non-experimental designs psych 231: research methods in psychology

Non-Experimental designs

Psych 231: Research Methods in Psychology

Exam 2

Mean = 76 Max = 94 Min = 48

Exam 2

Most common area of errors between/within manipulations interactions/main effects validity/reliability

If you’d like to go over your exam, contact me and we’ll set up a time to go through it


Sometimes you just can’t perform a fully controlled experiment Because of the issue of interest Limited resources (not enough subjects, observations are too

costly, etc). • Surveys

• Correlational

• Quasi-Experiments

• Developmental designs

• Small-N designs

This does NOT imply that they are bad designs Just remember the advantages and disadvantages of each

Correlational designs

Looking for a co-occurrence relationship between two (or more) variables Example 1: Suppose that you notice that the

more you study for an exam, the better your score typically is. This suggests that there is a relationship between

study time and test performance. We call this relationship a correlation.

3 properties: form, direction, strength

Strength

r = 1.0“perfect positive corr.”

r = -1.0“perfect negative corr.”

r = 0.0“no relationship”

-1.0 0.0 +1.0

The farther from zero, the stronger the relationship


Looking for a co-occurrence relationship between two (or more) variables

Example 1: Suppose that you notice that the more you study for an exam, the better your score typically is

Explanatory variables (Predictor variables) Response variables (Outcome variables)

For our example, which variable is explanatory and which is response? And why?

It depends on your theory of the causal relationship between the variables

Scatterplot

Y

X

1

2

3

4

5

6

1 2 3 4 5 6

Response (outcome) variable

Explanatory (predictor) variable

For descriptive case, it doesn’t matter which variable goes where

Correlational analysis

For predictive cases, put the response variable on the Y axis

Regression analysis


Advantages: Doesn’t require manipulation of variable

• Sometimes the variables of interest can’t be manipulated Allows for simple observations of variables in

naturalistic settings (increasing external validity) Can look at a lot of variables at once

Disadvantages: Don’t make casual claims

• Third variable problem

• Temporal precedence

• Coincidence (random co-occurence)


Correlational results are often misinterpreted

Misunderstood Correlational designs

Disadvantages: Example 2: Suppose that you notice that kids

who sit in the front of class typically get higher grades. This suggests that there is a relationship between

where you sit in class and grades.

Daily Gazzett

Children who sit in the back of the classroom receive lower grades than those who sit in the front.

Possibly implied: “[All] Children who sit in the back of the classroom [always] receive worse grades than [each and every child] who sits in the front.”

Better: “Researchers X and Y found that children who sat in the back of the classroom were more likely to receive lower grades than those who sat in the front.”

Example from Owen Emlen (2006)




• Correlational



• Small-N designs


Quasi-experiments

What are they? Almost “true” experiments, but with an inherent

confounding variable

General types• An event occurs that the experimenter doesn’t

manipulate• Something not under the experimenter’s control

• (e.g., flashbulb memories for traumatic events)

• Interested in subject variables• high vs. low IQ, males vs. females

• Time is used as a variable

Quasi-experiments

Program evaluation– Research on programs that is implemented to achieve

some positive effect on a group of individuals.– e.g., does abstinence from sex program work in schools

– Steps in program evaluation– Needs assessment - is there a problem?– Program theory assessment - does program address the

needs?– Process evaluation - does it reach the target population? Is it

being run correctly?– Outcome evaluation - are the intended outcomes being

realized?– Efficiency assessment- was it “worth” it? The the benefits

worth the costs?

Quasi-experiments

Nonequivalent control group designs with pretest and posttest (most common)

(think back to the second control lecture)

participants

Experimentalgroup

Controlgroup

Measure

Measure

Non-Random Assignment

Independent Variable

Dependent Variable

Measure

Measure

Dependent Variable

– But remember that the results may be compromised because of the nonequivalent control group (review threats to internal validity)

Quasi-experiments

Advantages Allows applied research when experiments not

possible Threats to internal validity can be assessed

(sometimes) Disadvantages

Threats to internal validity may exist Designs are more complex than traditional

experiments Statistical analysis can be difficult

• Most statistical analyses assume randomness




• Correlational



• Small-N designs


Developmental designs

Used to study changes in behavior that occur as a function of age changes Age typically serves as a quasi-independent

variable Three major types

Cross-sectional Longitudinal Cohort-sequential


Cross-sectional design Groups are pre-defined on the basis of a pre-

existing variable • Study groups of individuals of different ages at the

same time• Use age to assign participants to group

• Age is subject variable treated as a between-subjects variable

Age 4

Age 7

Age 11

Cross-sectional design


Advantages:• Can gather data about different groups (i.e., ages)

at the same time• Participants are not required to commit for an

extended period of time

Cross-sectional design


Longitudinal design


Follow the same individual or group over time• Age is treated as a within-subjects variable

