Applying Science Towards Understanding Behavior in Organizations Chapters 2 & 3

Research Issues in Organizations Approaches to collecting data Experimental Observational/correlational Data collection issues Sampling How should we select participants? What impact does it have on the results? Experimental design Controlling potential confounds Assigning participants to experimental conditions Measurement issues Describing and interpreting the results

Experiments: A Review Experiments - Do changes in one variable (X) “cause” changes in another variable (Y)? Independent Variable (X) condition or event that is manipulated by experimenter Dependent Variable (Y) variable that is affected (hopefully) by manipulating independent variable Extraneous Variable(s) any variable other than independent variable that may influence dependent variable

Experiments: Pros and Cons Advantage: Allows conclusions about direct effects of one variable on another Disadvantages: Experimental conditions are artificial results may not “generalize” to the real world Some questions can’t be tested in an experiment Require control that is not always available in the “real” world

Experimental Design Controlling potential confounds Goal of experiment is to “rule out” alternate explanations of what affected dependent variable Confounds are threats to internal validity Can be controlled through appropriate experimental design and procedures

Internal Validity History Maturation Testing Instrumentation Statistical Regression Selection Mortality Selection-Maturation Diffusion of Treatment External Validity Sample Setting (e.g., culture) Time (e.g., 60s vs. 90s) Replication (lack of) Do the results of this experiment generalize (apply) to settings other than the experiment Is there another reason (other than the independent variable) that could explain the results of the experiment. Validity

How participants are selected for a study influences the extent to which the results can be applied to a larger group (external validity). A wide variety of techniques are available Two Main types of sampling  Probability  predetermined chance of any individual in the population being selected for the study  Nonprobability  Typically nonrandom sampling Sampling

Sampling Techniques Probability Sampling 1. Simple random sampling 2. Systematic sampling 3. Stratified random sampling 4. Cluster sampling 5. Multistage sampling Nonprobability Sampling 1. Convenience sampling 2. Quota sampling 3. Snowball sampling

Post with no Control Group TrainingPosttest

Pre – Post with no Control Group PretestTrainingPosttest

Control Group with no Pretest Experimental Group TrainingPosttest Control GroupPlaceboPosttest Group Differences

Pre – Post with Control Group Pretest Experimental Training Posttest PretestControlPosttest Group Differences Group Differences

Measurement Measurement – the process of assigning numbers to objects or events according to rules (Linn & Gronlund, 1995). Psychological Measurement – concerned with evaluating individual differences in psychological traits. Trait – descriptive label applied to a group of behaviors (e.g., friendly; intelligent)

Two basic types Descriptive Describes the nature and properties of the data Inferential Used in testing hypothesis  (e.g., differences between groups)  (e.g., relationships between variables) Data Analysis

Measures of Central Tendency Measures of Variability Distribution of the data Descriptive Statistics

Measures of Central Tendency Mean average score of all observations in distribution Median midpoint of all scores in distribution Mode most frequently occurring score in distribution Descriptive Statistics

Measures of Variability Range subtract the lowest from the highest score Standard Deviation measure of the “spread” of the scores around the mean Variance square of the standard deviation Descriptive Statistics

Shapes of distribution curves Bell (normal distribution) The bell curve has desirable statistical properties A number of inferential statistics “assume” data is normally distributed Skewed Curves Negative Skew - tail of the curve is to the left Positive Skew - tail of the curve is to the right Distribution of the data

Properties of a normal distribution Measures of central tendency are the same mean = median = mode We know percentage of scores that fall within  1 standard deviation (68%)  2 standard deviations (95%)  3 standard deviations (99%) Descriptive Statistics

Distribution in Normal Curve

The extent to which one variable can be understood on the basis of another Properties of correlation coefficient direction (positive or negative) magnitude (strength of the relationship) Cannot determine causality Correlation

r =.95 Scatter Plots (positive relationship)

r =.00 Scatter Plots (no relationship)

r = -.95 LowHigh Low High Scatter Plots (negative relationship)

Correlation: A Review