1. Data Analysis for Experimental Design
Method Section & Data Analysis

3. Components of the Research Method of Experimental Design
Method (Overview)
Sample
Design and experimental stimuli (manipulations)
Pre-manipulation and post-manipulation measures
Administration procedure

4. Independent Variables
Setting the stage
informed consent
brief explanation of what is expected
Types of manipulations
straightforward
staged
to create a psychological state
to simulate a real world situation
use confederates

5. Dependent Measures Types of measures self-report rating scales
behavioral
reaction time
error rate
physiological
GSR, heart rate

6. Dependent Measures (cont.)
Sensitivity
ceiling effect - too easy
floor effect - too hard
no effect
Multiple Measures
e.g. time perception
Ethics of measures (e.g. privacy)
Cost

7. Data Defects Missing data some statistics allow for missing data
can replace with averaging techniques
SPSS have several missing data options
Extreme score or outliers
techniques for discarding
replace with averaging
Appropriate Statistics!!!!!!!!!!!!!!!!!

8. Missing Data A Simple Example of a Missing Data Analysis
Understanding the Reasons Leading to Missing Data
Ignorable Missing Data
Other Types of Missing Data Processes
Examining the Patterns of Missing Data
Diagnosing the Randomness of the Missing Data Process

9. Missing Data (Cont.) Approaches for Dealing with Missing Data
Use of Only Observations with Complete Data
Delete Case(s) and/or Variable(s)

10. Outliers Detecting Outliers Outlier Description and Profiling
Univariate Detection
Bivariate Detection
Outlier Designation
Outlier Description and Profiling
Retention or Deletion of the Outlier

11. Measurement Scales
Nominal (categorical)
Ordinal
Interval
Ratio

12. Nominal Scales Mutually exclusive categories
Labels only, no arithmetic properties
Examples: Sex, location, marital status
Statistical operations: Mode, frequencies, crosstabs

13. Ordinal Scales Implies more or less, but not how much more or less
Can use median or mode, but not average
Crosstabs

14. Interval/Ratio Scales
Numbers in scale represent equal increments
Scales have the most desirable statistical properties
(The ratio scale is an interval scale with a fixed zero point, e. g. age and temperature)

16. The Multiple Choice Question
Simple, versatile
Single or multiple response
No more than 8-10 so categories
Mutually exclusive categories
Pre-testing should show labeled alternatives cover 90% of answers

17. Single Response, Multiple Choice Questions
Choice criterion must be clearly stated
Choice criterion must define a single answer

18. Forced Ranking Scales

19. Forced Ranking Scales Options judged relative to one another
Mimics actual decision making
Disadvantage: interval between items not measured
Limited number of items can be ranked

20. Guidelines for Using Forced Ranking Scales
10 Items or less
Relative standing is focus
Use single judgment criterion
Analysis confined to frequencies, special procedures for ranked data

21. The Likert Scale Widely used scale 5 points
Degree of agreement with statements
Use several statements to measure a “construct”
e.g. “customer satisfaction,” “loyalty,” “attitudes towards technology”

24. Developing Constructs

25. Curiosity I think learning about things is interesting and exciting.
I am curious about things.
 My spare time is filled with interesting activities.
 I like to solve problems that puzzle me.
I want to probe deeply into things.
I enjoy exploring new places.
New situations capture my attention.
I feel inquisitive.
The prospect of learning new things excites me.
I feel like searching for answers.

26. Attitudes towards Technology
I can usually figure out new hi-tech products and services without help from others.
New technologies are often too complicated to be useful.
I get overwhelmed by how much I need to know to use the latest technology.
Technology gives people more control over their daily lives.
People get too dependent on technology to do things for them.
New technologies are mentally stimulating.
Learning about new technologies can be as rewarding as the technology itself.
The products and services with the newest technologies are more convenient to use.
New technologies are not designed for use by people like me.

27. Customer Satisfaction (tailored for online purchases)
If I had it to do over again, I’d make my most recent purchase at this website.
I am sure it was the right thing to make my most recent online purchase at this website.
I have truly enjoyed purchasing from this website.
My choice to purchase from this website was a wise one.
I am satisfied with my most recent decision to purchase from this website.
I am happy I made my most recent online purchase at this website.

