profile analysis how to

7/30/2019 Profile Analysis How To

1/7

Paul Cook

11/9/05

Profile Analysis in SPSS

To perform a profile analysis, you need to first start with a dataset formatted in a specificway you need one line of data for each participant, with repeated measures (or multiple

DVs) all measured on the same scale, and in different columns for each participant. Then

you need at least one fixed factor (grouping variable) that you would like to use forcomparing subgroups of participants. In this example, the repeated-measures factor

(many DVs measured on the same scale) is trial, divided in to trial 1 through trial 4

for the same measure taken at different time points. We will use anxiety as the fixedfactor in this example. Technically, trial (values 1-4) is going to be treated as an IV,

and the scores within the various trial columns will be treated as the DV. Anxiety

will be treated as an additional (grouping) IV.


2/7

Paul Cook

11/9/05

For Profile Analysis, use the General Linear Model Repeated Measures command

in SPSS (note: this example is computationally identical to the Repeated-Measures

ANOVA example that can also be found on the Virtual CNR website).

This pop-up box appears at the start of the analysis. This box lets you define your within-subjects factor (the repeated-measures variable). Give the repeated-measures variable aname (like time, trial, or measurement scale). Next, specify how many different

levels the repeated-measures variable has (i.e., how many different observations on the

repeated-measures variable for each individual participant?). Finally, you need to hit theAdd button to add this repeated-measures variable to the list. The name you gave this

variable should appear in the middle white box. Finally, hit the Define button to go on.


3/7

Paul Cook

11/9/05

The next dialog box looks more like the usual ANOVA dialog. In this box,

1. You will see some lines with question marks for your repeated-measures variable.There should be as many lines as you specified there were levels of this variable

in the previous dialog box (e.g., 4 levels = 4 empty lines here). Take the variablenames of the columns in SPSS where you have stored your repeated-measures

data, and move them over to fill in the blanks in this top box.

2. Select your other IV (the grouping variable), and move it to the box labeledBetween-Subjects Factors.

3. Click the Plots button to see the next dialog box that you need.


4/7

Paul Cook

11/9/05

In the Plots dialog box, put yourrepeated-measures variable on the horizontal axis, andcreate separate lines for your grouping variable. You need to be sure to click the add

button to actually see the plot in SPSS. Once you do, it will show up in the list at the

bottom of this dialog box.

Click the Continue button to return to the main dialog box. Finally, click the OK

button in the main dialog box to run your analysis.

Here is the output from this test:

Multivariate Testsb

.961 64.854a 3.000 8.000 .000

.039 64.854a 3.000 8.000 .000

24.320 64.854a 3.000 8.000 .000

24.320 64.854a 3.000 8.000 .000

.479 2.451a 3.000 8.000 .138

.521 2.451a 3.000 8.000 .138

.919 2.451a 3.000 8.000 .138

.919 2.451a 3.000 8.000 .138

Pillai's Trace

Wilks' Lambda

Hotelling's Trace

Roy's Largest Root

Pillai's Trace

Wilks' Lambda

Hotelling's Trace

Roy's Largest Root

Effect

trial

trial * anxiety

Value F Hypothesis df Error df Sig.

Exact statistica.

Design: Intercept+anxiety

Within Subjects Design: trial

b.

The first section of the output shows multivariate tests for trial, which tell you whether theresults changed over time or across the different DVs. This is the profile analysis test forFlatness. The interaction between trial * anxiety tells you if there were different patternsof results for each level of the anxiety IV this is the profile analysis test forParallelism.


5/7

Paul Cook

11/9/05

Mauchly's Test of Sphericityb

Measure: MEASURE_1

.283 11.011 5 .053 .544 .701 .333

Within Subjects Ef

trial

auchly's W

Approx.

Chi-Square df Sig.

Greenhous

e-Geisser Huynh-Feldt ower-bound

Epsilona

Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed depend

proportional to an identity matrix.

May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected t

the Tests of Within-Subjects Effects table.

a.

Design: Intercept+anxiety

Within Subjects Design: trial

b.

This next section has a test for violation of the sphericity assumption. You want to see a

non-significant value here (p > .05, which we have barely), because that means theassumption has notbeen violated.

Tests of Within-Subjects Effects

Measure: MEASURE_1

991.500 3 330.500 128.627 .000

991.500 1.632 607.468 128.627 .000

991.500 2.102 471.773 128.627 .000

991.500 1.000 991.500 128.627 .000

8.417 3 2.806 1.092 .368

8.417 1.632 5.157 1.092 .346

8.417 2.102 4.005 1.092 .357

8.417 1.000 8.417 1.092 .321

77.083 30 2.569

77.083 16.322 4.723

77.083 21.016 3.668

77.083 10.000 7.708

Sphericity Assumed

Greenhouse-Geisser

Huynh-Feldt

Lower-bound

Sphericity Assumed

Greenhouse-Geisser

Huynh-Feldt

Lower-bound

Sphericity Assumed

Greenhouse-Geisser

Huynh-Feldt

Lower-bound

Source

trial

trial * anxiety

Error(trial)

Type III Sum

of Squares df Mean Square F Sig.

This next table gives you alternate statistics that you can use if the sphericity assumptionwas notmet the Greenhouse-Geisser and Huynh-Feldt tests are alternatives to Wilkslambda when the data are not spherical.


6/7

Paul Cook

11/9/05

Tests of Within-Subjects Contrasts

Measure: MEASURE_1

984.150 1 984.150 190.051 .000

6.750 1 6.750 4.154 .069

.600 1 .600 .663 .434

1.667 1 1.667 .322 .583

3.000 1 3.000 1.846 .204

3.750 1 3.750 4.144 .069

51.783 10 5.178

16.250 10 1.625

9.050 10 .905

trial

Linear

Quadratic

Cubic

Linear

Quadratic

Cubic

Linear

Quadratic

Cubic

Source

trial

trial * anxiety

Error(trial)

Type III Sum


This table shows you some alternate tests for non-linear trends in the data. The trial results aresimilar to the test for flatness (a linear trend), except that they test for curvilinear patterns of

change over time or across the different DVs.

Tests of Between-Subjects Effects

Measure: MEASURE_1

Transformed Variable: Average

4800.000 1 4800.000 280.839 .000

10.083 1 10.083 .590 .460

170.917 10 17.092

Source

Intercept

anxiety

Error

Type III Sum


The final table in this section shows you the significance test for the effect of groupmembership by itself this is the third piece of information provided by profile analysis, atest of differences in the means between groups.


7/7

Paul Cook

11/9/05

4321

trial

18

15

12

9

6

3

EstimatedMarginalMeans

2

1Anxiety

Estimated Marginal Means of MEASURE_1

The last item on the printout is the plot that we asked for a lot of interpretation in profileanalysis can be done using graphical methods. The downward trend in these linescorresponds to the significant effect of trial (change over time the flatness test) andthe fact that they are right on top of one another corresponds to the lackof a significanteffect for anxiety (group membership the difference between means test). The factthat the lines have different slopes at different points might suggest a significant effectforparallelism (the groups have different patterns of results across the different timepoints), but the variations in slope are very slight, which explains the nonsignificant effectthat we found for the parallelism test.

profile analysis how to

Documents