Crash Course in Statistics

Introduction to SPSS

July 2014

Dr. Jürg Schwarz juerg.schwarz@schwarzpartners.ch

Neuroscience Center Zurich

Slide 2

Program 8 July 2014: Morning Lessons (09.00 – 12.00)

◦ First Part – Introduction

- A typical example

- Resources

- First steps

◦ Exercises

Program 8 July 2014: Afternoon Lessons (13.00 – 16.00)

◦ Second Part – Additional Topics

- Analysis functions

- Charts

◦ Exercises

Slide 3

Table of Contents

Introduction _____________________________________________________________________________________________ 5

A typical example ................................................................................................................................................................................................... 5

"How-to" in SPSS – First Impression .................................................................................................................................................................... 10

Resources _____________________________________________________________________________________________ 11

Manuals................................................................................................................................................................................................................ 11

Sample Files ........................................................................................................................................................................................................ 12

Using the Help System (Core System User’s Guide) ............................................................................................................................................ 14

Online-Resources ................................................................................................................................................................................................. 17

First Steps _____________________________________________________________________________________________ 18

Change the Application Language ........................................................................................................................................................................ 18

Starting SPSS & Opening a Data File ................................................................................................................................................................... 19

Data Editor & Data Organization .......................................................................................................................................................................... 20

Running an Analysis & Viewing Results ............................................................................................................................................................... 22

Intermezzo: Alphabetical view of the variables in the dialog boxes ....................................................................................................................... 23

Working with Syntax ............................................................................................................................................................................................. 27

Modifying Data Values .......................................................................................................................................................................................... 33

Select Cases & Split File ...................................................................................................................................................................................... 37

Data Entry ............................................................................................................................................................................................................ 41

Data Editor: Defining Variables, Entering Data & Missing Values ......................................................................................................................... 42

Importing Data ...................................................................................................................................................................................................... 47

Exercise 01: First Part – Introduction _______________________________________________________________________ 52

Slide 4

Analysis functions: Analyze ______________________________________________________________________________ 53

Descriptive Statistics ............................................................................................................................................................................................ 53

Inferential Statistics .............................................................................................................................................................................................. 63

Creating charts _________________________________________________________________________________________ 70

Manuals................................................................................................................................................................................................................ 70

Creating and Editing Charts .................................................................................................................................................................................. 71

Bar Chart – Self-Study 45 minutes ....................................................................................................................................................................... 72

Exercise 02: Second Part – Additional Topics ________________________________________________________________ 73

Slide 5

Introduction

A typical example

Medical research: What are the factors affecting body weight?

Data set (EXAMPLE00.SAV)

Sample of n = 198 men and women

Typical questions

Is there an impact of the factors K

◦ body size [cm]

◦ age [years]

◦ sex [0 / 1]

K on body weight?

How can this impact be modeled?

How strong is the impact of each factor?

Body size [cm]

Body w

eig

ht

[kg]

Slide 6

A closer look: Joint representation of the relationships between the variables

Body size Body weight Age

Body w

eig

ht

Body s

ize

A

ge

Slide 7

Questions

Question in everyday language:

How do individual characteristics influence body weight?

Research question:

Is there an impact of the factors K

◦ body size

◦ age

◦ sex

K on body weight?

How strong is the impact of the factors?

Is there a model?

Is linear regression analysis the right model?

Statistical question:

H0: "No model" (= No overall model and no significant coefficients)

HA: "Model" (= Overall model and significant coefficients)

Can we reject H0?

Slide 8

Solution

Multiple linear regression model with body weight as the dependent variable

0 1 2 3body weight = age body size sex uβ + β ⋅ + β ⋅ + β ⋅ +

0 3

body weight dependent variable

age, ... sex independent variables

,... coefficients

u error term

=

=

β β =

=

"How-to" in SPSS

Scales

Dependent variable: metric

Independent variables: metric, categorical (coded as dummy variables)

SPSS: Analyze�Regression�Linear...

