introduction to spss – s0 · introduction • spss = s tatistical p ackage for the s ocial s...

Basic medical statistics for clinical and experimental research

Introduction to SPSS – S0

29 November 2019

Anna Morraa.morra@nki.nl

Slides of Katarzyna Jóźwiak

Introduction

• SPSS = Statistical Package for the Social Sciences• Very user friendly software for editing and analyzing all sorts of data

• After opening SPSS, two windows appear: • Data Editor with two spreadsheets:

• Data View• Variable View

• Output

Data editor

• SPSS Data View: displays our data values; columns represent variables, rows represent subjects/patients

Data editor

• SPSS Variable View: displays information regarding the meaning of our data, list of all variables, characteristics of all variables

Data editor

SPSS Variable View: displays information regarding the meaning of our data, list of all variables, characteristics of all variables

• Label: description of the variable in the dataset• Values: coding• Missing: 99=unkown• Measure:

• scale (it is a numeric continuous variable) • ordinal (categorical with an order between categories, i.e. tumor grade, tumor stage)• nominal (categorical with no order between categories, i.e. ethnicity)

Data editor: File

• New: to create new data set, syntax, output

• Open: to open existing dataset (format like .sav, .xls, .xlsx, .txt, .dta, .csv), syntax, output

• Save as: to save data (format like .sav, .csv, .xls, .xlsx, .dta)

Data editor: Data

• Sort Cases: to sort by a specific variable

• Sort Variables: to sort by, e.g., variables names, types, widths

• Merge Files: to combine data sets by adding variables or subjects

• Split File: to split a data set into smaller data sets

• Select Cases: to indicate which subjects should be used in analyses

Data editor: Transform

• Compute Variable : to compute a new variable

• Recode into Same Variables: to change values of an existing variable

• Recode into Different Variables: to change values of an existing variable and save the variable as an additional variable

Data editor: Analyze

• All standard statistical analyses can be found here

Data editor: Graphs• All standard graphical tools can be found here

Data example

Data example

Data example

Data exampleMs L. Meulen has a unique patient number 7. She is 26 years old and has 3 children (she was pregnant 3 times). She is a diabetic with glucose level 78, blood pressure 50, BMI 31 and height 169 cm.

Data example

What is the weight of the patients? 𝐵𝐵𝐵𝐵𝐵𝐵 =𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝐻𝐻𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊2 → 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 = 𝐵𝐵𝐵𝐵𝐵𝐵 ∗ 𝐻𝐻𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊2

Data exampleWhat is the weight of the patients? 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 = 𝐵𝐵𝐵𝐵𝐵𝐵 ∗ 𝐻𝐻𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊2

Data exampleWhat is the weight of the patients? 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 = 𝐵𝐵𝐵𝐵𝐵𝐵 ∗ 𝐻𝐻𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊2

Data exampleWhat is the weight of the patients?

Data exampleHow old are the patients in out dataset?

Data exampleHow old are the patients in out dataset?

Data exampleHow old are the patients in out dataset?

Data exampleHow old are the patients in out dataset?

Data exampleHow old are the patients in out dataset?

Data exampleHow old are the patients in out dataset?

Data exampleHow old are the patients in out dataset?

Data exampleHow old are the diabetic patients and non-diabetic patients in our data set?

Data exampleHow old are the diabetic patients and non-diabetic patients in our data set?

Data exampleHow old are the diabetic patients and non-diabetic patients in our data set?

Data exampleHow old are the diabetic patients and non-diabetic patients in our data set?

Data exampleHow old are the diabetic patients and non-diabetic patients in our data set?

Data exampleHow old are the diabetic patients and non-diabetic patients in our data set? → we can first split the file based on the "Diabetes" variable

Data exampleHow old are the diabetic patients and non-diabetic patients in our data set? → we can first split the file based on the "Diabetes" variable

Data exampleHow old are the diabetic patients and non-diabetic patients in our data set? → we can first split the file based on the "Diabetes" variable

Data exampleHow old are the diabetic patients and non-diabetic patients in our data set? → we can first split the file based on the "Diabetes" variable

Data exampleHow old are the diabetic patients? → we can first select patients of interest

Data exampleHow old are the diabetic patients? → we can first select patients of interest

Data exampleHow old are the diabetic patients? → we can first select patients of interest

Data exampleHow old are the diabetic patients? → we can first select patients of interest

Data exampleHow old are the diabetic patients? → We make a histogram after selecting patients of interest

Data exampleHow old are the diabetic patients? → We make a histogram after selecting patients of interest

Data exampleHow to indicate that diabetic patients are younger or older than 40 years of age?→ create categorical variable with values: 1 = non-diabetics, 2 = diabetics with age < 40, 3

= diabetics with age ≥ 40

Data exampleHow to indicate that diabetic patients are younger or older than 40 years of age? → create categorical variable with values: 1 = non-diabetics, 2 = diabetics with age < 40, 3 = diabetics with age ≥ 40

Data exampleHow to indicate that diabetic patients are younger or older than 40 years of age? → create categorical variable with values: 1 = non-diabetics, 2 = diabetics with age < 40, 3 = diabetics with age ≥ 40

Data exampleHow to indicate that diabetic patients are younger or older than 40 years of age? → create categorical variable with values: 1 = non-diabetics, 2 = diabetics with age < 40, 3 = diabetics with age ≥ 40

Data exampleHow to indicate that diabetic patients are younger or older than 40 years of age? → create categorical variable with values: 1 = non-diabetics, 2 = diabetics with age < 40, 3 = diabetics with age ≥ 40

Data exampleHow many diabetics patients younger and older than 40 we have in our data set?

Data exampleHow many diabetics patients younger and older than 40 we have in our data set?

Data exampleHow many diabetics patients younger and older than 40 we have in our data set?

Data exampleHow many diabetics patients younger and older than 40 we have in our data set?

Syntax editorBehind all the "drop down menu steps" there is a list of commands that perform the statistical analyses and data manipulation

Syntax editorAll commands can be saved in a syntax editor file → while performing statistical analyses and data manipulation, push "Paste" button instead of "OK" button

Syntax editorAll commands can be saved in a syntax editor file → while performing statistical analyses and data manipulation, push "Paste" button instead of "OK" button

Syntax editorAll commands can be saved in a syntax editor file → while performing statistical analyses and data manipulation, push "Paste" button instead of "OK" button

Syntax editorAny command from a syntax can be run separately by selecting the appropriate part and pushing "green arrow" button