INTRO TO EXPERIMENTAL RESEARCH, continued Lawrence R. Gordon Psychology Research Methods I.

INTRO TO EXPERIMENTAL RESEARCH, continued

Lawrence R. Gordon

Psychology Research Methods I

“TRUE” EXPERIMENTS

Investigate the “effect(s) of X(s) on Y(s)” At least one IV is manipulated (X) with two or

more “levels” All extraneous variables are controlled At least one DV is measured (Y)

SIMPLEST EXPERIMENT: one IV with two levels and one measured DV

Independent Variables Two or more “levels” (conditions, treatments) Types:

– Situational variables

– Task variables

– Instructional variables

– “Control groups” - not all have one

Expression:– Manipulated variables (above) - assigned to participants

– Subject variables - participants selected for (‘ex post facto’ study if none manipulated).

Extraneous Variables

Variables NOT of interest to our research question

But if they covary with the IV, become a “confound” or an “alternative” or “rival” explanation for effect on DV

Control procedures are all about this -- next chapter

Goodwin p. 152 has excellent tables for this; try to “fix” the example shown!

Dependent Variables

“...some characteristic of behavior or reported experience” (Woodworth, 1938)

Measured Considerations

– Operational definition of construct– Reliability and validity of measurement

A Simple Experiment: “Time Flies”

EXAMPLE: “Time flies when you’re having fun”

Hypothesis: IF one is “having more fun”, THEN time will seem to pass more quickly

Design: • IV: 100 persons randomly assigned to two groups:

– 1: “Having more fun”

– 2: “Having less fun”

– Procedure: manipulation of cartoon captions

• DV: Estimate of a standard 10 minute interval

A Simple Experiment, cont.

Results (cont.)– Group 1 = “More fun”

• Mean = 8.60, SD = 2.72, N = 50

– Group 2 = “Less fun”• Mean = 12.48, SD = 3.35, N = 50

– Quickie summary of results: the “More fun” group gave shorter estimates of the 10-minute interval, on average, than the “Less fun” group.

Can you think of any possible confounds, or alternative explanations for this effect?

Research Validity

How do we know we’re answering the question we asked?

Statistical conclusion validity Construct validity External validity Internal validity

External Validity

Generalization to– other populations? who– Other environments? where– Other occasions? When

Affects the scope of our inference; usually addressed in “discussion” of the research

Often the target when adding additional IVs

Internal Validity

Does X indeed cause Y? Analysis of possible confounds There are special “threats” to internal

validity that the “design” of research attempts to address– Affecting Pre-Post research– Concerning our participants

Threats to Internal Validity Affecting Pre-Post research

– Pre X Y Post X– History and maturation– Regression to the mean– Testing and instrumentation

Major solutions– Eliminate Pre-X (“Posttest only design”)– Add control condition without Y:

• Pre X Y Post X

• Pre X Post X

Threats to Internal Validity Concerning our participants

– Subject selection (are groups equivalent?)– Section by Other interactions (Sel History)– Attrition (“mortality”)

Wrap up: The Bower Experiment

IV(s)? DV(s)? EVs?

Results Problems?

Bower: Histograms (F’02)

Number of Pictures Remembered

16.014.012.010.08.06.04.0


CONDITIO: 1 No Words

Fre

qu

en

cy

40

30

20

10

0

Std. Dev = 2.71

Mean = 8.8

N = 95.00


14.012.010.08.06.04.0


CONDITIO: 2 With Words

Fre

qu

en

cy

40

30

20

10

0

Std. Dev = 2.23

Mean = 8.7

N = 99.00

Number of Pictures Incorrect

6.04.02.00.0


CONDITIO: 1 No Words

Fre

qu

en

cy

60

50

40

30

20

10

0

Std. Dev = 1.05

Mean = .7

N = 95.00


1.00.500.00


CONDITIO: 2 With Words

Fre

qu

en

cy

100

80

60

40

20

0

Std. Dev = .34

Mean = .13

N = 99.00

Number correctly recalled

12.010.08.06.04.02.0

Number of Pictures Rembered

CONDITION: 1.00 Without Words

Fre

qu

en

cy

40

30

20

10

0

Std. Dev = 2.36 Mean = 7.0N = 97.00

Number incorrectly recalled

4.03.02.01.00.0


CONDITION: 1.00 Without Words

Fre

qu

en

cy

50

40

30

20

10

0

Std. Dev = 1.07 Mean = .9N = 97.00

Number correctly recalled

14.012.010.08.06.04.0

Number of Pictures Correct

CONDITION: 2.00 With Words

Fre

qu

en

cy

40

30

20

10

0

Std. Dev = 2.40 Mean = 8.3N = 97.00

Number incorrectly recalled

5.04.03.02.01.00.0


CONDITION: 2.00 With Words

Fre

qu

en

cy

80

60

40

20

0

Std. Dev = .80 Mean = .3N = 97.00

BOWER: Descriptives (F’02)

