spss guide for mmi 409 - medical informatics...

SPSS Guide

For

MMI 409 by

John Wong

March 2012

Preface

Hopefully, this document can provide some guidance to MMI 409 students on how to use SPSS

to solve many of the problems covered in the D’Agostino book. In order to minimize the size of

this document, the images are reduced to very small sizes. Readers should view this document

electronically using maximum zoom. Good Luck.

John Wong

LBJOHN99 at Yahoo dot com

D’Agostino, R.B., Sullivan, L.M., & Beiser, A.S. (2006). Introductory applied biostatistics. Belmont, CA:

Brooks/Cole, Cengage Learning.

S P S S G u i d e F o r M M I 4 0 9 P a g e | i

Table of Contents Descriptive Statistics ..................................................................................................................................... 1

Explore, Percentile, 95% CI, Boxplot, Mean, Median, Histogram ............................................................. 1

T Test ............................................................................................................................................................. 3

One Sample T Test .................................................................................................................................... 3

T Test ............................................................................................................................................................. 4

2 Independent Samples T-test .................................................................................................................. 4

T Test ............................................................................................................................................................. 5

Paired Samples T-test ............................................................................................................................... 5

Chi-Square Test ............................................................................................................................................. 6

Goodness of Fit (Aggregated Data) ........................................................................................................... 6

Chi-Square Test ............................................................................................................................................. 8

Test of Independence (Aggregated Data) ................................................................................................. 8

ANOVA ........................................................................................................................................................ 10

ANOVA (One Way ANOVA) ..................................................................................................................... 10

ANOVA ........................................................................................................................................................ 12

ANOVA with Eta ..................................................................................................................................... 12

ANOVA ........................................................................................................................................................ 13

Repeated Measures ANOVA .................................................................................................................. 13

Correlation Analysis .................................................................................................................................... 15

Scatter Diagram ...................................................................................................................................... 15


Pearson r correlation coefficient ............................................................................................................ 16


Linear Regression .................................................................................................................................... 17

Non-parametric Test ................................................................................................................................... 18

2 Dependent Samples ............................................................................................................................. 18

Sign Test (Legacy Dialog) ......................................................................................................................... 18

Non-Parametric Test ................................................................................................................................... 19


Wilcoxon Signed Rank (Legacy Dialog) ................................................................................................... 19

S P S S G u i d e F o r M M I 4 0 9 P a g e | ii

Non-parametric Test ................................................................................................................................... 20


Wilcoxon Signed Rank (New Dialog) ....................................................................................................... 20


2 Independent Samples .......................................................................................................................... 21

Wilcoxon Rank Sum (Mann Whitney U).................................................................................................. 21


k Independent Samples .......................................................................................................................... 22

Kruskal-Wallis Test (Legacy Dialog) ......................................................................................................... 22

Non Parametric Test ................................................................................................................................... 23

k Independent Samples .......................................................................................................................... 23

Kruskal-Wallis Test (New Dialog) ............................................................................................................ 23


Spearman Correlation (Correlation Between Variables) ........................................................................ 24

S P S S G u i d e F o r M M I 4 0 9 P a g e | 1

Descriptive Statistics

Explore, Percentile, 95% CI, Boxplot, Mean, Median, Histogram

1) Setup Data

2) Descriptive Statistics -

> Explore

3) Select Dependent List

4) Select Factor List

(Independent Variable)

5) Click Statistics

6) Check Descriptives,

Outliers, Percentiles

7) Click Continue

8) Click Plot

9) Check Normality Plots

with tests

10) Click Continue

11) Click OK

Histogram Output 1

Histogram Output 2

Boxplot Output


Descriptives

TRT Statistic Std. Error

HDL 0 Mean 58.60 9.338

95% Confidence Interval for

Mean

Lower Bound 37.48

Upper Bound 79.72

5% Trimmed Mean 58.78

Median 64.00

Variance 872.044

Std. Deviation 29.530

Minimum 19

Maximum 95

Range 76

Interquartile Range 64

Skewness -.373 .687

Kurtosis -1.587 1.334

1 Mean 55.20 7.422

95% Confidence Interval for

Mean

Lower Bound 38.41

Upper Bound 71.99

5% Trimmed Mean 55.44

Median 56.00

Variance 550.844

Std. Deviation 23.470

Minimum 18

Maximum 88

Range 70

Interquartile Range 47

Skewness -.220 .687

Kurtosis -1.121 1.334

Percentiles

TRT

Percentiles

5 10 25 50 75 90 95

Weighted

Average(Definition 1)

HDL 0 19.00 19.10 21.50 64.00 85.50 94.20 .

1 18.00 19.00 31.75 56.00 78.25 87.10 .

