# PSYC 2360 Study Guide - Statistical Conclusion Validity, Null Hypothesis, Internal Validity

by OC73429

Things to Remember - Methods Final Exam

Two Factor Designs

when we have 2 independent variables each with 2+ levels

we calculate an F statistic like in one-way ANOVAs where

o

, if the null hypothesis is rejected

that means that F > 1 and there is a significant difference

between the groups larger than what we would expect if it

was due to chance alone. If the null hypothesis is not

rejected, F = 1 therefore there was not a big enough

difference between the groups to be considerably different

than what we would expect if due to chance

marginal means are when we calculate the mean of the entire row

or column

main effects are when we look to see if the marginal means are the

same or different for a given factor

o if there is a significant main effect (the marginal means for

the factor’s levels are different) we can say that that factor

has a significant effect on the dependent variable, ignoring

the effects of the other factor

an interaction is when the effects of one factor on the dependent

variable depends on the level of the other factor (when the lines

intersect)

o to see if we have an interaction we do decomposition and

see if we can get back the cell means by only knowing the

marginal means and the grand mean

a) get the grand mean (mean of all cells)

b) subtract grand mean from each cell

c) get the new marginal mean of the cells

d) add the marginal means and the grand mean and see if

you get the cell means back - if you do, there is no

interaction - if you do not, the leftovers are the

interaction

o when we have a significant interaction it just tells us there is

a difference between the means but it does not tell us the

pattern of the means, therefore we use simple main effects

simple main effects are when we look at the effect of a factor on

the dependent variable across a level of the other factor

o when we have a significant interaction, the main effects

should be interpreted with caution because the effect of one

factor DEPENDS on the level of the other

if we were to do a three way interaction (3 independent variables)

it gets a lot more complex…

o 1 three way interaction

o 3 two way interactions

o 3 main effects

many repeated measures designs are factorial and have the need

for counterbalancing

o e.g. Latin square design (each condition appears in each

order and equally follows each other condition)

when you have more than 2 levels in the factor, a simple main

effect will not tell you where the differences are within the means

therefore we need to conduct mean comparisons

o pairwise comparisons - we compare any mean with any other

mean, causes type 1 error probability to increase

o planned or priori comparisons - we compare two means that

we had expected would be different

o posthoc comparisons - we only do comparisons if we have a

significant effect

o complex comparisons - we compare more than 2 means at a

time

Internal Validity

there are a few threats to validity

o construct validity - is the test measuring what it is supposed

to measure

o statistical conclusion validity - is our conclusion valid

o internal validity - the extent to which we can say that our

independent variable is causing the dependent variable

o external validity - the extent to which our findings generalize

experimental control is when we try to make sure and control as

many variables as we can - the greater experimental control, the

more internally valid

o we can have extraneous variables that noise the data and

cause random error however we can still have an experiment

that is internally valid with extraneous variables

o we can also have confounding variables in which we cannot

say that our test is internally valid because confounding

variables always produce an alternative pathway and we have

no way of knowing which pathway is causing the change in

the dependent variable

since people are different, there are different designs we can do in

order to control for as many people differences as we can

o limited populations design - similar to convenience sampling

where we sample from population like university in which

subjects will be similar to each other (homogeneous) in a

variety of factors, this increases our statistical power by

decreasing the effects of extraneous variables

o before and after designs - are similar to repeated measures

designs except the group is only in one condition and the

dependent variable is measured twice - once before the

manipulation is presented and once after the manipulation

and participants serve as their own controls, this still has the

problems of retesting effects such as fatigue, practice,

guessing the research hypothesis

o matched groups designs are when the sample are measured

on a variable and then based on these scores, are put into

groups, this works as long as the variable they are being

scored on is related to the dependent variable, often this is

not necessary and random assignment is enough, this

reduces the differences between the groups and increases

the power of the test

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