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58 Cards in this Set
- Front
- Back
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Deception in Research
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- May not be told complete details of study or misled about procedures
- Must forewarn - Must be justified - No possible alternatives would be effective PROS: Naturalistic Behavior CONS: Cause Mistrust -Have to debrief |
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IRB
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Institutional Review Board
Effective safeguard for participants, researchers, and universities -Determines degree of risk -Expedited or Formal Reviews |
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Effective Literature Searches
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- Compose narrative of search questions
- Identify seperate concepts in your question - Use APA thesaurus - Combine concept words in manner that best suits question |
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Independent Variable
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-Predictor Variable
-Manipulated -"X" |
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Dependent Variable
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-Outcome Variable
-Observale behavior we're measuring in response to the IV -"Y" |
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Confounding Variables
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-Any variable that changes systematically with the IV
-Any uncontrolled extraneous variable that covaries with the IV and could provide an alternative explanation for the results -Causes poor internal validity |
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Constructs vs. Variables
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Construct - Not directly measurable concepts
Variables - Something we can measure |
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Subject Variables
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-Existing characteristics serve as variables
-Subject already possesses the thing you want to measure -Equivlalent groups is not gauranteed & could influence outcome -Cannot draw causation |
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Types of Variables
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Control-We do not allow to fluctuate
Random-Allow to fluctuate Confounding-Changes systematically with the IV Extraneous-Uncontrolled factors that are not of interest but may influence the DV |
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Hypothesis
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-Statement contain 2 or more variables that are measurable and specify how the variables are related
-Prediction about specific events that is derived from deduction -Educated guess about what should happen under certain circumstances |
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Empirical Questions
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-Those that can be answered through systematic observations & experiences that characterize scientific methodology
-Precise enough to allow specific predictions to be made MUST: -Answerable -Specific -Operational Definition -Leads to clear hypothesis -Asks ?'s we don't know answer -Theory Driven |
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Type I Error
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Rejecting the Null Hypothesis when it is in fact true - Found a significant difference in your study but there really isn't one
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Type II Error
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Failure to reject the Null when it is in fact false - You fail to find a significant difference in your study but there really was one
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Reliability
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How consistent is a measure over repeated applications
-Spread of scores cluster tight -How much error of measurement is associated with a measure |
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Measuring Reliability
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Single Administrations
-Split-Half -Internal Consistency -Interrater Multiple Administrations -Alternate Forms -Test-Retest |
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Measurement Validity
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Are we measuring what we intend to measure
-Constructs must be operationalized 4 Types: (1)Face Validity (2)Content (3) Criterion (4)Construct |
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Face Validity (1)
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Does it look like its measuring what it says its measuring
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Content Validity (2)
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Related to Face - Items reflect the area
-The more the items cover the relevant areas the more content validity |
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Criterion Validity (3)
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The degree to which a test is related to a criterion
How well does the measure predict outcomes based on info from other variables (1)Predictive (2)Concurrent |
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Construct Validity (4)
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Does the measure assess the construct it claims to assess
The degree to which a test is an accurate measure of the construct (1)Convergent - how is it similar to other measures (2)Discriminant - divergent (3)Nomological - |
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Experimenter Validity
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Approximation that a conclusion is true
-A set of standards by which research can be judged (1)Statistical Conclusion Valdity (2)Internal Valdity (3)Construct Validity (4)External Validity |
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Statistical Conclusion Validity (1)
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The extent to which the researcher uses statistics properly and draws the appropriate conclusions from the statistical analyses
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Internal Validity (2)
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The degree to which an experiment is methodologically sound and confound free
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Construct Validity (3)
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The adequacy of the definitions for the IV and DV
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External Validity (4)
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Generalizable
Can we generalize to: (1)Other persons/populations (2)Other environments (3)Other times |
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Experimental Validity is Best When
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-There is a relationship between the cause and effect
-The relationship is causal -You can generalize to the constructs -You can generalize to other persons, places, & times |
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Threats to Internal Validity