Tukey's Hinges HDL 0 22.00 64.00 85.00

1 33.00 56.00 78.00


T Test

One Sample T Test

1) Setup Data

2) Compare Means ->

One Sample T Test

3) Select Test Variable

4) Enter Test Value

5) Click OK

One-Sample Statistics

N Mean Std. Deviation Std. Error Mean

AGE 20 48.85 9.466 2.117

One-Sample Test

Test Value = 50

t df Sig. (2-tailed) Mean Difference

95% Confidence Interval of the

Difference

Lower Upper

AGE -.543 19 .593 -1.150 -5.58 3.28


T Test

2 Independent Samples T-test

1) Setup Data

2) Compare Means ->

Independent Samples

T Test


4) Select Grouping

Variable

5) Click Define Groups

6) Enter Groups range for

Analysis

7) Click Continue

8) Click OK

9) From the Levene’s

test, with pvalue > 0.05

(variances are equal),

use the t test for equal

variances.

Group Statistics

TRT N Mean Std. Deviation Std. Error Mean

LDL 0 10 283.90 108.189 34.212

1 10 227.10 70.245 22.213

Independent Samples Test

Levene's Test

for Equality of

Variances t-test for Equality of Means

F Sig. t df

Sig. (2-

tailed)

Mean

Difference

Std.

Error

Differe

nce

95% Confidence

Interval of the

Difference

Lower Upper

LDL Equal variances

assumed

3.651 .072 1.392 18 .181 56.800 40.791 -28.899 142.499

Equal variances

not assumed

1.392 15.443 .184 56.800 40.791 -29.927 143.527


T Test

Paired Samples T-test

1) Setup Data

2) Compare Means ->

Paired Samples T Test

3) Select Variables into

Variable 1 and

Variable 2

Paired Samples Correlations

N Correlation Sig.

Pair 1 Before & After 20 .313 .180

Paired Samples Test

Paired Differences

t df

Sig.

(2-

tailed) Mean

Std.

Deviation

Std. Error

Mean

95% Confidence Interval

of the Difference

Lower Upper

Pair 1 Before -

After

-8.050 24.752 5.535 -19.634 3.534 -1.454 19 .162


Chi-Square Test

Goodness of Fit (Aggregated Data)

1) Enter Data

2) Define Labels

(Optional)

3) Weight Cases to use

the aggregated data

4) Check Weight Cases

by

5) Select the aggregated

variable

6) Non-parametric test ->

Legacy Diaglo -> Chi-

square


8) Define Expected

Variable (Order must

match p1,p2,p3,p4)

9) Click OK


Chi-Square Test

Frequencies

Topic Issue

Observed N Expected N Residual

Drugs 52 48.0 4.0

Sex 38 30.0 8.0

Stress 21 30.0 -9.0

Education 9 12.0 -3.0

Total 120

Test Statistics

Topic Issue

Chi-Square 5.917a

df 3

Asymp. Sig. .116

a. 0 cells (.0%) have expected

frequencies less than 5. The

minimum expected cell

frequency is 12.0.


Chi-Square Test

Test of Independence (Aggregated Data)

1) Setup Data

2) Define Labels For

Variable 1 (Optional)

3) Define Labels for

Variable 2

4) Weight Cases to use

aggregated data

5) Click Weight cases by

6) Select measurement

variable

7) Click OK

8) Select Descriptive

Statistics -> Crosstab

9) Select Row and

Column Variables

10) Click Statistics

11) Check Chi-square

12) Click Continue

13) Click Cells

14) Check Expected


Crosstabs

Site * Treatment Crosstabulation

Treatment

Total Diet and Exercise

Oral

Hypoglycemics Insulin

Site HMO Count 294 827 579 1700

Expected Count 292.0 774.3 633.8 1700.0

UTH Count 132 288 352 772

Expected Count 132.6 351.6 287.8 772.0

IPA Count 189 516 404 1109

Expected Count 190.5 505.1 413.4 1109.0

Total Count 615 1631 1335 3581

Expected Count 615.0 1631.0 1335.0 3581.0

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 34.629a 4 .000

Likelihood Ratio 34.498 4 .000

Linear-by-Linear Association 1.744 1 .187

N of Valid Cases 3581

a. 0 cells (.0%) have expected count less than 5. The minimum expected count

is 132.58.


ANOVA

ANOVA (One Way ANOVA)

1) Setup Data

2) Compare Means -> One Way ANOVA

3) Select Dependent List 4) Select Factor

5) Click Post Hoc 6) Select Scheffe 7) Select Tukey

8) Click Continue

ANOVA

Time To Relief in Minutes

Sum of Squares df Mean Square F Sig.

