Study your flashcards anywhere!

Download the official Cram app for free >

  • Shuffle
    Toggle On
    Toggle Off
  • Alphabetize
    Toggle On
    Toggle Off
  • Front First
    Toggle On
    Toggle Off
  • Both Sides
    Toggle On
    Toggle Off
  • Read
    Toggle On
    Toggle Off

How to study your flashcards.

Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key

Up/Down arrow keys: Flip the card between the front and back.down keyup key

H key: Show hint (3rd side).h key

A key: Read text to speech.a key


Play button


Play button




Click to flip

30 Cards in this Set

  • Front
  • Back

Research strategy

Refers to general approach and goals of a research study. The question and what you want to address determines which research strategy you choose.

Research design

Refers to how you will implement the strategy

Research procedure

Refers to details on how the study will be done.

Overview of research strategies

General approach and goals of a study. Strategy depends on what questions you ask. Examine individual variables, measure variables for one group, compare variable between groups.

General approach and goals of a study. Strategy depends on what questions you ask. Examine individual variables, measure variables for one group, compare variable between groups.

Descriptive research study

Describe current state of a variable. Does not examine the relationship between variables. Not comparing between groups.

Correlational research strategy

Identify correlations between variables. Measures relationship between variables (positive, negative, curvilinear). Measurement of two variables. As one variable changes, does the other variable also change? (no manipulation of any variables). Typically on interval or ratio scale.

Non-experimental research strategy

Examine relationship between variable. Looking for difference between two groups. One variable has two levels/conditions. Therefore not a correlation.

Experimental research strategy

Explain relationship between 2 variables. Establish cause and effect relationships. Compare 2 or more groups on a dependent variable, The group is manipulated, individuals are randomly assigned to the group. Has a control group.

Quasi-experimental research strategy

Similar to experimental. Attempts to explain relationship between two variables. Groups are not randomly assigned. Based on pre-existing participant variable. Most human studies. Does not provide a definitive answer about cause and effect.

Validity of a measure

Are you measuring what you aim to measure?

validity of an experiment

Are you answering the question that you aim to answer? Internal validity, external validity

Internal validity

Are you confident in the cause-effect relationship between the variables in your experiment? Research has internal validity if it produces unambiguous explanation for the relationship between two variables. Demonstrate that change in dependent variable must be due to change in the independent variable within the experiment. Any factor that raises doubts or allows alternative explanations is a threat to the internal validity.

External validity

Can you generalize this relationship between variables beyond the experiment? Extent to which you experimental results hold true outside of the study. Will the results stand with a different sample? Will they stand in a different setting? Is using a different measure? Does a similar finding emerge in the real world? Any characteristic that limits the generalizability of the results is a threat to external validity.

Validity in experimental research strategies

conducted in controlled and constant lab settings, high internal validity. Experimental research often conducted in unfamiliar environments, so harder to generalize beyond the experimental setting, Limits external validity.

Validity in non experimental research studies

Realistic environments offer higher external validity. Environment is not manipulated or controlled, limited internal validity

Threats to external validity

Anything that limits the generality of the results.

Generalizing across participants or subjects

Selection Bias: favour choosing one individual over another.

Convenience sample: Can be biased

Volunteer Bias: volunteers may have different characteristics than non-volunteers.

Participant Characteristics: Characteristics in the demographic (age, race, politics, gender)

Cross species generalization: Cannot presume that nonhuman study is readily applied to humans. Note similarities/ differences between species.

Generalizing across study features

Novelty effects: Participants/ subjects influenced by being in novel environment.

Multiple treatment interference: effect from one trial carries over to the next trial

Experimenter characteristics: Demographic of experimenter can affect participant responses. Animal studies.

Generalizing across different measures

Sensitization: monitoring behaviour can alter behaviour

Timing of measurement: When you choose to administer your measure has an impact.

Threats to internal validity

To have internal validity, there must be only 1 explanation for results. Ideally only independent variable impacts the dependent variable. Other factors (confound) likely to affect the dependent variable. Minimize the size or impact of confound.

Confounding variables

Change with the independent variable, thus can affect the dependent variable. Very difficult to remove all confounds. Assignment bias, Environmental variables, time related variables, observer bias.

Assignment bias (C)

Participants in one experimental condition are noticeably different than the other experimental condition.

Environmental variables (C)

Administering test in different environments

Time related variables (C)

More apparent when comparing one group over time. History, maturation, instrument, experimenter, multiple treatment interference.

Observer Bias (C)

Researchers do not know the treatment of the group/ individual. Single blind or Double blind experiment.

Obscuring variables

Make changes in dependent variable hard to observe. Lead to measurement error, noisy data. Ineffective manipulation. Measurement error, excessive variation in data.

Ineffective manipulation

Inadequate manipulation of the independent variable, thus no detectable change in the dependent variable. Lead to false conclusion that independent variable has no effect on the dependent variable.

Measurement error

Too much variation in the measurement. Poor instrumentation, inconsistent training, scoring.

Excessive variation in data

Individual differences contribute the vast majority of variation. Repeated measures.

3 rules for generalization

Controlled comparison rule: when comparing data between experimental and control groups, the only difference must be the independent variable. When comparison is controlled, internal validity.

Sampling rule: Adequate sample size to control for random sampling error. Unbiased sample so it is representative of the population. Even if not fully representative, can typically generalize broad principles.

Operational Definitions rule: Determines what principles can be generalized. Single experiment only allows generalization within specific terms of the operational definition. To broaden generalizations to entire constructs, replicate experiments using different operational definitions of the same construct.