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Understanding Causation and Experimental Design: Essential Components for Valid Research

This chapter explores the critical aspects of establishing causation in research through experimental design. It discusses three main criteria for determining causation: correlation, time order, and non-spuriousness. Additionally, two ways to strengthen causal statements are identified: specifying the causal mechanism and context. True experiments are detailed, highlighting the need for comparison groups, random assignment, and potential threats to internal validity, such as non-comparable groups and contamination. The chapter further examines the challenges of generalizability in experimental research.

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Understanding Causation and Experimental Design: Essential Components for Valid Research

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  1. Research Methods Chapter 6 Causation & Experimental Design

  2. Three Criteria For Determining Causation in a Hypothesis • Correlation (association) • Time Order • Non-Spuriousness Two additional things used to strengthen a causal statement • Identify Causal Mechanism • Specify the context

  3. Why Experiment?: True Experiments • True experiments must: • have at least 2 comparison groups • Experimental Group • Control Group • have variation in the Ind. Variable before assessment of the dependent variable • Have subjects randomly assigned to 2 or more groups • Ensures internal validity but not generalizability • Sometimes matching is used as a poor substitute for random assignment

  4. Exp. Are well suited to produce valid conclusions about causality Less well suited to achieve generalizability Validity in Experiments

  5. Let’s take a look at some of the ways in which experiments help (or don’t help) to resolve potential problems with causal (internal) validity and generalizability…

  6. What Are The Threats to Validity in Experiments? • Threats to causal (internal) validity • Non-comparable groups • When the experimental group and control group are not really comparable • Endogenous change • When natural developments in the subjects account for changes between pre and post tests • History • When subjects become aware of things outside the experiment (like the news) that affects their outcome scores • Contamination • When comparison and treatment groups somehow affect one another • Treatment Misidentification • When subjects experience a treatment that was unintended • Generalizability (The Achilles heel of true experiments….) • Sample Generalizability • Cross-Population Generalizability • Interaction of Testing and Treatment

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