MPH 2009

MPH 2009

Introduction to SPSS

Judith L. Jacobsen, PhD

Iben Gjødsbøl

MPH 2009

SPSS

• Menu based / point & click Writes and runs programs No programming knowledge required

No syntax errors Easy to import files in many formats

BUT:Not everything will be includedHeavy to use if you use it a lotThe reproducibility is somewhat lost

MPH 2009

Variable view window

• Here you define variables: name, label, values ect.

• Example defining variable: sex– Name: v1

– Label: sex

– Values: 1 = man, 2 = woman

– Missing: 9

– Measure: scale

• Check out your variable definitions by making a code book: – Utilities|Variables

MPH 2009

MPH 2009

Data view/output window

• Data view window:

– Here you type in your data

– In our example: 1 if man, 2 if woman

• Output window:

– The results (if any)

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Example with existing data: Blood

pressure & obesity OBESE weight/ideal weight BP systolic

blood pressure

SEX OBESE BP .. .. ..male 1.31 130 .. .. ..male 1.31 148 female 1.25 98male 1.19 146 female 1.24 110male 1.11 122 female 1.27 118male 1.34 140 female 1.57 116male 1.17 146 female 1.30 118male 1.56 132 female 1.32 138male 1.18 110 female 1.41 142male 1.04 124 female 1.21 124male 1.03 150 female 1.20 120

.. .. ..

.. .. .. female 1.73 208

MPH 2009

Data

Data are located in a *.txt file bp.txt

• With the following variables:

– SEX: character variable

– OBESE: obesity, i.e. weight/ideal weight

– BP: systolic blood pressure

I.e.:

3 variables

102 observations

MPH 2009

MPH 2009

Open Data

MPH 2009

Creating the dataset – step 1

• Open SPSS and click

Cancel

• Chose Files|Read Text

Data

– Select No and click Next

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Step 2

• Click Delimited ife.g. a spacebetween eachvariable

• Click Fixed width iffixed

• Click Yes – variable names areincluded

• Click Next

MPH 2009

MPH 2009

Step 3

• Type in:

– The first case of data

begins on line number 9

– One line represents a

case

– Import all of the cases

• Click Next

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Step 4

• Separate data in colums

by inserting variable

break line

• Click Next

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Step 5

• Name variables

• Select Data format:

– V1: String

– V2 & V3: Numeric

• Click Next

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MPH 2009

Step 6

• Chose if you want to

save the file format and

paste the syntax

• Click Finish

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Variable View

• Define labels, measure etc.

MPH 2009

Tabulating data

Chose the menu

Analyze >

Descriptive Statistics

> Crosstabs

click the variable

Sex→ Row(s)

click the variable

BP→ Column(s)102696Total

4.5595.45

44242Male

6.9093.1

58454Female

Total10

Frequency

Row Pct

TABLE OF SEX BY BP150

MPH 2009

MPH 2009

Different kinds of Data

• Discrete

– Dichotomous (two alternatives) ♀♂

– Nominal (named categories)

– Ordinal (ordered categories)

• Continuous

– Interval scale (specific distance)

– Ratio scale (same reference)

The statistical methods we chose are defined by the

scale of the variables or combinations of variables we

are interested in.

1 2 3

MPH 2009

Data Examples

Ordinal data

• Small – medium – big

Nominal data

• Red – green – blue

• Growth medium A – B – C

Binomial data

• Bacteria growth or not

Interval data

• Arbitrary point of zero (e.g.

C and F),

20° is not twice as warm as

10°

Ratio data

• Same reference 50 years is

twice as old as 25 yr

MPH 2009

Overview

Interval & ratio data contains more information

than

ordinal data,

which contains more information than

nominal data

One can always go from continuous ⇒ discrete

⇒ ordinal ⇒ nominal

But never the other way!

MPH 2009

MPH 2009

Views

• Variable view

– Here you can see all the variables. If information

about code values is insufficient, this must be

filled in

• Data view

– Here you can see the observations

• Don’t forget to save your data set: Files|Save

As

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Descriptive Statistics: min, max, mean

• Procedure:

– Analyze|Descriptive

Statistics|Descriptives

– Options: Here you can

chose min, max and mean

MPH 2009

Frequency Tables

• Procedure:

– Analyze|Descriptive

Statistics|Frequencies

– Statistics: here you can

choose mean, mode and

medianFrequency table

MPH 2009

MPH 2009

Graphs

• Procedure:

– Graphs|Legacy

Dialogs|Bar/Histogram/P

ie

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Creating categorical variables by

recoding

• Procedure:

– Transform|Recode into

Different Variables

• Click obese � Numeric

Variable -> Output

Variable window

• Give the new variable

name and label

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How To

• Click Old and New

Values

• Range, LOWEST

through value: 1,30 �

chose new value = 1.

