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 ValiditySampleSetting (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 Sampling1. Simple random sampling

2. Systematic sampling

3. Stratified random sampling

4. Cluster sampling

5. Multistage sampling

Nonprobability Sampling1. Convenience sampling

2. Quota sampling

3. Snowball sampling

Post with no Control Group

Training Posttest

Pre – Post with no Control Group

Pretest Training Posttest

Control Group with no Pretest

ExperimentalGroup

Training Posttest

Control Group Placebo Posttest

GroupDifferences

Pre – Post with Control Group

PretestExperimental

TrainingPosttest

Pretest Control Posttest

GroupDifferences

GroupDifferences

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

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Exam Points

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r = .95

Scatter Plots (positive relationship)

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Job Satisfaction

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Low HighLow

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Scatter Plots (negative relationship)

Correlation: A Review