Method: Enter (All variables are entered into the model simultaneously)

Method: Stepwise (Each variable is individually tested for fitness and included)

Method: Blockwise (Variables are entered in a predefined sequences of blocks)

Slide 9

Result

Significant overall model (table not shown)

High value for "Adjusted R Square" (R2adj. ≤ 1)

Significant coefficients (Sig. < .05)

SPSS Output

weight = -25.295 + .417 ⋅⋅⋅⋅ size + .476 ⋅⋅⋅⋅ age + 8.345 ⋅⋅⋅⋅ gender_d

Example interpretation: One more year of age increases body weight by .476 kilograms,

holding all the other independent variables constant.

age has highest impact: Standardized Beta = .596

Slide 10

"How-to" in SPSS – First Impression

Structure of SPSS

Output

Data File (in Data Editor)

Syntax File (in Syntax Editor)

Slide 11

Resources

Manuals

This introduction refers to the manual "IBM SPSS Statistics 22 Brief Guide"

Find this manual and also "IBM SPSS Statistics 22 Core System User’s Guide" here:

◦ www-01.ibm.com/support/docview.wss?uid=swg27038407#en

Slide 12

Sample Files

This introduction uses the data file demo.sav

◦ Find it here: www.schwarzpartners.ch/ZNZ_2014/Data_Files

Variable View

Data View

Slide 13

The data file demo.sav is a fictional survey of several thousand people (n = 6400),

containing basic demographic and consumer information.

Name Label

age Age in years

marital Marital statusaddress Years at current address

income Household income in thousandsinccat Income category in thousands

car Price of primary vehiclecarcat Primary vehicle price category

ed Level of educationemploy Years with current employerretire Retired

empcat Years with current employerjobsat Job satisfaction

gender Genderreside Number of people in household

wireless Wireless servicemultline Multiple linesvoice Voice mail

pager Paging serviceinternet Internet

callid Caller IDcallwait Call waiting

owntv Owns TVownvcr Owns VCR

owncd Owns stereo/CD playerownpda Owns PDAownpc Owns computer

ownfax Owns fax machinenews Newspaper subscription

response Response

Slide 14

Using the Help System (Core System User’s Guide)

Help is provided in many different forms:

Help menu (the most important)

◦ Topics: Provides access to the Contents, Index, and Search tabs, which you can use to find specific Help topics.

◦ Tutorial: Illustrated, step-by-step instructions on how to use many of the basic features.

◦ Case Studies: Hands-on examples of how to create various types of statistical analyses and how to interpret the results.

◦ Statistics Coach: A wizard-like approach to guide you through the process of finding the procedure that you want to use.

◦ Command Syntax Reference: Detailed command syntax reference information is available in two forms: integrated into the Help system and as a separate document in PDF form.

Context-sensitive Help

◦ Dialog box Help buttons: Most dialog boxes have a Help button that takes you directly to a Help topic for that dialog box.

◦ Pivot table context menu Help: Right-click on terms in an activated pivot table in the View-er and choose What’s This? from the context menu to display definitions of the terms.

◦ Command syntax: In a command syntax window, position the cursor anywhere within a syntax block for a command and press F1 on the keyboard.

Slide 15

Help menu

Dialog box Help buttons

=>

=>

:

Slide 16

Tutorials

:

Slide 17

Online-Resources

SPSS Solutions for Education

www-01.ibm.com/software/analytics/spss/academic/students/resources.html

IBM-ID znz@gmx.ch Password 7mydevelopper

SPSS Support (especially Knowledgebase Search)

http://support.spss.com/tech/default.asp

User spssswitzerland Password spssswitzerland

SPSS Support (resources for all levels of users and application developers)

www.spss.com/devcentral

User znz@gmx.ch Password 7mydevelopper

Other Resources / Forum / Discussion

www.ats.ucla.edu/stat/spss

http://spssx-discussion.1045642.n5.nabble.com

www.spssusers.co.uk

www.dynelytics.com/en =>

Slide 18

First Steps

Change the Application Language

The language can be selected through the Language tab under Edit�Options:

Slide 19

Starting SPSS & Opening a Data File

From the Start menu choose:

IBM SPSS Statistics

IBM SPSS Statistics 21

Other possibility:

Double click on SPSS data file

Find data file demo.sav here: www.schwarzpartners.ch/ZNZ_2014/Data_Files

Slide 20

Data Editor & Data Organization

The Data Editor displays the contents of the active data file

◦ Data View Columns represent variables and rows represent cases (observations)

◦ Variable View Each row is a variable, each column is an attribute of that variable

Slide 21

SPSS data is organized by cases (rows) and variables (columns)

Variables (columns)

Each column in the data editor corresponds to a specific measurement.

In many areas of research, these measurements are called variables.

Cases (rows)

For a survey of individuals, each row

would represent a respondent.

In an experiment, each row might corre-

spond to a single recorded observation.

