curvilinear relationship

2 x 2 factorial design

has 2 independent variables,

each having 2 levels

2 x 3 factorial design

has 2 independent variables,

with variable A having 2 levels and variable B having 3 levels

2 x 2 x 2 factorial design

has 3 independent variables,

each with 2 levels

main effect

the direct effect of an

independent variable on a dependent variable

interaction

the differing effect of one independent variable on the dependent variable, depending on the particular level of another independent variable

moderator variable

a variable that influences the nature of the relationship between 2 variables

IV x PV design

allows researchers to investigate how different types of individuals respond to the same manipulated variable

independent group designs

each group has its own independent group of people

repeated measures design

1 group of people participating in all conditions

mixed factorial design using combined assignment

use of both independent groups and repeated measures procedures

single case experimental design

ABA designs

treatment is introduced after a baesline period and then withdrawn during a second baseline period

multiple baseline design

observing behavior before and after a manipulation under multiple circumstances

program evaluation steps

needs assessment

program theory assessment

process evaluation

outcome evaluation

efficiency assessment

needs assessment

ask whether there are problems that need to be addressed in a target population

program theory assessment

program must be based on valid assumptions about the causes of the problems

process evaluation

whether the program is reaching the target population, attracting enough clients, if staff is providing the planned services

outcome evaluation

are the intended outcomes of the program being realized; impact assessment

efficiency assessment

program has its intended effect; cost of program must be weighed against its benefits

quasi-experimental designs

design that approximates the control features of true experiments to infer that a given treatment did have its intended effect

interrupted time series design

examines the DV over an extended period of time, both before and after the IV is implemented

control series design

extension of the interrupted time series quasi-experimental design in which there is a comparison or control group

cross sectional method

persons of different ages are studied at only 1 point in time

longitudinal method

same group is observed at different times

sequential method

combination between longitudinal and cross sectional methods

3 ways to describe results

(1)comparing group percentage

(2)correlating individual scores

(3)comparing group means

frequency distributions

pie charts

histograms

bar graph

frequence polygons

variability

the amount of dispersion of scores about some central value

Pearson's r

correlation coefficient that indicates the strength and direction of relationships

effect size

the strength of association between variables

regression equations

calculations used to predict a person's score on one variable when the score on another variable is already known

y=a+bX

multiple correlations

used to combine a number of predictor variables to increase the accuracy of prediction of a given criterion or outcome variable

partial correlations

the correlation between 2 variables with the influence of the 3rd variable statistically controlled for

structural equation modeling

set of techniques used to examine models that specify a set of relationships among variables using the nonexperimental method

models

an expected pattern of relationships among a set of variables

path diagrams

a visual representation of the model being tested

path analysis

used to study modeling with arrows leading from variable to variable

inferential statistics

statistics designed to determine whether results based on sample data are generalizable to a population

null hypothesis

the hypothesis that the variables under investigation are not related in the population, that any observed effect based on sample results is due to a random error

research hypothesis

the hypothesis that the variables under investigation are related in the population, that the observed effect based on sample data is true in the population

statistical significance

rejection of null hypothesis when an outcome has a low probability of occurrence if, the null hypothesis is correct

type I errors

made when we reject the null hypothesis but the null hypothesis is actually true

type II errors

made when we accept the null hypothesis but the null hypothesis is false

power analysis

statistical test that determines optimal sample size based on probability of correctly rejecting the null hypothesis

issues with generalizing results

participants typically not selected from population

issues with studying

culture and race

research sometimes inappropriately regard differences in test results as artifacts of testing rather than of true differences across groups

issues with lab settings

allows experimenter to study the impact of IVs under highly controlled conditions

exact replications

an attempt to replicate precisely the procedures of a study to see whether the same results are obtained

conceptual replication

the use of different procedures to replicate a research finding

literature reviews

meta analysis

method for determining the reliability of a finding by examining the results from different studies