Report

8.79 .74

2.71 1.05

95 95

8.74 .13

2.23 .34

99 99

8.76 .43

2.47 .83

194 194

Mean

Std. Deviation

N

Mean

Std. Deviation

N

Mean

Std. Deviation

N

ConditionNo Words

With Words

Total

Number ofPictures

Remembered

Number ofPicturesIncorrect

Bower Experiment Replication Descriptive Statistics

7.0309 .9072

2.35608 1.07124

97 97

8.2577 .3299

2.40346 .80002

97 97

7.6443 .6186

2.45209 .98637

194 194

Mean

Std. Deviation

N

Mean

Std. Deviation

N

Mean

Std. Deviation

N

ConditionWithout Words

With Words

Total

Number ofPictures

Rembered

Number ofPicturesIncorrect

BOWER: Inferential (a peek) (F’02)

BOWER EXPERIMENT: Compare Groups on Each of Two DVs

.146 192 .884

5.431 192 .000



t df Sig. (2-tailed)

t-test for Equality of Means

“Significant difference” if “Sig (2-tailed)” is <.05

Independent Samples Test

192 .000 -1.2268

192 .000 .5773

number correct

number incorrect

df Sig. (2-tailed)Mean

Difference

t-test for Equality of Means

CORRELATION: The Problem

Are two variables related?– Does one increase as the other increases?

• e. g. skills and income

– Does one decrease as the other increases?• e. g. health problems and nutrition

How can we get a numerical measure of the degree of relationship? SPSS, for now...

Correlation: A Quick Introduction

Descriptive: “Pearson product-moment correlation coefficient,” r

Values: -1 __________ 0 _________ +1

Correlation Coefficient

A measure of degree of relationship. Sign refers to direction. Based on covariance

– Measure of degree to which large scores go with large scores, and small scores with small scores



Values: -1 __________ 0 _________ +1 Visualization: SCATTERPLOTS

Cigarette Consumption per Adult per Day

12108642

CH

D M

ort

ality

per

10,0

00

30

20

10

0

{X = 6, Y = 11}

N=19 data pairs

What Does the Scatterplot Show?

As smoking increases, so does coronary heart disease mortality.

Relationship looks strong Not all data points on line.

– These are “residuals” or “errors of prediction”



Values: -1 __________ 0 _________ +1 Visualization: SCATTERPLOTS Inferential -- is the computed r unlikely from a

population with a correlation of 0? EXAMPLES -- from Bower & Class Survey

BOWER, revisited, F’02 Are Correct vs.

Incorrect recall related?

That is, do they predict one another “really”?

YES - why?

Bower: Correct vs. Incorrect

Correlations

-.116

.111

190

Pearson Correlation

Sig. (2-tailed)

N

TVWATCHGPA

Correlations

-.382**

.000

194

Pearson Correlation

Sig. (2-tailed)

N

# Correct

#Incorrect

Correlation is significant at the 0.01 level(2-tailed).

**.

Number Incorrect

6543210-1

Num

ber C

orre

ct

14

12

10

8

6

4

2

0

CLASS SURVEY 2002 Are the scales measuring

Need for Cognition and Concern for Future Consequences related?

That is, do they predict one another “really”?

YES - why?

CFC Scale by NC Scale

Need for Cognition Scale

10090807060504030

Conc

ern

for F

utur

e Co

nseq

uenc

es S

cale

60

50

40

30

20

Correlations

.395**

.000

174

Sig. (2-tailed)

N

CFCSCALENCSCALE

Correlation is significant at the 0.01 level(2-tailed).

**.

INTRO TO EXPERIMENTAL RESEARCH, continued Lawrence R. Gordon Psychology Research Methods I.

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Transcript of INTRO TO EXPERIMENTAL RESEARCH, continued Lawrence R. Gordon Psychology Research Methods I.