fundamentals of behavioral research chapter 12 experimental design: one-way correlated samples...

Fundamentals of Behavioral Research

Chapter 12

Experimental Design: One-Way Correlated

Samples Design


Experimental Design: One-Way Correlated Samples Design

Advantages and limitations Comparing two groups Comparing t-test to ANOVA Comparing more than two groups


Advantages and limitations

One-way correlated samples One-way = 1 IV Correlated samples = no random assignment

Each score in one group (condition) is paired with a score in the other group(s) (condition(s))

Advantages Can reduce systematic error (confounding) Can reduce random error (due to indiv diff)

Limitations Creating pairs of participant scores may be

difficult Repeated measurements can create

methodological concerns



Natural pairs Participants’ scores paired for some natural

reason Matched pairs

Participants’ scores paired because researcher matches them on some variable

Repeated measures Participants’ scores paired because they come

from the same participants Objective is to reduce sources of

extraneous variability



Advantages of repeated measures design Controls EVs due to individual

differences Requires fewer participants Appropriate for studying questions that

involve repeated exposure/testing Appropriate for longitudinal research



Methodological issues of repeated measures design Carryover effects

Transient Permanent Sensitization

Carryover effects can often be controlled by:

Randomized order of conditions counterbalancing



Comparing repeated measures design to independent samples design Effect on random error and inferential

statistic Effect on degrees of freedom Consider the net effect


Comparing two groups

Random sampling Paired assignment to 2 groups

(conditions) 1 IV with 2 levels

Let’s try an experiment involving the Stroop effect Go to the following website: http://faculty.washington.edu/chudler/java/ready.html


Comparing two groups

variability within groups (error variability) = random error (extraneous variables)

variability between groups = systematic error (confounds) + systematic variability (effect of IV)

Goals: Reduce random error Eliminate systematic error Maximize systematic variability through

manipulation of IV


Comparing t-test to ANOVA

Correlated samples t-test Limited to 2 groups

Independent samples Analysis of Variance (ANOVA) 2 or more groups

Both parametric tests Require assumptions of:

Normality Homogeneity of variance



Correlated samples t-test difference between the 2 group means t = ----------------------------------------------------------

standard error of the difference between means

t values when null hypothesis is true t values when null hypothesis is false Larger the t (pos or neg), the lower the

probability that the difference is simply due to chance

Alpha level and decision-making



Correlated samples ANOVA variability between the two groups F = ----------------------------------------------------------

error variability

F values when null hypothesis is true F values when null hypothesis is false Larger the F, the lower the probability

that the difference is simply due to chance

Alpha level and decision-making


Comparing more than 2 groups

Addition of groups often clarifies relationship between IV and DV

ANOVA to determine effect A priori specific comparison test

Does not require significant F post hoc specific comparison test

Does require significant F


Summary

Correlated samples design Random sampling Paired assignment

Natural pairs Matched pairs Repeated measures

Paired assignment designed to reduce random error

Manipulation of IV Analyzed with t-test or ANOVA

fundamentals of behavioral research chapter 12 experimental design: one-way correlated samples...

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