28. Loyalty (intentions)
I encourage friends and relatives to do business with this website.
I say positive things about the website to other people.
I will do more business with the website in the next few years.
I would recommend the website to someone who seeks my advice.
I consider this website to be my first choice to buy the kind of product I most recently purchased online.

29. The Likert Scale Easy to use Easy to code
Useful in statistical analyses
Usually treated as interval level

30. The Likert Scale Keep in mind:
Only use for statements that ask for degree of agreement
Use for several items to use space well
Items should be diverse enough to cover the issue

31. The Likert Scale Items for a construct can be summed
Recode reverse scored items before summing

32. Likert-like Scales (NOT Likert-Scales)

34. Verbal Frequency Scale
Indicates how often
Likert-like, but usually not interval level

36. The Horizontal Numeric Scale
Items judged on a single dimension
Intermediate values are not labeled

38. The Semantic Differential Scale
7 point scale
Bipolar adjectives/statements
Can give quick comparative profile of objects

41. Purchase Intent Scales
Often of interest in marketing
Write them carefully

42. The Fixed Scale
Rs allocate a fixed number of points (usually 10 or 100) to choices

45. Univariate Statistical Analysis
What is hypothesis?
Null and alternative hypotheses
Significance level
Chi-square test (test of a proportion)
“Is the difference between the expected (hypothetical) distribution and the observed distribution statistically significant?”
Illustration of an Example

46. Bivariate Statistical Analysis: Tests of Differences
Chi-square test (a test of differences among groups)
“Is the difference between the distribution statistically different?”
Illustration of an Example
T-test for Difference of Means
“Is the difference between the means of men and women statistically different?”

47. ANOVA (Analysis of Variance)
Determines if mean group scores are far apart relative to our uncertainty about the actual value of the means

48. Example
Grand Mean= /3 = 8

49. Example There seems to be an overall average difference Questions:
Is this difference statistically significant?
If so, is the size of the difference managerially significant?

50. ANOVA (Analysis of Variance)
Conceptually, ANOVA compares:
difference among means/uncertainty OR
explained variance/unexplained variance OR
between treatment variance/within treatment variance

51. Analysis of Variance (ANOVA)
F-test
The larger F-value, the better result
Determines whether there is more variability in the scores of one sample than in the scores of another sample)
“Are the two sample variances different from each other or are they are from the same population?”

52. Logic of ANOVA
Each observation is different from the Grand (total sample) Mean by some amount
There are two sources of variance from the mean
1) That due to the treatment or independent variable
2) That which is unexplained by our treatment

53. Logic of ANOVA (cont.)
The statistical test of significance is based on the relative size of the variance caused by the IV relative to unexplained variance
Three quantities are calculated:
Total variance
Between treatment sum of squares (explained)
Within treatment sum of squares (unexplained)

54. The F-ratio MS = mean square bg = between groups wg = within groups
Numerator is the “effect” and denominator is the “error”
df = # of categories – 1 (k-1)

55. F-Statistic Used in Factorial Designs Is an extension of the t-test.
It is an analysis of variance that is a more general procedure than the t-test.
When a study has only one independent variable and only two groups using an F or a t makes no difference.
However analysis of variance (ANOVA) is conducted when there are more than two levels of an independent variable and when a factorial design with two or more independent variables is used.

56. One-Way ANOVA Partitions Total Variation
Variation due to Random Sampling are due to Individual Differences Within Groups.
Variation due to treatment
Variation due to random sampling
Sum of Squares Among
Sum of Squares Between
Sum of Squares Treatment (SST)
Among Groups Variation
Sum of Squares Within
Sum of Squares Error (SSE)
Within Groups Variation 89

57. Post Hoc Tests Vs Planned Comparisons
Post hoc tests allow us to make all pairwise comparisons. These tests trawl the data, searching for significant differences and so are not very sensitive.
Planned comparisons enable us to address specific questions with a higher degree of sensitivity.
It is not appropriate to use both in order to answer the same question.

58. Statistical Issues Assumptions of the test
are your data normally distributed
is the sd about equal in each group
Statistically significant difference vs. “real” difference
Hypotheses
null vs. research
never “prove” anything

59. Statistical Issues (cont.)
Scale attenuation effects
floor and ceiling effects
restricted range
Regression to the mean
Criterion choice
the .05 “rule”
Type I and Type II errors

60. Statistical Issues (cont.)
Interpreting non-significant results
poor design
poor procedures
poor instructions
poor operational definitions
power of the test
N and effect size