PASW Brief Training Guide

Amanda Haboush-Deloye, PhD Dawn Davidson, PhD

UNLV Nevada Institute for

Children’s Research and Policy

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SPSS Brief Training Guide Table of Contents

Part 1: SPSS Data Entry and Descriptive Statistics ................................................................................ 2

Data Entry ................................................................................................................................................. 2

Recoding Data ........................................................................................................................................... 4

Computing Data ........................................................................................................................................ 5

Missing Data .............................................................................................................................................. 5

Selecting Cases for Analysis ...................................................................................................................... 6

Syntax ........................................................................................................................................................ 6

Data Analysis: Frequencies and Descriptives ............................................................................................ 7

Part 2: Independent T-Test and One-Way Analysis of Variance (ANOVA) ........................................... 10

Part 3: Correlation ........................................................................................................................... 15

Part 4: One-way Chi Square .............................................................................................................. 17

Part 5: Repeated Measure ANOVA ................................................................................................... 19

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PART 1

SPSS Data Entry and Descriptive Statistics Descriptive analyses are done to help you describe your sample population. This will be done

using the demographic information you collected. The purpose of the exercise is to introduce you

to SPSS, data entry, and the analysis process. There are three steps to this process:

1) Data entry

2) Data analysis

3) Write up of results

Data Entry

1) Click on “Start” in the lower left hand corner of your screen, drag the mouse up to

“applications,” over and down to “SPSS for Window” and then over to “SPSS 18”

2) A box will appear with several options, click the option for “Type in Data”

3) There are two tabs in the lower left hand corner of the screen: Data view and Variable view.

You will organize and begin to set up your data on the variable view screen.

VARIABLE: a measurable factor, characteristic, or attribute of an individual or a

system—in other words, something that might be expected to vary over time or

between individuals.

Variables include your gender, age, ethnicity and/or race, education level, etc.

Variables also include each item on your measure/questionnaire and/or your total

score for a measure/questionnaire.

YOUR FIRST Variable should usually be an ID number for each participant.

4) Under the Name Column in Variable View, you will enter the name of your variable such as

ID, Age, Gender, and Race. For items on a measure you would normally use an abbreviation of

the measure plus the item number. For example, Depression Scale could be Dep1,

Dep2….DepTotal.

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5) Once you enter a variable name, information in the other columns will appear. For the type of

information, if you are entering a number for your data (23, 25, etc) it will be numeric, if you are

entering words (White, Asian, etc.) for your data it will be string. If you look at the picture

below, in the Type column, it says Numeric, and has a box with three dots. If you click on the

box, it will give you the option to change to string variable (words).

If you want to enter a long name in the Name Column you need to increase your “Width”

column. You can also put more details, such as the entire question in the “Label” Column.

Depending on your data, you may or may not want decimals. You can adjust this in the Decimal

column.

In the last column, you need to indicate what type of a measure the variable is. You have a

choice between scale, ordinal or nominal. It is a scale if the distance between two values is equal

(scale of 1-5 strongly agree to strongly disagree), ordinal if the order of the numbers matters but

the values are not equal distance (running a race and coming in 1st, 2

nd, etc.), or nominal where

numeric values are arbitrary (male = 1, female =2).

6) Values Column: If you are entering in words (e.g. strongly agree), most likely you will re-

code them into numbers for final analysis. Values allow you to assign a number to a word.

However, do not do this until you are ready to recode or you will have to do it all over again.

Re-coding is not hard and sometimes, if you do not already have numbers assigned to

information on your measure, it may be less confusing to just enter what is written on your

measure. For example, with race, just type in the race they circled rather then numbering them in

your head and entering that data.

However, if your measure already assigns numbers to the words, you can just enter the numbers

and their values so you do not have to recode. To enter values, assign a number to each

name/word (Label) in that variable. Then click “Add” to create the value. Repeat until finished;

then click OK.

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NOW ENTER YOUR DATA

Please go to the Data view tab on the bottom left hand corner of the SPSS screen. On the top row

you should see the variables you entered. Underneath you start entering your data. Each subject’s

data will go across the rows.

7) Re-Coding Data:

There are a few different reasons you might recode a variable. You want to change the values

from words to numbers, or you may want to reverse code several items.

On the Tool Bar, click on “Transform.” Then drag down to “Recode” and then over to “into

Different Variables.” It is not recommended that you recode into the same variable, although

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you could. This way, the original data is always in the database. You just need to make sure you

select the recoded variable for analyses.