Data View

Slide 22

Running an Analysis & Viewing Results

The "Analyze" menu contains different methods of analysis.

For example a simple frequency table with histogram:

Analyze�Descriptive Statistics�FrequenciesK

Slide 23

Intermezzo: Alphabetical view of the variables in the dialog boxes

The default settings of SPSS show labels for the variables in the dialog fields:

This could make the search for particular variables difficult.

Variables are shown with a label.

Slide 24

SPSS can be adjusted so that variables are displayed with their names and in

alphabetical order.

To do so, select the following setting under the General tab of Edit�Options:

Variables are displayed

alphabetically by names.

Place the cursor in the box that contains the

variables, and enter a character from the

keyboard. The first variable beginning with

this character will appear.

This allows you to quickly search through

the variable box to find a variable.

Slide 25

Create an additional histogram

Slide 26

Slide 27

Working with Syntax

Open a new syntax file through the menu: File�New�Syntax

Output

Data Editor

Syntax-Editor

*.sav files

*.spv files

*.sps files

Slide 28

How do you get the command syntax?

Option I: Perform an analysis through the menu

Example: Analyze�Descriptive Statistics�Frequencies

Output

Data Editor

Slide 29

Where is the syntax for this analysis? => The syntax is displayed in the output.

Double-click the syntax part in the log, highlight and copy the syntax.

Paste the syntax into the Syntax Editor.

Slide 30

Option II: Paste the syntax directly from the dialog box ("Paste" button).

Option III: Write the syntax yourself.

Executing the Syntax

Place the cursor inside the syntax in the syntax editor and run the analysis through the menu

Run�Selection.

Slide 31

Typical Syntax File

Why should you use syntax?

Rapidly leads to greater efficiency.

Documentation Reproducing the results Automatically process many commands Allows access to all commands Communication with other persons Opens the world of macros

Slide 32

What if the syntax is not displayed in the output?

Through the menu Edit�OptionsK�Viewer, choose Display commands in the log

The syntax is now displayed in the output.

Slide 33

Modifying Data Values

The data may not always exist in a form that can be used for analysis or reporting.

For example, you may want to:

◦ convert a scale variable into a categorical variable.

◦ merge different response categories into a single category.

◦ calculate a new variable from the difference between two existing variables.

Slide 34

Computing a new variable

New variables can be computed based on existing ones, for example by averaging scores,

summing them up etc.

For example you may want to compute the equivalence income (based on the household inco-

me and the number of persons in the household).

Transform�Compute VariableK

Syntax

COMPUTE income_equiv = income / SQRT(reside).

Slide 35

Recoding a variable

Example: creating a categorical variable from a scale variable.

For example, based on age in years we could build age categories.

Menu: Transform�Recode into Different VariablesK

Slide 36

Syntax

RECODE age (Lowest thru 24=1) (25 thru 44=2) (45 thru 60=3) (61 thru Highest=4) INTO age_r.

FREQUENCIES VARIABLES=age age_r /ORDER ANALYSIS.

Result

Scale values (age) Categorical values (age_r)

: Categories

1: up to 24 years

2: 25 - 44 years

3: 45 - 60 years

4: over 60 years

==>

Slide 37

Select Cases & Split File

Select cases

A particular subset of the data can be analyzed by selecting specific cases. Through this, all

undesired cases of your data set are either temporarily or permanently deleted.

For example, you may want to analyze only respondents who are older than 45 years.

Menu: Data�Select CasesK

Slide 38

Syntax Result

USE ALL.

COMPUTE filter_$=(age > 45).

FILTER BY filter_$.

EXECUTE .

FREQUENCIES VARIABLES=age

/FORMAT=NOTABLE

/HISTOGRAM

/ORDER=ANALYSIS.

FILTER OFF.

USE ALL.

EXECUTE .

These lines remove the

"filter" for all analyses

to come.

Slide 39

Split File

Sometimes data in different categories should be analyzed separately.

To do this, the data can be split up, and the same analysis can be performed on two or more da-

tasets.

For example, we could split the dataset by means of the variable age_r which means we are

conducting separate analyses for each of the age categories.

Menu: Data�Split FileK

Slide 40

Syntax Result

SORT CASES BY age_r .

SPLIT FILE SEPARATE BY age_r .

EXECUTE.

FREQUENCIES VARIABLES=income

/FORMAT=NOTABLE

/HISTOGRAM

/ORDER=ANALYSIS.

SPLIT FILE OFF.

