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

2. 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:

3. 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.

4. 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.

5. 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”

6. 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

7. Ch. 9) Itemized Rating Scales: Types
Continuous rating scales (e.g )
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

8. 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?

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

10. 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

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

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

13. 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