This is just an overview and is not exhaustive! Exam Two: Study Guide This is just an overview and is not exhaustive!

Ch. 15) Parametric v. Non-parametric Scales Non-parametric level scales: Parametric level scales: Nominal level Scale: Interval level scale: Ordinal level scale: Ratio level scale:

Ch. 15) SPSS Windows: One Sample t Test Choose your TEST VARIABLE(S): “Income” Choose your TEST VALUE: “$55,000” Results: Mean = $60,700 P-value of the t-test = .519 There is no statistical difference between the mean of the sample and $55,000.

15) SPSS Windows: Two Independent Samples t Test Choose your TEST VARIABLE(S): “Attitude toward Nike” Choose your GROUPING VARIABLE: “Sex” Results: Means = 3.52 (Female) compared to 5.00 (Male). P-value of the t-test = .006 There is a statistical difference between men and women in regards to attitude towards Nike.

Ch. 15) SPSS Windows: Paired Samples t Test Results: Means = 4.35 (Awareness) compared to 4.31 (Attitude). P-value of the t-test = .808 There is no statistical difference between awareness of Nike and attitude towards Nike. Choose your first TEST VARIABLE: “Awareness of Nike” Choose your second TEST VARIABLE: “Attitude toward Nike”

Ch. 8) Importance of Bathing Soap Attributes Using a Constant Sum Scale Form Average Responses of Three Segments Attribute Segment I Segment II Segment III 1. Mildness 2. Lather 3. Lasting Power 4. Price 5. Fragrance 6. Packaging 7. Moisturizing 8. Cleaning Power Sum 8 2 4 17 3 9 7 53 19 5 20 13 60 15 100

Ch. 9) Itemized Rating Scales: Types Continuous rating scales (e.g. 0-100) Itemized rating scales are: Likert scale Example: Strongly disagree, Disagree, Neither agree nor disagree, Agree, Strongly agree Semantic differential scale Extremely bad, Bad, Neither bad nor good, Good, Extremely good Stapel scale +5, +4, +3, +2, +1, useful, -1, -2, -3, -4, -5  

Ch. 10) Questionnaire & Form Design What are/is: Double-barreled questions? Grids/matrices? Filter questions? The funnel approach? What do you do with sensitive info?

Ch. 11) Classification of Sampling Techniques Nonprobability Probability Convenience Sampling Judgmental Quota Snowball Systematic Stratified Cluster Simple Random

Ch. 13) Aspects of Field Work Single blind v. Double blind: Single-blind describes experiments where information that could skew the result is withheld from the participants, but the experimenter/researcher will be in full possession of the facts. Double-blind describes experiments where information that could skew the result is withheld from the participants and the experimenter/researcher. Watch out for: Confounding variables Observer bias

Ch. 14) Data Preparation What is/are: Imputation? Pairwise and casewise deletion? Outliers? Dummy variables? Variable respecification?

Ch. 17) Interpreting Correlation Correlations Age InternetUsage InternetShopping Pearson Correlation 1 -.740 -.622 Sig. (1-tailed)   .000 .002 N 20 .767

Ch. 7) The Different Classifications of Experimental Designs Pre-experimental One-Shot Case Study One Group Pretest-Posttest Static Group True Experimental Pretest-Posttest Control Group Posttest: Only Control Group Solomon Four-Group Quasi Experimental Time Series Multiple Time Series Statistical Randomized Blocks Factorial Design Experimental Designs