This line removes the

split for all analyses

to come.

Slide 41

Data Entry

There are different ways to enter data into SPSS.

Data can be directly entered into SPSS or can be imported from many different sources:

◦ Direct: SPSS Data Editor

◦ From a spreadsheet program (such as Excel)

◦ From a database program (such as Access)

◦ From other applications (such as a text editor)

Scanners may be efficient for entering large amounts of data.

Slide 42

Data Editor: Defining Variables, Entering Data & Missing Values

Entering (new) numerical data

Open a new data file (through the menu File�New�Data)

At the bottom of the Data Editor window, switch to Variable View.

◦ Enter age in the first row of the first column.

◦ Enter marital in the second row.

◦ Enter income in the third row.

New variables are automatically assigned the "Numeric" data type.

Slide 43

Switch to the Data View in order to enter values.

To suppress the decimal place for the variables age, marital and income:

◦ At the bottom of the Data Editor window, switch to Variable View.

◦ Select the Decimals column and enter a 0 for age.

◦ Select the Decimals column and enter a 0 for marital.

Slide 44

Adding variable labels and value labels

Enter "Age in years" into the age cell of the "Labels" column.

Do the same for "Marital Status", and so on.

Select the Values cell for marital and open the dialog box.

◦ For Value, enter 1.

◦ For Label, enter "single".

◦ Click on Add so that this designation is registered.

Slide 45

Handling missing values

In general, missing or invalid data should not be ignored.

Sometimes survey participants refuse to answer particular questions.

They may not know an answer, or may respond in an unexpected way.

If these data are not identified or filtered out, your analysis may not yield correct results.

Empty data cells, or cells that con-

tain invalid input, are converted to

missing values, which are displayed

as a period.

Slide 46

The reason why data is missing could be important for your analysis.

For example, for a particular question, it could be useful to distinguish between those who refu-

sed to answer and those for whom the question was not applicable.

In "Variable View" select the Missing cell for income and open the dialog box.

In this dialog box you can specify up to three different missing values, either by defining a range

of values, or particular single values.

Slide 47

Importing Data

Data can be imported from different sources.

◦ Reading an SPSS Data File

SPSS data files have a file extension of *.sav.

◦ Importing data from a spreadsheet

In addition to entering data into the data editor, you can import from programs such as Micro-soft Excel. The column headings serve as variable names.

◦ Importing data from a text file

Text files are common sources of data. Many spreadsheet programs and databases can save their contents in text file format. For example, in CSV files, variables are separated with commas or tabs.

◦ Importing data from a database (not in this course)

Data from a database can be imported with the help of a database wizard.

Slide 48

Importing data from Spreadsheets

Find the Excel file "demo.xls" in www.schwarzpartners.ch/ZNZ_2014/Data_Files

Column headings

are variable names.

Slide 49

Open the Excel file through the SPSS File menu (Excel file must be closed)

Slide 50

Importing data from a text file

Find the text file "demo.txt" in www.schwarzpartners.ch/ZNZ_2014/Data_Files

Open the text file through the SPSS File menu (text file must be closed)

Slide 51

Slide 52

Exercise 01: First Part – Introduction

Ressources => www.schwarzpartners.ch/ZNZ_2014 => Exercises SPSS => Exercise 01

Slide 53

Analysis functions: Analyze

Descriptive Statistics

Summary information about the distribution,

variability, and central tendency of variables.

FrequenciesI

◦ Provides statistics and graphical displays for describing many types of variables.

◦ For a frequency report and bar chart, you can arrange the distinct values in ascending or de-scending order or order the categories by their frequencies. The frequencies report can be suppressed when a variable has many distinct values. You can label charts with frequencies (the default) or percentages.

Statistics and plots: Frequency counts, percentages, cumulative percentages, mean, median,

mode, sum, standard deviation, variance, range, minimum and maximum values, standard error

of the mean, skewness and kurtosis (both with standard errors), quartiles, user-specified percen-

tiles, bar charts, pie charts, and histograms.

Slide 54

Menu FrequenciesI

Slide 55

Example FrequenciesI

FREQUENCIES VARIABLES=age

/FORMAT=NOTABLE

/STATISTICS=STDDEV VARIANCE MINIMUM MAXIMUM MEAN MEDIAN SKEWNESS SESKEW KURTOSIS SEKURT

/HISTOGRAM

/ORDER=ANALYSIS.