Click Add

• Range, value through

HIGHEST: 1,31 �

chose new value = 0

• Standard setting:

Reference group = 0

MPH 2009

MPH 2009

Value Labels

• Type in values of the

new variable in variable

view window

• By using the same

method, create the

binary variable:

– BP > 150 / BP ≤ 150

– Ref. group: BP > 150

MPH 2009

Tabulating data

• Procedure:

– Analyse|Descriptive

Statistics|Crosstabs

– Click the variable

sex � Rows

– Click the variable

BP BINARY � Column(s)

• Row percentages: Click

Cells

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Output

Table of SEX by BP150 (High BP)

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MPH 2009

Categorical Data Example

• In a randomised investigation ( Storr et. al. Lancet,1987) the effect of a single dose prednisolone is compared to placebo for children w acute asthma

73 placebo and 67 prednisolone

• The result section states:

“2 patients in the placebo group (3%, 95% confidence interval −1 to 6%) and 20 in the prednisolone group (30%, 19 to 41%) were discharged at first examination (P < 0.0001)”

• The method section explains, that the above P-value is calculated using Fisher’s exact test

Lets check that out

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Data

Group

Response Placebo Prednisolone

Discharged 2 20

Hospitalised 71 47

73 57

Treatment Status

Placebo Discharged

Placebo Discharged

Placebo Hospitalized

Placebo Hospitalized

Placebo Hospitalized

SPSS needs data arranged in columns

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Solution

• Chi-square-test for independence chisq.test

• To calculate RR it is important to write the response

variable last in the table-specification:

• Procedure: Analyze|Descriptive Statistics|Crosstabs

click the variable Treatment→ Row(s)

click the variable Status→ Column(s)

click the button Statistics > new menu >

� by Chi-square

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MPH 2009

Data

• There must be an easier

way than

copy – paste

But I didn’t find it!

140 rows in all.

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Analyze

Procedure:

– Analyse|Descriptive

Statistics|Crosstabs

– Click the variable

treatment � Rows

– Click the variable

Status � Column(s)

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Output

100,00%1400,00%0100,00%140

Treatment * Status

PercentNPercentNPercentN

TotalMissingValid

Cases

Case Processing Summary

14011822Total

674720Predni

73712PlaceboTreatment

TotalHospDisch

Status

Count

Treatment * Status Crosstabulation

Ready for

your report

MPH 2009

MPH 2009

Procedure: File|Export

MPH 2009

MPH 2009

Result

140N of Valid Cases

0,0000,000Fisher's Exact Test

0,000121,753Likelihood Ratio

0,000117,394

Continuity

Correction(a)

0,000119,387(b)

Pearson Chi-Square

Exact Sig.

(1-sided)

Exact Sig.

(2-sided)

Asymp. Sig.

(2-sided)dfValue

Chi-Square Tests

a Computed only for a 2x2 tableb 0 cells (,0%) have expected count less than 5.

The minimum expected count is 10,53.

MPH 2009

MPH 2009

Furthermore

Chi-Square 19.387 p-value <.0001

Likelihood Ratio Chi-Square 21.7528 p-value <.0001

Fishers Exact 17.3942 p-value <.0001

Thus: Strong significant difference for the two treatments

• Fisher’s exact test, should be used when an expected value in any cell < 5.

• Recall info that no cells had expected count < 5. All expected values >> 5 so use the Chi-square-test

The authors were probably confused because one of the

observered numbers < 5

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Stratified analysis/Mantel Haenszel

analysis

• Example:

– The coherence between sex and BP controlling for

obesity

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Stratified analysis/Mantel Haenszel

analysis• Procedure:

– Analyze|Descriptive

Statistics|Crosstabs

– Click Obese BINARY �

Layer 1 of 1

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MPH 2009

Statistics

• Click Statistics and

chose:

– Chi-square

– Cochran’s and Mantel

Haenszel statistics

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Table of sex by high BP controlling

for obesity

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Test for effect modification

• Breslow-Day test:

– Tests if the 2 separate

OR are different

– H0: homogeneity

– If homogeneity:

calculate ORMH

– If effect modification:

stop analysis• In this case:

χ2 = 0,709 � p>0,05

homogeneity

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MPH 2009

Mantel Haenszel estimate (ORMH )

• ORMH = 1,098

�A weighted

average of

separate OR-

estimates

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Mantel Haenszel test

• χ2MH is a test for conditional independence: no

association between exposure (sex) and outcome

(high BP), adjusted for the confounder (obesity)

• �tests if ORMH = 1 (H0)

In our case: χ2MH = 0,707 � ORMH = 1

MPH 2009

Conclusion

• ORMH does not differ from 1 � there is

conditional independence, i.e there is no

coherence between sex and BP when

controlling for obesity

• If ORMH differs substantially from 1 (rejection

of the H0), the control variable is a confounder