Between Groups 423.333 2 211.667 10.160 .003

Within Groups 250.000 12 20.833

Total 673.333 14

Post Hoc Tests


Multiple Comparisons

Dependent Variable:Time to Relief in Minutes

(I) Drug Type (J) Drug Type

Mean

Difference (I-J) Std. Error Sig.

95% Confidence Interval

Lower Bound Upper Bound

Tukey HSD 1 2 7.0000 2.8868 .076 -.701 14.701

3 13.0000* 2.8868 .002 5.299 20.701

2 1 -7.0000 2.8868 .076 -14.701 .701

3 6.0000 2.8868 .136 -1.701 13.701

3 1 -13.0000* 2.8868 .002 -20.701 -5.299

2 -6.0000 2.8868 .136 -13.701 1.701

Scheffe 1 2 7.0000 2.8868 .091 -1.047 15.047

3 13.0000* 2.8868 .003 4.953 21.047

2 1 -7.0000 2.8868 .091 -15.047 1.047

3 6.0000 2.8868 .158 -2.047 14.047

3 1 -13.0000* 2.8868 .003 -21.047 -4.953

2 -6.0000 2.8868 .158 -14.047 2.047

*. The mean difference is significant at the 0.05 level.

Homogeneous Subsets

Time to Relief in Minutes

Drug Type N

Subset for alpha = 0.05

1 2

Tukey HSDa 3 5 20.000

2 5 26.000 26.000

1 5 33.000

Sig. .136 .076

Scheffea 3 5 20.000

2 5 26.000 26.000

1 5 33.000

Sig. .158 .091

Means for groups in homogeneous subsets are displayed.

a. Uses Harmonic Mean Sample Size = 5.000.


ANOVA

ANOVA with Eta

1) Setup Data

2) Select Compare Means -> Means

3) Select Dependent List 4) Select Independent List

5) Click Options 6) Check Anova Table with

Eta

7) Click Continue

ANOVA Table

Sum of

Squares df

Mean

Square F Sig.

Time to Relief in Minutes *

Drug Type

Between Groups 423.333 2 211.667 10.160 .003

Within Groups 250.000 12 20.833

Total 673.333 14

Measures of Association

Eta Eta Squared

Time to Relief in Minutes *

Drug Type

.793 .629


ANOVA

Repeated Measures ANOVA

1) Setup Data

2) General Linear Model -> Repeated Measures

3) Enter Within Subject Name

4) Type Number of Levels 5) Enter Measure Name

6) Click Define

7) Select Variables to Within Subjects

8) Click OK

Tests of Within-Subjects Effects

Measure:Time

Source

Type III Sum of

Squares df Mean Square F Sig.

Course

Between

Course

Sphericity Assumed 476.467 2 238.233 15.601 .000

Greenhouse-Geisser 476.467 1.270 375.146 15.601 .001

Huynh-Feldt 476.467 1.384 344.184 15.601 .001


(Treatment) Lower-bound 476.467 1.000 476.467 15.601 .003

Error(Course)

Within Course

(Treatment)

Sphericity Assumed 274.867 18 15.270

Greenhouse-Geisser 274.867 11.431 24.046

Huynh-Feldt 274.867 12.459 22.062

Lower-bound 274.867 9.000 30.541

Tests of Within-Subjects Contrasts

Measure:Time

Source Course

Type III Sum of


Course Linear 470.450 1 470.450 43.628 .000

Quadratic 6.017 1 6.017 .305 .594

Error(Course) Linear 97.050 9 10.783

Quadratic 177.817 9 19.757

Tests of Between-Subjects Effects

Measure:Time

Transformed Variable:Average

Source

Type III Sum of


Intercept 624386.133 1 624386.133 2526.137 .000

Error

Between

Subjects

2224.533 9 247.170


Correlation Analysis

Scatter Diagram

1) Enter Data

2) Legacy Dialogs -> Scatter

Plot

3) Select Simple Scatter

4) Click Define

5) Select Dependent

Variable into Y Axis

6) Select Independent

Variable into X

Axus

7) Click OK

Scatter Plot Output



Pearson r correlation coefficient

1) Enter Data

2) Correlate -> Bivariate

3) Select Variables for

correlation

4) Click Options

5) Select Cross product

Deviations and

covariances

Correlations

Body Mass Index Systolic Blood Pressure

Body Mass Index Pearson Correlation 1 .860**

Sig. (2-tailed) .001

Sum of Squares and Cross-products 286.669 1036.950

Covariance Var(X)=31.852 Cov(X,Y)=115.217

N 10 10

Systolic Blood Pressure Pearson Correlation .860** 1

Sig. (2-tailed) .001

Sum of Squares and Cross-products 1036.950 5072.500

Covariance 115.217 Var(Y)=563.611

N 10 10

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



Linear Regression

1) Define Measure

Variables as scale.