Choose the variable you want to recode by highlighting the variable and clicking on the

arrow to move the variable name into the box.

Next, name and label the new recoded variable by typing the information into the

appropriate box.

Next, click on “Change” and then on “old and new values.” Each value of a variable

may be recoded separately by using the “Value” box to enter the old value and then the

new value. Multiple variable values may be recoded into one new value by using the

“Range” boxes. Missing values may be coded here, as well.

Each recoding step must be added to the display box by choosing “Add.” When all values

you want to recode are in place, click on “continue.”

If you want to keep a record of your recodes, click paste. This will bring you to a screen

called syntax. This is recommended so you don’t forget what you did. Or you can just

click “OK” and your analysis will be run. This will bring you back to the data view and

your new variable will appear as the last column to the far right in the data set.

Now you have to enter values for the new variable in the “Variable View” of the data set that is

described above.

8) Computing Data:

If you would like to calculate a total across variable, calculate BMI, or perform other calculation

you:

Got to Tool Bar, click on “Transform.” Then drag down to “Compute Variable”

Make a new target name (DepTotal)

In the numeric expression you are going to select the variables from the column

on the left and move them into the numeric expression box and then include your

operation (*, +, -, etc.)

The functions and special variable box will help show you how to write certain

calculations so they are done correctly.

At the bottom of this screen, there is an “IF” box. This can be used if you only

want the computations done on specific cases. See selecting cases below for more

information.

When you are done, you can click “OK” to run, or “Paste” to include in the syntax

for your records.

10) Missing Data:

In the Variable View of the data set, there is a column entitled “Missing.” Click on the cell in

that column where missing data is to be coded and then click on the gray box within that cell.

Choose “Discrete Missing Values” and enter the missing value(s) into the boxes provided (you

may enter 3 missing data values for each variable this way. This shows you that you can track

when individuals skip an item so you know you did not make a data entry error.

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11) Selecting Cases for Analysis

If you only want to examine a certain subset of your sample there are a few ways you can do

this. First, you can filter your sample to run an analysis.

Go to Data Select Cases

o Here you can either select cases based on certain criteria or select cases at

random

o To select cases based on criteria click on the “If condition is satisfied

Then you select the variable you want from the left column and

click the arrow to move it to the right box.

Then it depends on what criteria you have. You select cases based

on a value (e.g. gender = 1 will only select females), or on scores

(e.g. deptotal < 10), or you can select several values together by

include an & symbol.

o After you have finished click continue.

o At this point you can

Filter in the current data set by selecting “ok” (It is still

recommended you paste into a syntax file and run from that file to

keep a record).

Put these variables into a new data set, by selecting “copy selected

cases into a new dataset”. Make sure you save this new dataset

with a new name.

It is not recommended that you delete unselected cases.

Selected Casing when transforming/computing variables

o If you are transforming variables, there is an option to select specific

cases. There is a button toward the bottom of the window that says “IF”

The steps described above are similar. Using the Select Cases options will

NOT filter the data set when you transform or computer new variables, it

will use the whole dataset unless you have extracted variables into a new

dataset. Use the “IF” option to only recode or do computations for certain

cases.

12) Syntax

This is a file that helps you keep track of all the analyses you have run on the data. Every

time you click paste, it will paste into an open syntax file or create a new file if one is not

already open.

You can run data from the syntax file by

o highlighting the analysis you want to run and clicking the big green arrow

at the top row, or

o you can click on Run either All or Selection

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Data Analysis

In this section you will begin to analyze the data into a format that will allow you to

describe your sample. The basic idea of this is to tell people, how many men and women you

have in your sample, around what age are they, what is the ethnic breakdown….

1) On the Tool bar, click on “Analyze”, drag the arrow down to “Descriptive Statistics” and

over to “Descriptives”. Choose your demographic variables.

2) Highlight the variables and put them in the right hand box. Hit options and make sure that

the boxes for range, standard deviation, and mean are checked. Hit Okay. Make sure your

variables are selected and hit OK again. You have just performed your first statistical

operation in SPSS.

3) Now you should see that an output window shows up (look at bottom, blinking tab) and

you should have descriptive stats for your variables.

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4) Now, click back on the tab with your data. On the Tool bar, click on “Analyze”, drag the

arrow down to “Descriptive Statistics” and over to “Frequencies”. Choose the same

variables or demographics and move them over top the box. Click on the Statistics button

at the bottom and make sure that mean, median, and mode are checked, then hit continue,

then OK.