• Rather than comparing groups, the same individuals are compared to themselves at different times

• Changes in dependent variable likely to reflect changes due to aging process• Changes in performance are compared on an

individual basis and overall

Age 11

time

Age 20Age 15

Longitudinal Designs

Example Wisconsin Longitudinal Study (WLS)

• Began in 1957 and is still on-going (50 years)• 10,317 men and women who graduated from Wisconsin high schools

in 1957

• Originally studied plans for college after graduation• Now it can be used as a test of aging and maturation

Longitudinal design


Advantages:• Can see developmental changes clearly• Can measure differences within individuals• Avoid some cohort effects (participants are all from

same generation, so changes are more likely to be due to aging)

Longitudinal design


Disadvantages• Can be very time-consuming• Can have cross-generational effects:

• Conclusions based on members of one generation may not apply to other generations

• Numerous threats to internal validity:• Attrition/mortality

• History

• Practice effects• Improved performance over multiple tests may be due to

practice taking the test

• Cannot determine causality


Measure groups of participants as they age• Example: measure a group of 5 year olds, then the

same group 10 years later, as well as another group of 5 year olds

Age is both between and within subjects variable

• Combines elements of cross-sectional and longitudinal designs

• Addresses some of the concerns raised by other designs• For example, allows to evaluate the contribution of cohort

effects

Cohort-sequential design


Cohort-sequential designTime of measurement

1975 1985 1995

Cohort A

Cohort B

Cohort CCro

ss-s

ectio

nal c

ompo

nent

1970s

1980s

1990s

Age 5 Age 15 Age 25

Age 5 Age 15

Age 5

Longitudinal component


Advantages:• Get more information

• Can track developmental changes to individuals• Can compare different ages at a single time

• Can measure generation effect• Less time-consuming than longitudinal (maybe)

Disadvantages:• Still time-consuming• Need lots of groups of participants• Still cannot make causal claims

Cohort-sequential design

Small N designs

What are they? Historically, these were the typical kind of design

used until 1920’s when there was a shift to using larger sample sizes

Even today, in some sub-areas, using small N designs is common place

• (e.g., psychophysics, clinical settings, expertise, etc.)

Small N designs

One or a few participants Data are typically not analyzed statistically; rather rely

on visual interpretation of the data Observations begin in the absence of treatment

(BASELINE) Then treatment is implemented and changes in

frequency, magnitude, or intensity of behavior are recorded

Small N designs

Baseline experiments – the basic idea is to show:

1. when the IV occurs, you get the effect

2. when the IV doesn’t occur, you don’t get the effect (reversibility)

Before introducing treatment (IV), baseline needs to be stable

Measure level and trend

Small N designs

Level – how frequent (how intense) is behavior? Are all the data points high or low?

Trend – does behavior seem to increase (or decrease) Are data points “flat” or on a slope?

ABA design

ABA design (baseline, treatment, baseline) A B A

Steady state (baseline) | Transition steady state | Reversibility

– The reversibility is necessary, otherwise something else may have caused the effect other than the IV (e.g., history, maturation, etc.)

Small N designs

Advantages Focus on individual performance, not fooled by

group averaging effects Focus is on big effects (small effects typically can’t

be seen without using large groups) Avoid some ethical problems – e.g., with non-

treatments Allows to look at unusual (and rare) types of subjects

(e.g., case studies of amnesics, experts vs. novices) Often used to supplement large N studies, with more

observations on fewer subjects

Small N designs

Disadvantages Effects may be small relative to variability of situation

so NEED more observation Some effects are by definition between subjects

• Treatment leads to a lasting change, so you don’t get reversals

Difficult to determine how generalizable the effects are

Small N designs

Some researchers have argued that Small N designs are the best way to go.

The goal of psychology is to describe behavior of an individual

Looking at data collapsed over groups “looks” in the wrong place

Need to look at the data at the level of the individual

non-experimental designs psych 231: research methods in psychology

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