Enter Data

2) Regression -> Linear

3) Select Dependent and

Independent varaibles

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .860a .739 .707 12.853

a. Predictors: (Constant), Body Mass Index

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 40.786 21.112 1.932 .089

Body Mass Index 3.617 .759 .860 4.765 .001

a. Dependent Variable: Systolic Blood Pressure


Non-parametric Test

2 Dependent Samples

Sign Test (Legacy Dialog)

1) Define Measure Variables as Scale; Enter Data

2) Nonparametric tests -> Legacy Dialogs ->2 Related Samples

3) Select Before and After variables

4) Check Sign

Sign Test

Frequencies

N

Postprogram - Baseline Negative Differencesa 2

Positive Differencesb 6

Tiesc 0

Total 8

a. Postprogram < Baseline

b. Postprogram > Baseline

c. Postprogram = Baseline

Test Statisticsb

Postprogram -

Baseline

Exact Sig. (2-tailed) .289a

a. Binomial distribution used.

b. Sign Test


Non-Parametric Test

2 Dependent Samples

Wilcoxon Signed Rank (Legacy Dialog)

1) Define Measure Variables as Scale; Enter Data

2) Nonparametric tests -> Legacy Dialogs -> 2 Related Samples

3) Select Before and After Variables

4) Check Wilcoxon

Wilcoxon Signed Ranks Test

Ranks

N Mean Rank Sum of Ranks

Postprogram - Baseline Negative Ranks 2a 5.25 10.50

Positive Ranks 6b 4.25 25.50

Ties 0c

Total 8

a. Postprogram < Baseline

b. Postprogram > Baseline

c. Postprogram = Baseline

Test Statisticsb

Postprogram -

Baseline

Z -1.053a

Asymp. Sig. (2-tailed) .292

a. Based on negative ranks.

b. Wilcoxon Signed Ranks Test


Non-parametric Test

2 Dependent Samples

Wilcoxon Signed Rank (New Dialog)

1) Define Measure Variables as Scale; Setup Data

2) Nonparametric tests -> Related Samples

3) Click Customize Analysis

4) Click Fields 5) Select Variables

6) Click Settings 7) Check Sign test 8) Check Wilcoxon

9) Click Run

Hypothesis Output Double Click to drill down to analysis

Sign Test Output

Wilcoxon Test Output


Non-Parametric Test

2 Independent Samples

Wilcoxon Rank Sum (Mann Whitney U)

1) Define Treatment type as Ordinal, Measure Variable as Scale, Setup Data

2) Nonparametric tests -> Independent Samples

3) Select Customize analysis

4) Select Fields tab 5) Select Measure

variable in Test Fields 6) Select Treatment type

variable in Groups

7) Select Settings Tab 8) Check Mann Witney U 9) Click Run

Hypothesis Output (one tail test) Double Click to drill down to analysis

Mann-Whitney U Output Use Exact Sig for one tail analysis Use Asymp Sig for 2 tail analysis


Non-Parametric Test

k Independent Samples

Kruskal-Wallis Test (Legacy Dialog)


2) Nonparametric tests -> Legacy Dialog -> K Independent Samples

3) Select test Variable 4) Select Grouping Variable 5) Click Define range

6) Define range of grouping

7) Click Continue 8) Click OK

Kruskal-Wallis Test

Ranks

Treatment N Mean Rank

Time 0 5 9.70

15 5 17.40

40 5 11.20

50 5 3.70

Total 20

Test Statisticsa,b

Time

Chi-Square 13.692

df 3

Asymp. Sig. .003

a. Kruskal Wallis Test

b. Grouping Variable:

Treatment


Non Parametric Test

k Independent Samples

Kruskal-Wallis Test (New Dialog)


2) Nonparametric tests -> Independent Samples

3) Select Customize analysis

4) Select Fields tab 5) Select Measure variable

in Test Fields 6) Select Treatment type

variable in Groups

7) Select Settings 8) Select Customer Tests 9) Select Kruskal Wallis 10) Click Run

Hypothesis Output (one tail) Double Click to drill down to analysis

At the bottom of output, click the down arrow, select pairwise comparison

n) Pairwise Comparison. Use the “Sig.” column value for significance


Non-Parametric Test

Spearman Correlation (Correlation Between Variables)


2) Correlate -> Bivariate

3) Select Variables 4) Check Spearman

Nonparametric Correlations

[DataSet0]

Correlations

Number of Cigaretes

Per Day

Number of Hours of

Exercise Per Day

Spearman'

s rho

Number of Cigaretes Per

Day

Correlation Coefficient 1.000 -.454

Sig. (2-tailed) . .139

N 12 12

Number of Hours of

Exercise Per Day

Correlation Coefficient -.454 1.000

Sig. (2-tailed) .139 .

N 12 12

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