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)