limit collection of categorical data age 0 - 18 19 – 25 26 – 35 36 – 45 46 – 55 56 – 65 85...

• Limit collection of categorical data

Age

0 - 1819 – 2526 – 3536 – 4546 – 5556 – 6585 & Above

Income

0 ------ 10,00010,001 – 25,000 25,001 – 35,00035,001 – 50,00050,001 – 75,00075,001 – 100,000100,000 & Above

Age in Years: _______

Income: ____________

~ I-O Research ~Measurement

~ I-O Research ~Measurement (cont.)

Yes _____

No __________ _____ _____ _____ _____

1 2 3 4 5 Highly HighlyDisagree Agree

• Limit collection of dichotomous data

Data Analysis

~ I-O Research ~

Limit use of ANOVA approach

100

0

High

Low

Leads to less statistical power, effect sizes, and reliability

Best to use some form of regression analysis

~ I-O Research ~Measurement (cont.)

• Restrict possibility of missing data

1.2.3.4.5.

48 49 50

Scale Questions

Missing

Missing

Computed score for scale or subscales containing questions #5 and #48 will

also be missing

Absolute versus Relative (Comparative) Assessments

Absolute: “How many hours of TV did you watch last year?

“Is this drink sweet?” or “How sweet is this drink?”

Relative: Did you watch TV more hours than you spent reading the local paper?

“Which of these five drinks is the sweetest?”

• Generally, it is easier for people to make relative vs. absolute judgments (more accuracy and consistency exists)

• People rarely make absolute assessments in everyday activities (most choices are basically comparative)

Limitation with relative assessments and the instances when absolute judgments are vital ---

Scales of Measurement

1) Nominal -- Indicates categories, classification (e.g., gender, race, yes/no)

Stats: N of cases (e.g., chi-square), mode

2) Ordinal -- Indicates relative position; greater than, less than (e.g., rank ordering percentiles)

Stats: Median, percentiles, order statistics

3) Interval -- Indicates an absolute judgment on an attribute (equal intervals)

No absolute zero point (a score of 80 is not twice as high as a score of 40)

Stats: Mean, variance, correlation

4) Ratio -- Possesses an absolute zero point (e.g., number of units produced)

All numerical operations can be performed (add, subtract, multiply, divide)

1st

2nd

3rd

Does not indicate how much of an attribute one possesses (e.g., all may be low or all may be high)

Does not indicate how far apart the people are with respect to the attribute

~ I-O Research ~

Interesting fact: Substantial amount of I-O studies are non-experimental (about 50%)

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

• Safety in work vehicles: A multilevel study linking safety values and individual predictors to work-related driving crashes. • Beyond change management: A multilevel investigation of contextual and personal influences on employees' commitment to change.

• The development of collective efficacy in teams: A multilevel and longitudinal perspective.

Some Recent Articles in the Journal of Applied Psychology

Study Variables

Multi-level analysis (or hierarchical linear modeling; HLM). Allows for the assessment of variance in outcome variables to be investigated at multiple, hierarchical levels. Related analyses include structural equation modeling and latent class modeling

~ I-O Research Trends ~

• Predicting workplace aggression: A meta-analysis.

• The good, the bad, and the unknown about telecommuting: Meta-analysis of psychological mediators and individual consequences.

Some Recent Articles in the Journal of Applied Psychology (cont.)

Meta-analysis: Statistical approach that allows the combination of results from multiple independent studies on a given topic. It allows a better estimate of the true “effect size,” giving more “weight” to larger studies.

~ I-O Research Trends ~

Some Recent Articles in the Journal of Applied Psychology (cont.)

Moderating variable (or 3rd variable): A variable that affects the strength and/or direction of the relationship between two variables.

Mediating variable: Variable that accounts for (explains) the relationship between two variables

Job enrichment strategies Job Satisfaction Age (as moderator)(The relationship may be stronger for older individuals)

Job enrichment strategies Job Satisfaction Growth need strength (as mediator)

(When growth need strength is considered the relationship between job enrichment and satisfaction goes away)

~ I-O Research Trends ~

Data Analysis

Usage: Approximately 10% of papers published in Journal of Applied Psychology employ factor analysis

✖ Avoid:

Varimax rotationPrinciple components analysisAutomatically keep factors with eigenvalues greater than 1.0

Use:

Iterative principle factors (least squares, or maximum likelihood)Oblique rotation (no assumption of factor independence)

~ I-O Research ~

Factor Analysis ---

✔

~ I-O Research (cont.) ~Suggestions

1) More use of “archival” data (many are of high quality with large sample sizes; e.g., government statistics on unemployment rates)

2) Longitudinal studies (assessment of change over time)

3) Report confidence intervals and effect sizes in addition to significance levels (e.g., p < .01)

Common Research Designs Used in I-O

One-Shot Case Study

X O

X = Treatment or Intervention

O = Observation or Collection of Data

One-Group Pretest-Posttest Design

O X O

13

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

6-week training program between tests

Did the program work to increase scores?

14

% increase

100

90

80

70

60

50

40

30

20

10

0

Math English

“Lying” with numbers

15

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?

1995 Drug Testing

55

50

45

40

35

30

25

20

15

10

5

1997

*

*

*

16

65

60

55

50

45

40

35

30

25

20

15

Given the illustration below, now what do you make of the effectiveness of the drug testing program?

1992 1993 1994 1995 1996 1997 1998 1999 2000

*

*

* *

*

**

*

*

17