RMP Exam 2

experiment

a study where there are only independent variables

conditions/levels

the groups formed by the manipulation

Control group

the group that remains unaffected by manipulation

placebo group

group that remains unaffected by manipulation but think they are

constant

something that does not vary in the study

covariation

two variables must go together

temporal precedence

the predictor variable must come before the DV

third variable criterion

ability to eliminate all possible alternative explanations for results

random assignment/confounds

manipulation check

designed to see how well the manipulation worked

pilot study

tests a new manipulation to decide whether it is appropriate or not

main effect

the effect of predictor variable on DV and IV ignoring other predictor variables (each predictor has its own main effect)

interaction

the effect of one predictor variable on the DV which depends on the level of another predictor variable (two predictors together produce and interaction)

correlation

a statistical analysis which tests the association between two variables (pearsons’ r) STAT ANALYSIS

correlational study

all predictors are measured METHODOLOGICAL APPROACH

statistical significance

the likelihood of getting our data by chance ASSUMING there is no effect in our population

effect size

strength of magnitude of a relationship

larger effect size are often but not always more important

Restriction of range

having a limited range on one variable can make relationships look weaker than they are

internal validity

the strength of the claim that the predictor variable is causing change in the DV

includes:

covariation

temporal precedence

third variable criterion

matched design/matched groups

participants who are similar to some important measure are grouped into sets and then members of each set are randomly assigned to different conditions

potential disadvantages are losing two participants if one drops out

pretest/posttest design

participants are tested on the key DV twice, once before and after manipulation

potential disadvantages are that it lacks internal validity

within groups design

each participant experiences all levels of the IV

potential disadvantages are the carryover effect

order effects

one experience or level of the IV can effect the others

demand effects

cues that lead participants to guess the study’s purpose

counter balancing

presenting levels of IV to participants in different orders to control order effects

Quasi experiment

one measured and one manipulated predictor variable

statistics

math procedures to interpret info

parameter

a numerical value we use to describe a population- does not vary

a statistic

a numerical value we use to describe a sample- this does vary

sampling error

differences that exist from one sample to another

descriptive statistics

numerical and graphical ways to analyze data including mean, median and mode used to simplify and organize data

graphs, correlation and effect size, z-score percentile

central tendency

inferential statistics

uses sample info to make estimates, conclusions and predictions about population

null hypothesis, type I & II error, power, types of tests

central tendency

descriptive stats that define the center of distribution

mean, mode, median

variability

describes the distribution

estimates distance between scores

insight of how well individual score will represent population

how much error to expect

variance

average deviation ² distance from the mean

standard deviation

average distance or deviance score from the mean

P<0.05

reject the null, data is statistically significant

P>0.05

fail to reject null, data is not statistically significant

type I error

false positive

type II error

false negative

power

the probability that the test will correctly reject the null

inferential tests

chi squared

correlational

independent t test

one way ANOVA

factorial ANOVA

replication

conducting a study again and getting the same results

replication plus extension

has additional variables to original study

conceptual replication

has same conceptual variables but new operational

meta-analysis

quantitative review of effect

pro: precise estimate of the effect

con: varies across different populations

floor effects

data is clustered at the bottom of the scale

ceiling effect

data is clustered at the top of the scale

noise

too much variability associated with predictors

construct validity

how well a conceptual variable is operationalized

external validity

how well the results generalize to other samples/contexts

statistical validity

the strength of the results and how they are interpreted