1. What does a test score of 85 (out of 100) indicate?
When you get a test back in class, what is one of the first questions that you ask?

2. Test Score Distribution
Why is this problematic?
Low Variability *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* * *
* *
Test Score Distribution

3. * Positively Skewed Distribution Negatively Skewed Distribution
Test Scores
Test Scores

4. * Normal Curve Central Tendency Variability (Spread in Scores)
-4    Mean    
Central Tendency
a) Mode (most frequent score)
b) Mean (average score; [EX/N])
c) Median (midpoint of scores)
Variability (Spread in Scores)
a) Range (lowest to highest score)
b) Standard Deviation
c) Variance

5. Deviation scores (scores minus the mean
Computation of Standard Deviation & Variance
Deviation scores (scores minus the mean
Squared deviation scores
Test Scores X 10 20 30 40 50 x
-20
-10 10 20 x2
400
100
EX2 = 1000 (Sum of the squared deviation scores)
EX = 150
(EX/N) = 30 (Mean)
Mean of the sum of the squared deviation scores
EX2/N = 200 (the variance or s2)
= standard deviation or s
200 s2
= (standard deviation)

6. Z Score
X – Mean
___________ SD

7. Relationships Among Different Types of Test Scores in a Normal Distribution
Number of Cases
2.14%
0.13%
0.13% %
13.59% % % %
-4    Mean    
Test Score
Z score
T score
CEEB score
Deviation IQ
(SD = 16)
Stanine
Percentile
4% % % % % % % % %

8. * Correlation --- Some Key Concepts
Consists of a set of ordered pairs
Indicates both the magnitude and direction of the relationship between variables
c) Range is from -1.0 to + 1.0

9. Fathers Height (in inches)85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65 64 63 62 61 60
Example of basic concept of correlation * * * * * * * * * * * * * * * * * * * * *
Son’s Height
Fathers Height (in inches)

10. Widely Reported Correlations
Smoking and lung cancer
Media violence and aggression
Condom use and sexually transmitted HIV
Passive smoking and lung cancer at work
Lead exposure and children’s IQ
Nicotine patch and smoking cessation
Calcium intake and bone mass
Homework and academic achievement
From Bushman, B.J., & Anderson, C. A. (2001). Media violence and the American public: Scientific facts versus media misinformation, American Psychologist, June/July,
Asbestos and laryngeal cancer
Self-examination and breast cancer

11. Positive Correlation Test Scores Performance Job * * * * * * * * * * *85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65 64 63 62 61 60 * *
Positive Correlation * * * *
Performance
Job * * * * * * * * * * * * * * *
Test Scores

12. Absenteeism (in hours)85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65 64 63 62 61 60 * * * *
Negative Correlation * * * *
Performance
Job * * * * * * * * * * * * * *
Absenteeism (in hours)

13. * Test Scores Correct Acceptances False Rejections Performance Job85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65 64 63 62 61 60 *
Correct Acceptances
False
Rejections * * * *
Good * *
* * *
* *
Performance
Job * * * * * * * * * * * * * *
Poor
Correct Rejections
False Acceptances
Fail
Pass
Significant Correlation
Test Scores

14. Test Scores Correct Acceptances False Rejections Performance Job Poor85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65 64 63 62 61 60
Correct Acceptances
False
Rejections * * * * * * * *
Good * * * * *
Performance
Job * * * * * * * * * * * * * * * * * *
Poor * * *
Correct Rejections
False Acceptances
Fail
Pass
No Correlation
Test Scores

15. Job Performance Scores
Test Scores
100 50
100 50

16. Effect of raising cutoff score?85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65 64 63 62 61 60
False Rejections
Correct Acceptances
Effect of raising cutoff score? * * * *
Good * *
* * *
* *
Performance
Job * * * * * * * * * * * * * *
Poor
False Acceptances
Correct Rejections
Fail
Pass
Test Scores

17. Effect of lowering cutoff score?85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65 64 63 62 61 60
False Rejections
Correct Acceptances
Effect of lowering cutoff score? * * * *
Good * *
* * *
* *
Performance
Job * * * * * * * * * * * * * *
Poor
False Acceptances
Correct Rejections
Fail
Pass
Test Scores

18. Basic Steps in Research
Observation
Statement of the Problem
(Research Question)
State Hypotheses
Use/Generate a Theory
Design Study
Measurement (Collect Data)
Statistical Analysis
Interpretation (Conclusion)

19. ~ Tips for Choosing a Research Topic ~
Read, read, read (e.g., journal articles) to generate research ideas
Pick a topic that interests you (it will be more fun to do)
Be realistic (pick a topic that can be accomplished in a reasonable
time frame)
Improve on prior research (e.g., “limitations of present study,”
“suggestions for future research”)
Minimize problems, time delays in data collection phase of
research

20. * Basic Scales of Measurement
Measurement: The assignment of numerals to events or objects according to certain rules
Nominal (Categorization or classification)
Yes/No, True/False
Male/Female
Ordinal (Ranking)
1st, 2nd, 3rd
Interval (Equal intervals exist between points on the scale)
_______ _______ _______ _______ _______
Ratio (an absolute zero point exists)
Kelvin scale of temperature
Time
Height, Weight
Secretariat Belmont Stakes (1973) Video