5) The results will appear in your output window below your fist analysis.

How to Read Your Output

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The variables that will be most useful in the Descriptive output are actual, real numbers that

you have entered, such as years and education. If you have coded your data (ex. 1=male,

2=female) you will use the output for frequencies.

Example 1:

Demographics

The sample generated from the 1990 NORC national Health Survey consists of 2,469

individuals’ ranging from 18-94 years with the mean age of 49.63. Fifty-seven percent of the

population was female. The ethnic background of this sample was predominately White (84%),

followed by African Americans (9%), Hispanics (3.6%), and Asians (1%). The mean number of

years of education (ranging from 1-17 years) for this sample was 12.77. Two percent of the

population was classified as Other. With regards to religion, 47.5 were identified as Protestant,

23.3% were Catholic, 17.8% of the sample indicated Other, and 8.5% indicated no religion.

Please refer to Table 1.

Table 1 Means, Standard Errors and Frequencies for Participant Characteristics

N Mean SD %

Age 2463 49.63 18.43

Gender 2469

Male 1055 42.6%

Female 1414 57.2%

Ethnicity 2467

Caucasian 2077 84%

African American 223 9%

Hispanic 88 3.6%

Asian/Pacific Islander 24 1%

Other 55 2.2%

Religion 2445

Christian 1174 47.5%

Catholic 577 23.3%

Other 441 17.8%

No Religion 211 8.5%

Education 2435 12.77 2.80

____________________________________________________________

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PART 2

Analysis Independent T-Test and ANOVA

Study: You are researching attitudes of homelessness in the community and would like to

compare different groups. The Attitudes Toward Homelessness Scale is a 20-item measure with

scores ranging from 0-100. High scores indicate a more positive attitude toward individuals who

are homeless.

Study 1Independent T Test:

Social Workers

General Community Member

1 95 85

2 84 56

3 76 74

4 82 68

5 83 50

6 88 59

7 99 61

8 91 72

9 90 90

10 79 88

Step 1: Enter Data into SPSS. Remember that one variable will be group (1= social worker,

2=Community Member). Do not put each into its own column. You should have twenty

participants. Make sure to label the groups in the variable view just as you have done for

previous assignments. Then enter in the group codings and the individual scores in the data view

of SPSS.

Step 2: Analysis:

Analyze→ Compare Means → Independent Sample T test.

The test variable will be the scores on the test. Click on that variable in the left box and move it

into the test variable box. The grouping variable will indicate which groups you would like to

compare. Click on your group variable and move it into the grouping box. Then click Define

Groups. Put in your assigned labels for the two groups you want (1= social worker,

2=Community Member). Then click continue.

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Next, click on options. You will see that it automatically begins with a 95% Confidence Interval.

This is fine; however you can change it if you want. But you need to say you did so and why.

Next, click Paste.

Next, click the green arrow to run.

This should provide you with output that looks like the following.

T-Test [DataSet0]

Group Statistics

10 86.7000 7.21187 2.28059

10 70.3000 14.02419 4.43484

Groups

sw

community

VAR00002

N Mean Std. Dev iation

Std. Error

Mean

Independent Samples Test

4.973 .039 3.289 18 .004 16.40000 4.98687 5.92297 26.87703

3.289 13.449 .006 16.40000 4.98687 5.66296 27.13704

Equal v ariances

assumed

Equal v ariances

not assumed

VAR00002

F Sig.

Levene's Test f or

Equality of Variances

t df Sig. (2-tailed)

Mean

Dif f erence

Std. Error

Dif f erence Lower Upper

95% Conf idence

Interv al of the

Dif f erence

t-test for Equality of Means

First check to see if you use Equal Variances assumes. If this is significant, use equal variances

not assumed. In this case, it is significant (p<.05). Then, to see if you have significant results,

look for the Sig. (2-tailed) results for the Equal Variances Not assumed. Then look at your

descriptive information to say more about the results. In this case the groups are significantly

different from one another. The means in the descriptive box tell you which group has more

positive attitudes, the group with the higher mean; in this example, social workers.

T-test Write Up:

Significant Findings:

A t-test of independent means was conducted to determine if there were differences in attitudes

toward homelessness among social workers and general community members. Results indicated

that there were statistically significant differences between social workers (86.70, 7.21) and

general community members (70.30, 14.02), t(13.45) = 3.29, p < .01, such that social workers

have a more positive attitude toward homelessness compared to general community members.

