casual research design
Page 1: Introduction to Research Designs
School: W. P. Carey School of Business, Arizona State University
Topic: Overview of Exploratory, Descriptive, and Causal Research Designs
Page 2: Review of Previous Class
Focus on three basic types of research design:
Emphasis of each type
Key characteristics and techniques of "Exploratory" research
Differences between cross-sectional and longitudinal studies in "Descriptive" research
Page 3: Descriptive Research Example
Example: Brand Switching Matrix from Sun Devils Panel
Sample Size: 100 students
Brand Loyalty for Brand B: 48.1% (13/27)
Market Share: Brand A (April: 19%, May: 15%)
Conditional Probability of buying Brand C in May after buying Brand B in April: 40.1% (11/27)
Data Presentation:
Matrix of brand shifts from April to May
Page 4: Competitive Analysis in Brand Switching
Identifying competition: Brands B and C show significant rivalry
Brand data analysis:
Brand A (April: 21.1%, May: 11.1%)
Brand B (April: 26.3%, May: 40.1%)
Brand C (April: 3.7%, May: 38.9%)
Page 5: Cross-sectional Study Limitations
Explanation of cross-sectional study timeframes:
Two time points: April and May
Possibility to calculate Market share, not brand loyalty or conditional probability
Page 6: Learning Objectives
Understanding of causality:
Distinction between laboratory experiments and field experiments
Page 7: Types of Research Design
Categorization:
Exploratory Research
Descriptive Research
Causal Research
Conclusive Research
Page 8: Causal Research Focus
Decision Problem (DP): Improving student satisfaction
Research Problem (RP): Identifying factors affecting student satisfaction
Factors from exploratory research:
Self-confidence, curriculum, quality of teaching, etc.
Cross-sectional survey results: Correlation between mentorship and satisfaction
Page 9: Defining Causal Research
Causal Research: Focuses on obtaining evidence for cause-and-effect relationships
Applicability:
When stronger evidence is required for the outcomes of actions
Testing cause-and-effect relationships
Methodology: Experimentation
Page 10: Correlation vs. Causation
Definition of causation: Change in one variable results in change in another
Page 11: Misinterpretation of Correlation
Correlation does not imply causation due to:
Reverse causation (e.g., wind causing windmills to rotate)
Third factor influence (e.g., drinking habits influencing both headache and shoe wearing)
Page 12: Coincidental Correlation
Third point of misinterpretation: Coincidence (spurious correlations)
Page 13: Conditions for Inferring Causality
Necessary conditions include:
Concomitant variation
Time order of events
Absence of alternative causes
Example of causation framework
Page 14: Example 1 – Promotion Impact on Sales
Hypothesis: "Buy One Get One Free" increases sales
Analysis of conditions affecting sales through variation assessments
Page 15: Example 2 – Advertisement Impact on Sales
Hypothesis: Advertisement spending increases sales
Comprehensive assessment of associated variation
Page 16: Conducting Causal Research via Experiments
Experiments enable isolation of relationships between independent and dependent variables:
Change levels of X variables
Observe impact on Y variables
Control other variables
Page 17: Distinction of Experimental Context
Laboratory vs. Field Experiments:
Laboratory: Controlled conditions, higher precision
Field: Natural settings, reflects real-life scenarios
Page 18: Advantages and Disadvantages of Experimental Approaches
Laboratory experiments:
(+) Replicability and control
(-) Artificiality of setting
Field experiments:
(+) Realistic context
(-) Less control over variables
Page 19: A/B Testing in Causal Research
Explanation of A/B testing as a randomized experiment
Page 20: Market Testing Overview
Definition: Controlled experiment in selected market segments
Page 21: Characteristics of Experiments
Key characteristics:
Control of conditions by the experimenter
Random assignment of subjects to conditions
Page 22: Key Variables in Research
Independent Variables (IVs): Factors being varied to assess impact
Example: Packaging influence on coffee taste perception
Page 23: Dependent Variables (DVs)
DVs: Observed attitudes, beliefs, perceptions, and behaviors affected by IVs
Example: Rating scale for taste perception of coffee
Page 24: Comparative Analysis of Research Designs
Objective:
Exploratory: Discovery of ideas
Descriptive: Market characteristics
Causal: Cause-and-effect determination
Distinction of methodology:
Exploratory: Qualitative, smaller samples
Descriptive: Quantitative, larger samples
Causal: Experiments manipulating independent variables
Page 25: Quiz and Assignments
In-class assignments to reinforce learning concepts.