21. ~ I-O Research ~ Measurement Limit collection of categorical data
Age in Years: _______
Income: ____________
Age
19 – 25
26 – 35
36 – 45
46 – 55
56 – 65
85 & Above
Income
,000
10,001 – 25,000
25,001 – 35,000
35,001 – 50,000
50,001 – 75,000
75,001 – 100,000
100,000 & Above

22. ~ I-O Research ~ Measurement (cont.)
Limit collection of dichotomous data
Yes _____
No _____
_____ _____ _____ _____ _____
Highly Highly
Disagree Agree

23. ~ I-O Research ~ Measurement (cont.)
Restrict possibility of missing data
Scale Questions 1. 2. 3. 4. 5.
Missing
Computed score for scale or subscales containing questions #5 and #48 will also be missing 48 49 50
Missing

24. ~ Key Terms ~
Predictor: One that can be manipulated or used to predict scores on the dependent variable
Criterion: The variable of interest; the one you are attempting to understand or affect
SAT, ACT scores used to predict success in college
Interview scores used to predict performance in a job
Predictor
Criterion

25. Research Design Options Laboratory Experiment
Control
Realism
Laboratory Experiment
Manipulate independent variable
Precise measurement of dependent variable
Low generalizability
Quasi-experiment
Less control over variables
More generalizability
Case study
Detailed information
Low generalizability
Naturalistic observation
Low control
Data used to identify themes; placed into categories
Not common in IO
Field study
Survey research

26. Alternative Research Approaches
Meta Analysis:
Statistical analysis of past research findings
More accurate estimate of the true “effect size”
More “weight” to findings from larger studies
Role of "file drawer" effect
Predicting workplace aggression: A meta-analysis.
The good, the bad, and the unknown about telecommuting: Meta-analysis of psychological mediators and individual consequences.

27. Alternative Research Approaches (cont.)
Data Mining ("Big Data"):
Statistical analysis of large, complex data sets to examine patterns (e.g., Internet activity, social network connections, consumer behavior)
Example: Collaboration between Weather Channel & IBM to reduce economic impact of weather on business (about ½ billion/year). Weather data is collected from over 100,000 weather sensors, aircraft, smartphones, buildings, and moving vehicles. Combined data yields 2.2 billion unique forecast points, and an average of more than 10 billion forecasts on an active weather day.
Some Uses:
Companies can alter staffing and supply chain strategy
Insurance companies can forewarn customers, limit damage and reduce costs
Power companies can anticipate supply and demand
Source: Lisa Morgan, Information Week (2015), LINK

28. ~ Some I-O Research Suggestions ~
More use of “archival” data (many are of high quality with large sample sizes; e.g., government statistics on unemployment rates)
Longitudinal studies (assessment of change over time)
3) Report confidence intervals and effect sizes in addition to significance levels (e.g., p < .05)
Cohen's d (effect size example) here

29. ~ I-O Research ~
Interesting fact: Substantial amount of I-O studies are non-experimental
Overall Point:
Best for research to be driven by theories and problem-solving approaches not by methodology/statistics
Much research efforts in I-O focus on rather trivial questions that can be studied with “fancy” techniques
Bulk of research has limited applied significance

30. Some Pre-Experimental Designs
X = Treatment or Intervention
O = Observation or Collection of Data
One-Shot Case Study
X O
One-Group Pretest-Posttest Design
O X O
Static Group Comparison
X O O

31. 6-week training program between tests
Did the program work to increase scores?
Math
Pretest 55 64 44 33 28 63 48 38 46 47
Math
Posttest 56 66 46 38 29 63 50 40 48 47
English
Pretest 33 35 43 36 20 60 40 31 52 64
English
Posttest 35 37 47 36 21 62 40 31 56 66

32. “Lying” With Numbers % increase 100 90 80 70 60 50 40 30 20 10 Math
Math
English

33. An organization reports that accidents have decrease substantially since they began a drug testing program. In 1995, the year before drug testing, the number of accidents was 50. In 1996, the year testing began, the amount dropped to 40. In 1997, the year after drug testing the number of accident dropped to 29. What do you make of this? 55 50 45 40 35 30 25 20 15 10 5 * * *
1995
Drug Testing
1997

34. Given the illustration below, now what do you make of the effectiveness of the drug testing program?* 65 60 55 50 45 40 35 30 25 20 15 * * * * * * * *

35. Some Quasi-Experimental Designs
Non-Equivalent Control-Group Design
O X O
O O
Time-Series Design
O1 O2 O3 O4 O5 X O6 O7 O8 O9 O10
Multiple Time-Series Design
O1 O2 O3 O4 O5 X O6 O7 O8 O9 O10
O1 O2 O3 O4 O O6 O7 O8 O9 O10

36. Some True Experimental Designs
Pretest-Posttest Control Group Design
R O X O
R O O
R indicates randomization
Posttest-Only Control Group Design
R X O
R O

37. ~ Basic Ethics in Research ~
Informed Consent
Right to Privacy
Anonymous, confidential treatment of data
Protection from Deception (cost-benefit assessment)
Debriefing e.g., (explanation of the purpose of the study)

38. At first glance, is there anything happening here?50 45 40 35 30 25 20 15 10 5
J F M A M J Jul Aug S O N D

39. How about now? 10 9 8 7 6 5 4 3 2 1
J F M A M J Jul Aug S O N D