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

2. 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 ---

3. 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) 1 st 2 nd 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

4. -4  -3  -2  -1  Mean +1  +2  +3  +4  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 Normal Curve

5. X 10 20 30 40 50 -20 -10 0 10 20 Computation of Standard Deviation & Variance x (EX/N) = 30 (Mean) EX = 150 Test Scores Deviation scores (scores minus the mean x2x2 200 100 0 100 200 Squared deviation scores EX 2 = 1000 (Sum of the squared deviation scores) EX 2 /N = 200 (the variance or s 2 ) 200 = 14.14 (standard deviation) s2s2 = standard deviation or s Mean of the sum of the squared deviation scores

6. -4  -3  -2  -1  Mean +1  +2  +3  +4  Test Score 13.59% 34.13% 34.13% 13.59% 0.13% 2.14% 2.14% 0.13% Number of Cases Z score T score CEEB score Deviation IQ (SD = 15) Stanine Percentile -4 -3 -2 -1 0 +1 +2 +3 +4 10 20 30 40 50 60 70 80 90 200 300 400 500 600 700 800 55 70 85 100 115 130 145 4% 7% 12% 17% 20% 17% 12% 7% 4% 1 2 3 4 5 6 7 8 9 1 5 10 20 30 40 50 60 70 80 90 95 100 Relationships Among Different Types of Test Scores in a Normal Distribution

7. 40 45 55 60 70 75 80 90 100 Test Scores 40 45 55 60 70 75 80 90 100 Test Scores Positively Skewed Distribution Negatively Skewed Distribution

8. 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 program between tests Did the program work to increase scores?

9. % increase 100 90 80 70 60 50 40 30 20 10 0 MathEnglish “Lying” with numbers

10. Some Common Designs Used in I/O Research Static Group Comparison X O 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

11. Multiple Time-Series Design O 1 O 2 O 3 O 4 O 5 X O 6 O 7 O 8 O 9 O 10 O 1 O 2 O 3 O 4 O 5 O 6 O 7 O 8 O 9 O 10 Common Designs (cont.) O 1 O 2 O 3 O 4 O 5 X O 6 O 7 O 8 O 9 O 10 Time-Series Design O X O O Non-Equivalent Control-Group Design

12. R O X O R O O R indicates randomization Pretest-Posttest Control Group Design R X O R O Posttest-Only Control Group Design Fairly Uncommon Designs in I/O Research

13. 50 45 40 35 30 25 20 15 10 5 0 J F M A M J Jul Aug S O N D This is a graph of accident rates for a year. At first glance, does this graph indicate anything of importance to the organization?

14. 10 9 8 7 6 5 4 3 2 1 0 J F M A M J Jul Aug S O N D How about now?

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? 1995Drug 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 * * * * * * * * *