Non-Significant Findings:

A t-test of (dependent/independent) means was conducted to determine if there were ethnic

differences in levels of acculturative stress between African American and Hispanic youth.

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Results indicated that there were no significant different in levels of acculturative stress between

African American (78.93, 27.50) and Hispanic youth (84.24, 5.62), t(98) = 6.04, p = .57.

*NOTE: only difference when reporting dependent means is that you are referring to a pre and

post-tests verses African American and Hispanic youth. A t-test of dependent means was

conducted to determine if there were differences in levels of acculturative stress in Hispanic

youth after participating in therapy. Results indicated that there were significant differences

between pre (78.93, 27.50) and post (101.58, 30.83) acculturative stress scores, t(98) = 6.04, p <

.01.

Study 2 ANOVA:

Now you will do another analysis except with more groups.

Social Workers

General Community Member

College Students

High School Students

1 95 85 44 65

2 84 56 51 79

3 76 74 52 94

4 82 68 59 85

5 83 50 87 87

6 88 59 76 82

7 99 61 41 52

8 91 72 55 65

9 90 90 85 59

10 79 88 54 65

Step 1: Enter the rest of the Data into SPSS. Remember to add labels to your group variable for

the new groups. You should have forty twenty participants. Then enter in the group codings and

the individual scores in the data view of SPSS.

Step 2: Analysis:

Analyze→ Compare Means → One Way ANOVA

The dependent variable in this case would be scores on the measure. Click on that variable and

move it into the dependent variable list. The Factor will be how you want to divide your sample,

and we want to divide by groups, so click on the group variable and move it to the factor box.

Next, click on Post Hoc. Check the box that indicates Tukey. If you have significant differences

this will help you determine between which groups they exist. Also notice at the bottom of this

box the significance level is set at .05. Then click continue.

Next, click on Options. Check the descriptive box. This will give you means and SD for each

group so you can make specific statements later. Then click continue.

Then Click OK.

You will get output that looks like the following. To know if you have significant difference

between the groups, look for F then next to it will be Sig. at some level. If there is significance a

box below will give you post hoc results.

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Oneway

Descriptives

VAR00002

10 86.7000 7.21187 2.28059 81.5409 91.8591 76.00 99.00

10 70.3000 14.02419 4.43484 60.2677 80.3323 50.00 90.00

10 60.4000 16.43979 5.19872 48.6397 72.1603 41.00 87.00

10 73.3000 13.84879 4.37937 63.3932 83.2068 52.00 94.00

40 72.6750 15.95729 2.52307 67.5716 77.7784 41.00 99.00

sw

community

college

high school

Total

N Mean Std. Deviation Std. Error Lower Bound Upper Bound

95% Conf idence Interval for

Mean

Minimum Maximum

You are going to use this descriptive table in order to determine which groups have higher

means, if there is significance. That will tell you more about your research question regarding

attitudes, similar to the t-test, but with more groups.

ANOVA

VAR00002

3534.075 3 1178.025 6.630 .001

6396.700 36 177.686

9930.775 39

Between Groups

Within Groups

Total

Sum of

Squares df Mean Square F Sig.

If there is significance, then you need to continue on by interpreting the post hoc

tests. If the results are non-significant, you are done. In this case there is

significance.

Statistically significant differences in attitudes toward homelessness emerged among social

workers, general community members, college students, and high school students, F (3, 36) =

6.63, p < .01.

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Post Hoc Tests

I would read this table by saying ok, social work group compared to the general community

group is significantly different at the .05 level. Then look at your descriptive information to say

more about the population. The mean score (found in descriptive table above) for SW is 86.7 and

the mean score for the community is 70.3, so I can conclude that social workers have

significantly more positive attitudes toward homeless individuals compare to general community

members. You would do this for each group comparison.

