Lab, Field, and Survey

In this post I will summarize lab, field, and surveys. The most important things to remember are the measures that are used and the inferences each approach allows us to make.

Lab Experiment

  • Requires IVs and DVs, pre and post testing, experimental and control groups.
  • Does not recreate reality, but studies variables in highly controlled, generic/created situation with stimulus/manipulation controlled by the experimenter.

Field Experiment:

  • Similar to the lab experiment, it manipulates something to see effect on something else. Less control.

Survey

  • Doesn’t include a manipulation and the goal is to leave setting and participants just as they were found.
  • Looks at natural variance instead of stimulating variance.

The measures used for each approach are:

  • Field and Lab Experiments
    • Dependent variable is real behavior.
    • Uses ANOVA, t-tests, etc.
    • Looks at mean differences.
  • Surveys
    • Dependent variable is measured through self-reports.
    • Correlational and often measured at same time.
    • Common method variance issues.

The key aspect to keep in mind when designing a study is understanding how each approach allows us to make different inferences. Ideally, there will be triangulation – using multiple approaches.

  • Lab and Field Experiments
    • Causal: treatment X caused outcome Y
    • Able to do this because of issue of control (rule out extraneous influences on Y).
    • Field allows for time.
  • Surveys:
    • Correlational
    • Non-causal
    • Associational inferences
    • X varies with Y

It is good to keep in mind the pros and cons for each approach.

Laboratory

  • PROS
    • Random assignment
    • Control
    • Precision
    • Causal inferences
    • Can plan
  • CONS
    • Ethical limitations
    • Generalizability
    • Short-lived (brevity)
    • \Weaker manipulations than “real-life” counterparts
    • May miss critical boundary conditions
    • Artificial
    • Demand characteristics
    • Evaluation apprehension
    • Experimenter expectancy

Field Experiments

  • PROS
    • Random assignment (possibly)
    • Manipulation in real setting
    • Allows control for causal relationships
    • May encourage application of results
    • Longer time frame
    • Context is meaningful to participants
    • Subjects less likely to be aware in experiment
  • CONS
    • Hard to do
    • Hard to get true control groups
    • Hard to control outside influences/confounds (therefore, stimulus less impactful)
    • Difficult to control independent variable (therefore, hard to draw “cause-effect” relationships)
    • Cost/time
    • Ethical considerations in selecting control group
    • Demand characteristics
    • Evaluation apprehension
    • Experimenter expectancy

Surveys

  • PROS
    • Natural setting (perhaps more ext. validity)
    • More realistic (more believable)
    • Describes the population
  • CONS
    • Hard to control
    • May not be as generalizable as we think
    • Difficult to replicate
    • Not causal
    • More bias (therefore, less reliable)
    • Cross-sectional (measure everything at once—may lead to common method variance)
(Adapted from group and course notes)
(Flashcards and other resources here)
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