design n Total population randomly divided into two samples; u control sample u experimental sample. n Only the experimental sample is exposed to the manipulated variable. n compares pretest results with the post test results for both samples. n divergence between the two samples is assumed to be a result of the experiment.

Solomon four group design – n The population is randomly divided into four samples. n Two of the groups are experimental samples. n Two groups experience no experimental manipulation of variables. n Two groups receive a pretest and a post test. n Two groups receive only a post test. n improvement over the classical design because it controls for the effect of the pretest.

Factorial designFactorial design – Factorial designFactorial design – n similar to a classical design except additional samples are used. n Each group is exposed to a different experimental manipulation.

Factorial Design

Validity Issues n internal validity: elimination of plausible alternative explanations n external validity: ability to generalize (outside the experiment)

Internal Validity Threats n selection bias: groups not equivalent n history: unrelated event affects exp. n maturation: separate process causes effects n testing: ex. Pretest effects

More Internal Validity Threats n instrumentation: measure changes n mortality/attrition n statistical regression : ex. Violent films n contamination n compensatory behaviour n experimenter expectancy

External Validity Threats n realism n reactivity: u Hawthorne effect u novelty effect u placebo effect

Laboratory vs. Field experiments n lab.- more control, higher internal validity n field- more natural, higher external validity

Recall : New Ethical Norms n protection of subjects n debates about deception