Multiple Comparisons

Dependent Variable: VAR00002

Tukey HSD

16.40000* 5.96131 .044 .3448 32.4552

26.30000* 5.96131 .000 10.2448 42.3552

13.40000 5.96131 .130 -2.6552 29.4552

-16.40000* 5.96131 .044 -32.4552 -.3448

9.90000 5.96131 .359 -6.1552 25.9552

-3.00000 5.96131 .958 -19.0552 13.0552

-26.30000* 5.96131 .000 -42.3552 -10.2448

-9.90000 5.96131 .359 -25.9552 6.1552

-12.90000 5.96131 .153 -28.9552 3.1552

-13.40000 5.96131 .130 -29.4552 2.6552

3.00000 5.96131 .958 -13.0552 19.0552

12.90000 5.96131 .153 -3.1552 28.9552

(J) Groups

community

college

high school

sw

college

high school

sw

community

high school

sw

community

college

(I) Groups

sw

community

college

high school

Mean

Dif f erence

(I-J) Std. Error Sig. Lower Bound Upper Bound

95% Conf idence Interv al

The mean dif f erence is signif icant at the .05 lev el.*.

ANOVA Write Up:

An analysis of variance (ANOVA) was conducted to determine if there were differences in

attitudes toward homelessness among social workers, general community members, college

students, and high school students. Statistically significant differences in attitudes toward

homelessness emerged among social workers, general community members, college students,

and high school students, F (3, 36) = 6.63, p < .01. Tukey post hoc tests revealed social workers

(86.70, 7.21) had significantly more positive attitudes when compared to the general community

(70.30, 14.02), p<.05 and college students (60.4, 16.44), p < .01. No other differences were

detected.

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PART 3

Analysis: Correlations

The purpose of this assignment is to learn how to run a correlation analysis.

Study: You are hypothesizing that case managers at a community agency will have higher

satisfaction ratings with more experience. You also believe that the more years of experience

should be related to income level as well as years of education. Please run three correlation

analyses to test your hypotheses.

Social Worker ID Income

Years of Education

Case Manager Satisfaction

Years of Experience

1 25000.00 16 70 3

2 28000.00 16 75 4

3 30000.00 16 69 3

4 50000.00 20 85 8

5 47000.00 20 80 6

6 45000.00 20 85 7

7 35000.00 18 75 4

8 22000.00 16 64 2

9 37000.00 18 79 5

10 34000.00 18 66 4

11 34000.00 18 72 6

12 33000.00 18 70 5

13 38000.00 18 78 6

14 42000.00 20 92 10

15 46000.00 20 95 9

16 18000.00 16 72 2

17 45000.00 20 78 4

18 24000.00 16 82 5

19 28000.00 16 75 1

20 30000.00 18 80 6

Step 1: Enter Data into SPSS. You should have twenty participants.

Step 2: Analysis:

Analyze→ Correlate → Bivariate

Click on the two variables in the left box and move it into the variable box. Pearson’s box should

be checked under the variables box.

Next Click OK.

This should provide you with output that looks like the following. To see if you have significant

results, look for the Sig. (2-tailed) results. Then look at your descriptive information to say

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more about the results. To read the results, you look at the top right box, this is the correlate for

(in my example) income and education.

Please repeat the process to get each correlation.

Correlations [DataSet0]

Correlations

1 .940**

.000

20 20

.940** 1

.000

20 20

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

SES

Education

SES Education

Correlation is signif icant at the 0.01 level (2-tailed).**.

To read this output, you first examine the strength of the relationship which is Pearson

Correlation, r=.940. Then you need to determine if the relationship is significant, meaning it

did not occur by chance. Look right below Pearson, to Sig. (p<.01).

A good rule of thumb is that there is not a meaningful relationship if r <.3, relationship is small if

r < .5, relationship is moderate if r < .7, and the relationship is strong if the r >.7.

Sample Write Up

A study was conducted to research case manager satisfaction in relation to years of experience as

well as years of experience in relation to income and years of education. A sample of 20 social

workers was randomly selected to complete a self-report survey at a conference. A Pearson’s

correlational analysis was used to determine these relationships. With an alpha level of .05, the

results determined that there was a strong positive correlation between case manager satisfaction and

years of experience, r=.822, p<.01. This indicates that case manager satisfaction increases as years of

experience increase. Results also showed that there was a significant strong positive correlation

between years of experience and both income r=.737and years of education r=.790, p<.01. This

indicates that case workers with more years of experience are more likely to have a higher income as

well as a higher level of education.

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PART 4

Analysis: Chi Square Test

Chi Square tests are used to run analysis on nominal level data. You are trying to determine if

certain values (male, female) and equal within one variable.

Analysis:

One Way Chi Square Analysis

Step 1. Analyze→ Non-parametric Test → Legacy Dialogue → Chi Square

Click on the variable you are studying and move it to the left box and move it into the variable

box.

