Exam Two:
Study Guide
This is just an overview and is not
exhaustive!
Ch. 15) Parametric v. Non-parametric Scales
Non-parametric level scales:
– Nominal level Scale:
– Ordinal level scale:
– Parametric level scales:
– Interval level scale:
– Ratio level
scale:
Ch. 15) SPSS Windows: One Sample t Test
1.
Choose your TEST VARIABLE(S): “Income”
2.
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
1.
2.
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
1.
2.
Results:
•
•
•
Choose your first
TEST VARIABLE:
“Awareness of Nike”
Choose your second
TEST VARIABLE:
“Attitude toward Nike”
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.
Ch. 8) Importance of Bathing Soap Attributes
Using a Constant Sum Scale
Form
Average Responses of Three Segments
Attribute
1. Mildness
2. Lather
3. Lasting Power
4. Price
5. Fragrance
6. Packaging
7. Moisturizing
8. Cleaning Power
Sum
Segment I
Segment II
Segment III
8
2
4
2
3
53
9
7
5
13
100
4
9
17
0
5
3
60
100
17
7
9
19
9
20
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
• Example:
Extremely bad, Bad, Neither bad nor good, Good,
Extremely good
– Stapel scale
• Example:
+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
Sampling Techniques
Probability
Nonprobability
Sampling Techniques Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Simple Random
Sampling
Systematic
Sampling
Quota
Sampling
Stratified
Sampling
Snowball
Sampling
Cluster
Sampling
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
Age
Pearson
Correlation
Sig. (1tailed)
N
InternetUsage
Pearson
Correlation
Sig. (1tailed)
N
InternetShopping Pearson
Correlation
Sig. (1tailed)
N
InternetShoppi
InternetUsage
ng
1
-.740
-.622
20
-.740
.000
.002
20
1
20
.767
.000
.000
20
-.622
20
.767
.002
.000
20
20
20
1
20
Ch. 7) The Different Classifications of Experimental Designs
Experimental Designs
Pre-experimental
True
Experimental
Quasi
Experimental
One-Shot Case
Study
Pretest-Posttest
Control Group
Time Series
Randomized
Blocks
One Group
Pretest-Posttest
Posttest: Only
Control Group
Multiple Time
Series
Factorial
Design
Static Group
Solomon FourGroup
Statistical