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58 Cards in this Set
 Front
 Back
Deception in Research

 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 

IRB

Institutional Review Board
Effective safeguard for participants, researchers, and universities Determines degree of risk Expedited or Formal Reviews 

Effective Literature Searches

 Compose narrative of search questions
 Identify seperate concepts in your question  Use APA thesaurus  Combine concept words in manner that best suits question 

Independent Variable

Predictor Variable
Manipulated "X" 

Dependent Variable

Outcome Variable
Observale behavior we're measuring in response to the IV "Y" 

Confounding Variables

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 

Constructs vs. Variables

Construct  Not directly measurable concepts
Variables  Something we can measure 

Subject Variables

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 

Types of Variables

ControlWe do not allow to fluctuate
RandomAllow to fluctuate ConfoundingChanges systematically with the IV ExtraneousUncontrolled factors that are not of interest but may influence the DV 

Hypothesis

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 

Empirical Questions

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 

Type I Error

Rejecting the Null Hypothesis when it is in fact true  Found a significant difference in your study but there really isn't one


Type II Error

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


Reliability

How consistent is a measure over repeated applications
Spread of scores cluster tight How much error of measurement is associated with a measure 

Measuring Reliability

Single Administrations
SplitHalf Internal Consistency Interrater Multiple Administrations Alternate Forms TestRetest 

Measurement Validity

Are we measuring what we intend to measure
Constructs must be operationalized 4 Types: (1)Face Validity (2)Content (3) Criterion (4)Construct 

Face Validity (1)

Does it look like its measuring what it says its measuring


Content Validity (2)

Related to Face  Items reflect the area
The more the items cover the relevant areas the more content validity 

Criterion Validity (3)

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 

Construct Validity (4)

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  

Experimenter Validity

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 

Statistical Conclusion Validity (1)

The extent to which the researcher uses statistics properly and draws the appropriate conclusions from the statistical analyses


Internal Validity (2)

The degree to which an experiment is methodologically sound and confound free


Construct Validity (3)

The adequacy of the definitions for the IV and DV


External Validity (4)

Generalizable
Can we generalize to: (1)Other persons/populations (2)Other environments (3)Other times 

Experimental Validity is Best When

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 

Threats to Internal Validity
PrePost Tests 
History
Maturation Regression to the mean Testing Effects Instrumentation Effects 

Threats to Internal Validity
Participants 
Sample Selection
Attrition Compare Groups 

Operational Definition

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 

BetweenSubject Design
What is It 
Participants only receive 1 level of the IV
Subject variables are almost always betweensubjects CrossSectional 

BetweenSubject Design
Advantage 
Subjects enter study fresh and naive


BetweenSubjects Design
Disadvantages & Error 
Large # of people needed
Time and energy Individual Differences: Errorwhenever there is a large difference between people there will be a large amount of error 

BetweenSubjects Design
Threats 
Differential Attrition
Diffusion Compensatory Equalization Compensatory Rivalry Resentful Demoralization 

WithinSubjects 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 

WithinSubjects Design
Advantages 
Smaller sample size
Convenient Use to study limited population Avoids Error Variance 

WithinSubjects Design
Disadvantages 
Order/Sequence Effects
Equivalent Groups Time related factors Attrition 

WithinSubjects Design
Error 
Differences can be due to:
IV Systematic Error Nonsystematic Error Random Error 

Experimenter Bias

Experimenter Expectancy Effects
experimenters may inadvertently do something that leads participants to behave in ways that confirm the hypothesis (a)BioSocial Effects (b)PsychoSocial Effects (c)Situational Effects 

Participant Bias

Participants unconsciously modify their behavior to match expected results of the research


Participant Bias
Hawthorne Effect 
Change behavior when they know they're being studied/observed


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 

Controlling Participant Bias

Deception
Manipulation Check Use small sample Field Research 

Single Blind Study

Only experimenter knows which condition participant is in 


Double Blind Study

Neither the experimenter nor participant know who is getting which condition


Single Factor Designs

1 IV with 2 or more levels
Simplest experimental design Between or Within Subjects Weaknesses: Not impressive Strengths: Simplistic 

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 

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)1way ANOVA (2)PostHoc Analysis 

Factorial Designs

At least 2 IV's with 2 or more levels each
Numerical System indicates # of IV's and levels in each Factorial Matrix 

Factorial Designs
Advantages 
Main Effects
Interactions How do factors operate independently & together to affect behavior 

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 

Factorial Designs
Statistics 
Nway ANOVA
N = # of IV's F score for each main effect and each possible interaction PostHoc Analysis 

Main Effects

Mean Effect
Comparing overall means The overall effect of a single IV How do factors influence behavior simultaneously 

Interactions

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 

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 

Correlations

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 

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 = .501.00 

Correlations
Strength 
Study what exists
Variables that cannot be tested Study many variables High external validity 

Correlations
Weaknesses 
Directionality Problem Does A cause B or does B cause A
Third VariableAnother variable may be contributing to effect 