767 Midterm Notes (Imported) Flashcards Preview

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Flashcards in 767 Midterm Notes (Imported) Deck (294)
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Variables

symbols to which numerals/values are assigned

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Dichotomous/binary variables

only 2 values, i.e. 1 or 0, yes or no

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Polytomous variable

more than 2 values

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Continuous variable

taking on an ordered set of values within a certain range

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Categorical variable

grouped by either having or not having the characteristic of subset

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Independent variable

presumed cause of the dependent variable (antecedent)

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Dependent variable

the presumed effect (consequent)

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Manipulated variable/active variable/stimulus variable

any variable that is manipulated in experiment

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Measured variable/attribute variable/response variable

variables that can’t be manipulated are attribute/subject characteristic variables/organismic variables/individual differences

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Latent variable

unobserved, presumed to underlie observed measured variable, i.e. intervening or construct variable

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Characteristics of the scientific revolution of the 17th century

1) using observations to correct apparent errors rather than using to support theories
2) active observations, experiments
3) controlling for intervening variables, such as by random assignment or adding control groups

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Inus condition

a condition that is insufficient but non-redundant part of a unnecessary but sufficient condition; i.e. doesn’t need it for something to happen, but with it event will happen – a match leading to forest fire – most causes are more accurately called inus conditions

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Effect

the difference between what did happen and what would have happened; this follows the counterfactual model (Hume), something that is contract to fact; hence, what would have happened if the cause variable was not there? How are results different under that condition

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John Stuart Mill: causal relationship exists if

1) The cause preceded the effect
2) The cause was related to the effect
3) We can find no plausible alternative explanation for the effect other than the cause

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Correlation

does not prove causation; does not indicate which variable came first

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Causal description

describing the consequences attributable to deliberately varying a treatment; this is what experiments do better at than causal explanation; line is not clear

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Moderator variable

explains the conditions under which the effect holds

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Mediator variables

explains the causal effect

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Randomized experiment

Sir Ronald Fisher; various treatments being contrasted are assigned to experimental units by chance

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Quasi-experiment

lacks random assignment; uses self-selection or administrator selection

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Natural experiment

naturally occurring contrast between a treatment and comparison condition

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Nonexperimental designs/correlational design/passive observational design

situations in which a presumed cause and effect are identified and measured, but other structural features of experiments are missing; i.e. no random assignment, no design elements such as pretests and control groups

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How is strength of experimentation defined?

ability to illuminate causal inferences

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2 kinds of generalizations

1. Construct validity generalizations 2. External validity generalizations

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Construct validity generalizations

inferences about the constructs that research operations represent (representation)

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External validity generalizations

inferences about whether the causal relationship will remain with variations in persons, settings, etc (extrapolation)

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Grounded theory of causal generalization (scientists make causal generalizations by using 5 closely related principles):

1) Surface Similarity
2) Ruling out irrelevancies
3) Making discriminations
4) Interpolation and extrapolation
5) Causal explanation

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Surface Similarity

They assess the similarities between study operations and of the target

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Ruling out irrelevancies

identify those things that do not change a generalization

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Making discriminations

Clarify key discriminations that limit generalization