Pre-Post Tests |
-History
-Maturation -Regression to the mean -Testing Effects -Instrumentation Effects |
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Threats to Internal Validity
Participants |
-Sample Selection
-Attrition -Compare Groups |
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Operational Definition
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A definition of a concept or variable in terms of precisely described operations, measures, or procedures
-Defines a variable in terms of the techniques used to measure it |
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Between-Subject Design
What is It |
-Participants only receive 1 level of the IV
-Subject variables are almost always between-subjects -Cross-Sectional |
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Between-Subject Design
Advantage |
Subjects enter study fresh and naive
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Between-Subjects Design
Disadvantages & Error |
-Large # of people needed
-Time and energy -Individual Differences: Error-whenever there is a large difference between people there will be a large amount of error |
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Between-Subjects Design
Threats |
-Differential Attrition
-Diffusion -Compensatory Equalization -Compensatory Rivalry -Resentful Demoralization |
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Within-Subjects Design
What is it |
Every participant receives every condition or level of the IV
-Each group is assigned to each condition -longitudinal studies -repeated measures |
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Within-Subjects Design
Advantages |
-Smaller sample size
-Convenient -Use to study limited population -Avoids Error Variance |
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Within-Subjects Design
Disadvantages |
-Order/Sequence Effects
-Equivalent Groups -Time related factors -Attrition |
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Within-Subjects Design
Error |
Differences can be due to:
-IV -Systematic Error -Nonsystematic Error -Random Error |
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Experimenter Bias
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Experimenter Expectancy Effects
-experimenters may inadvertently do something that leads participants to behave in ways that confirm the hypothesis (a)Bio-Social Effects (b)Psycho-Social Effects (c)Situational Effects |
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Participant Bias
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Participants unconsciously modify their behavior to match expected results of the research
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Participant Bias
Hawthorne Effect |
Change behavior when they know they're being studied/observed
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Participant Bias
Demand Characteristics |
Any potential cues or features of a study that make the hypothesis obvious & influence participants to respond or behave in certain ways
(1)Good Subject (2)Negativistic Subject (3)Faithful Subject (4)Apprehensive Subject |
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Controlling Participant Bias
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-Deception
-Manipulation Check -Use small sample -Field Research |
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Single Blind Study
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Only experimenter knows which condition participant is in -
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Double Blind Study
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Neither the experimenter nor participant know who is getting which condition
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Single Factor Designs
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-1 IV with 2 or more levels
-Simplest experimental design -Between or Within Subjects Weaknesses: -Not impressive Strengths: -Simplistic |
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Single Factor Designs
4 Types |
Between Subjects
(1)Independent Groups - randomly assigned (2)Matched Groups - matched (3)Nonequivalent Groups - assignment is not random Within Subjects (1)Repeated Measures - uses counterbalancing |
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Single Factor Designs
Statistics |
T Test - analyze mean difference
For Two Levels: (1)t test for independent groups (2)t test for dependent groups More Than Two Levels: (1)1-way ANOVA (2)Post-Hoc Analysis |
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Factorial Designs
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-At least 2 IV's with 2 or more levels each
-Numerical System indicates # of IV's and levels in each -Factorial Matrix |
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Factorial Designs
Advantages |
-Main Effects
-Interactions How do factors operate independently & together to affect behavior |
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Factorial Designs
4 Types |
(1)Between Subject
(2)Within Subject (3)Mixed Factorial Design - 1 factor within, 1 between (4)SxM (SubjectxManipulated)- 1 subject variable, 1 manipulated variable |
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Factorial Designs
Statistics |
-N-way ANOVA
-N = # of IV's -F score for each main effect and each possible interaction -Post-Hoc Analysis |
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Main Effects
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Mean Effect
-Comparing overall means -The overall effect of a single IV -How do factors influence behavior simultaneously |
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Interactions
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-One factor modifies the effect of a second factor
-Factors are interdependent -Occurs when the effect of 1 IV depends on the level of another IV -When effects of a factor vary depending on the level of another factor, unique effects occur |
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Interactions
Not/Is |
Not an Interaction if:
-Main effects are additive -You can predict cell means Is an Interaction if: -Main effects are not additive -Extra means differences not explained by main effects -Below .05 is significant |
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Correlations
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-A numerical relationship between 2 variables
-When the goal of descriptive research is to test a hypothesis about the relationship between variables -No manipulation of variables -Implies Prediction -Predictor Variable -Criterion Variable |
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Correlations
3 Things To Consider |
(1)Directionality
-Positive -Negative -Curvilinear -No Relationship (2)Form -Linear -Monotonic (3)Strength Small = .10-.29 Moderate = .30-.49 Large = .50-1.00 |
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Correlations
Strength |
-Study what exists
-Variables that cannot be tested -Study many variables -High external validity |
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Correlations
Weaknesses |
-Directionality Problem- Does A cause B or does B cause A
-Third Variable-Another variable may be contributing to effect |