Next Click OK.

This should provide you with output that looks like the following. To see if you have significant

results, look at the bottom right hand corner of the test statistics box.

NPar Tests

Chi-Square Test

Frequencies

Gender

8 5.0 3.0

2 5.0 -3.0

10

male

f emale

Total

Observed N Expected N Residual

Test Statistics

3.600

1

.058

Chi-Square a

df

Asy mp. Sig.

Gender

0 cells (.0%) hav e expected f requencies less than

5. The minimum expected cell f requency is 5.0.

a.

This is the box you look at to determine significance for the chi square. In this example, results are not significant (p=.058). p has to be <.05, not equal to in order to be significant.

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Example Write Up

Example 1: Approaching Significance, Results obtained from above

In this study, we are examining the tendency for an agency to accept women over men to a free

parenting program offered in the community. The agency reported the number of women and

men enrolled in the parenting program at the time of the study. A Chi Square test will be

conducted in order to determine if a tendency exist. Results of the Chi Square test indicate that

there is no significant difference between expected and observed frequencies, χ2 (1, N=10) =

3.60, p=.058. However, the p value is extremely close to .05 therefore it appears inconclusive

whether or not I would reject or accept the null hypothesis.

Example 2: Insignificant results

In this study, we are examining the tendency for an agency to accept women over men to a free

parenting program offered in the community. The agency reported the number of women and

men enrolled in the parenting program at the time of the study. A Chi Square test will be

conducted in order to determine if a tendency exist. Results of the Chi Square test indicate that

there is no significant difference between expected and observed frequencies, χ2 (1, N=10) = 2.1,

p=.12. Therefore, the null hypothesis would be retained suggesting that the agency shows no

tendency to admit one particular gender into their program.

Or

Example 3: Significant Results

Results of the Chi Square test indicate that there is a significant difference between observed and

expected frequencies, χ2 (1, N=10) = 8.25, p<.05. Therefore, the null hypothesis would be

rejected suggesting that the agency shows a tendency to admit more men compared to women in

their program.

Note: (In order to determine which way the tendency goes, look at the first frequency

box, and you can see there are 8 men and 2 women, which means, if there is a significant

difference, significantly more men were admitted than women.)

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PART 5 Analysis: One-Way Repeated Measures ANOVA (Within-Subjects ANOVA)

The purpose of this assignment is to learn how to run a one-way repeated measures analysis of

variance. A repeated measures analysis of variance is also called a within-subjects analysis of

variance.

Sample Study: You are hypothesizing that patients from a residential treatment program will

report a decrease in the number of days of alcohol use after discharge as compared to pre-

treatment and that this decrease will be maintained over time. Run a one-way repeated measures

analysis of variance to test your hypothesis.

Time

Time 1 Time 2 Time 3

Participant ID Number of Days of ETOH use in past 30 days before treatment

Number of Days of ETOH use in past 30 days 1-Month after discharge

Number of Days of ETOH use in past 30 days 6-Months after discharge

1 20 0 0

2 30 5 15

3 18 0 0

4 25 20 15

5 5 7 30

6 3 0 0

7 15 2 30

8 0 0 0

9 24 30 0

10 30 30 30

11 30 0 0

12 30 5 0

13 16 0 0

14 30 0 0

15 30 1 30

16 30 0 0

17 30 30 15

18 0 0 0

19 2 8 0

20 5 0 0

21 10 0 0

22 5 5 5

23 0 0 2

24 0 4 4

25 30 30 30

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Data Analysis in SPSS Step 1: Enter data into SPSS. You should have twenty-five rows of data and four variables

including ID, Examples of variable names could include: ID, Time1, Time2, and Time3.

Step 2: Analysis:

Analyze General Linear Model Repeated Measures

Repeated Measures Definitions:

Within-Subjects Factor Name:

Rename your within subjects factor (currently in SPSS as “factor1”) to something

that is more meaningful to you. In this example “Time” is appropriate.

Number of Levels:

The number of times your dependent variable (Number of Days) has been

measured (in this assignment it will be 3).

Click the “Add” button next to the Number of Levels box.

Measure Name:

Put a meaningful name in this box for your dependent variable. In this example,

“ETOHuse” might be appropriate.

Click the “Add” button next to the “Measure Name” box.

Click the “Define” button at the bottom of the box.

A new box will open called “Repeated Measures”.

Repeated Measures:

Transfer your time variable labels into the “Within Subjects Variables (time)” box

by dragging and dropping them or by using the buttons. The variables should be

in chronological order, starting with the first time point (ETOH_1, ETOH_2,

ETOH_3).

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Click “Options”.

A new box will open called “Repeated Measures: Options”.

Repeated Measures: Options:

Transfer “Time” from the “Factor(s) and Factor Interactions” box to the “Display

Means” box.

Below this box is a checkbox for “Compare main effects”. Check this box.

Under the checkbox is a dropdown menu for “Confidence interval adjustment”.

Select “Bonferroni” from this menu.

In the “Display” menu, check “Descriptive statistics” and “Estimates of effect

size”.

Click “Continue”.

Click “Paste”.

A syntax window will open indicating the operations that you are about to run.

Select the syntax and then click the green arrow to run the listed operations.

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Interpreting the results

The first table you will need to look at is “Mauchly’s Test of Sphericity”. This test is used to

determine if you violated one of the assumptions of a repeated measures analysis of variance.

The assumption measured by this test is the homogeneity of covariance (that the amount of

variance in the DV between time 1 and time 2 is the same as between time 2 and time 3 for a

given subject). Homogeneity of covariance is also called sphericity.

Look at the output for the “Mauchly’s Test of Sphericity”. If the test is significant (< .05), then

the assumption has been violated. If the assumption is violated, the Greenhouse-Geisser

approach can adjust for this. If the assumption is not violated (the Mauchly’s Test of Sphericity

is not significant at p >.05) then you can interpret your results based on “Sphericity Assumed”.

Here is the “Mauchly’s Test of Sphericity” for the assignment above.

In the above table we can see that the Mauchly’s Test of Sphericity was not significant (p =

.589). Therefore, we can interpret our results based on the assumption not being violated. So, in

the “Tests of Within-Subjects Effects” table below, you will look at the “Time Sphericity

Assumed” row to determine whether or not there was a difference in your measure over time.

In the “Tests of Within-Subjects Effects” table above, we can see that there was an overall

significant difference in our DV over time (p = .001).

If we had violated the assumption of sphericity (the Mauchly’s Test of Sphericity was

significant), then we would look at the “Time Greenhouse-Geisser” line in the “Tests of Within-

Subjects Effects” table.

We now know that in this assignment there was a significant effect for time but we don’t know

where those differences lie. To determine this, we look at the “Pairwise Comparisons” table.

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(Note: If you do not have an overall statistically significant finding for the within subjects

effects, you do NOT examine the “Pairwise Comparisons” table.)

The “Pairwise Comparisons” table compares our DV at all of the measured time points in our

study to determine if they differ from one another. In the table above, from the assignment, we

can see that the difference between Time 1 and Time 2 is significant (p = .003), the difference

between Time 1 and Time 3 is significant (p = .020), but the difference between Time 2 and

Time 3 is not significant (p = 1.000).

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Reporting the results:

A repeated measures analysis of variance was conducted to determine whether or not the number

of days of reported alcohol use (“in the past 30 days”) decreased after substance abuse treatment

and whether or not this decrease was maintained. The results indicate that the number of days of

reported alcohol use did differ over time (F (2, 48) = 8.141, p < .005).a Post-hoc tests using a

Bonferroni correction indicated that the number of days of reported alcohol use (“in the past 30

days”) statistically significantly decreased (p < .005) from pre-treatment (M= 16.72, SD = 12.42)

to 1-Month post discharge (M = 7.08, SD = 11.10). The number of days of reported alcohol use

(“in the past 30 days”) remained low at 3-Months post discharge (M = 8.24, SD = 12.12) as

compared to pre-treatment (p < .05). There was no statistically significant difference between

the number of days of alcohol use at 1-Month and 3-Months post-discharge (p=1.0).

a If the Mauchly’s Test of Sphericity is statistically significant, you would report the overall significance based on the

“Greenhouse-Geisser” line of the “Tests of Within-Subjects Effects”. The first two sentences of the results would then be

changed to: “A repeated measures analysis of variance with a Greenhouse-Geisser correction was conducted to determine

whether or not the number of days of reported alcohol use (“in the past 30 days”) decreased after substance abuse treatment and

whether or not this decrease was maintained. The results indicated that the number of days of reported alcohol use did differ over

time (F (1.914, 45.934) = 8.141, p < .005).