Slide 56

DescriptivesI

◦ Displays univariate summary statistics for several variables in a single table

◦ Calculates standardized values (z scores).

◦ Variables can be ordered by the size of their means (in ascending or descending order), alphabetically, or by the order in which you select the variables (the default).

When z scores are saved, they are added to the data in the data editor

Statistics: Sample size, mean, minimum, maximum, standard deviation, variance, range, sum,

standard error of the mean, and kurtosis and skewness with their standard errors.

Slide 57

Example DescriptivesI

DESCRIPTIVES VARIABLES=age

/SAVE

/STATISTICS=MEAN STDDEV MIN MAX.

z scores are saved in the data editor

Slide 58

ExploreI

◦ Produces summary statistics and graphical displays either for all of your cases or separately for groups of cases

◦ Use for: data screening, outlier identification, description, assumption checking, and charac-terizing differences among subpopulations (groups of cases).

Slide 59

Example ExploreI

EXAMINE VARIABLES=income BY gender

/PLOT BOXPLOT

/COMPARE GROUPS

/STATISTICS DESCRIPTIVES

/CINTERVAL 95

/MISSING LISTWISE

/NOTOTAL.

:

Slide 60

Example ExploreI

Numbers indicate cases in the dataset

Slide 61

CrosstabsI

◦ Forms two-way and multiway tables and provides a variety of tests and measures of association

Slide 62

Example CrosstabsI

CROSSTABS

/TABLES=gender BY inccat

/FORMAT=AVALUE TABLES

/CELLS=COUNT ROW

/COUNT ROUND CELL.

Slide 63

Inferential Statistics

SPSS offers univariate and multivariate analysis techniques, including (among many others):

General linear models (GLM)

Survival analysis procedures

Non-parametric procedures

One-Sample t-Test

Use to test the claim that a population mean is equal to a specific value.

Example: Test the hypothesis that in the population the mean age is 45 years.

Slide 64

Hypothesis structure

H0: µ = 45 years

HA: µ ≠ 45 years

SPSS output

Mean of sample age = 42.06 years

The probability of the t-Test is p = .000, assuming the null hypothesis

H0 can be rejected:

The mean age in the population is significantly different than 45 years.

Slide 65

Two-Sample t-Test

Comparing two independent Means.

Example: Test the hypothesis that the mean income of men and woman is different.

Slide 66

Hypothesis structure

H0: µmen = µwomen

HA: µmen ≠ µwomen

SPSS output

When comparing groups, their variances must be relatively similar for the t-test to be used.

Levene's test checks for this. If the significance for Levene's test is

◦ ≤ 0.05, then the row "Equal variances not assumed" is used

◦ > 0.05, then row "Equal variances assumed" is used

Men and women in the population do not differ significantly in terms of their mean income.

(t-test: df = 6398, t = .702, p = .483).

Mean of men's income = 70.1608 [1000 $]

Mean of women's income = 68.7798 [1000 $]

Slide 67

Paired Samples t-Test

Very often the two samples to be compared are not randomly selected:

The second sample is the same as the first after some treatment has been applied.

Example: Influence of diet on body weight of overweight men.

Data set: body_weight.sav

◦ weight_0 = weight at beginning of diet

◦ weight_1 = weight after ¼ year

Slide 68

Hypothesis structure

H0: µbeginning = µafter

HA: µbeginning ≠ µafter

SPSS output

The mean weight at beginning of the diet is not significantly different from

the mean weight after ¼ year of diet. (t-test: df = 98, t = -1.210, p = .229).

Mean weight at beginning = 115.4586 [kg]

Mean weight after ¼ year = 116.3687 [kg

Slide 69

How to choose a statistical test?

Use an inferential statistics decision-making tree!

http://en.wikiversity.org/wiki/Inferential_statistics_decision-making_tree

Tree from UZH (German) www.methodenberatung.uzh.ch

Slide 70

Creating charts

Manuals

This introduction refers to the manual "IBM SPSS Statistics 22 Brief Guide"

Find this manual and also "GPL Reference Guide for IBM SPSS Statistics" here:

◦ www-01.ibm.com/support/docview.wss?uid=swg27038407#en

Slide 71

Creating and Editing Charts

SPSS provides a large number of options for producing charts and diagrams.

The graphics options are available on the Graphs menu.

Either use Chart Builder or Legacy Dialogs which are the old styled commands

Slide 72

Bar Chart – Self-Study 45 minutes

Create a bar chart of mean income for different levels of job satisfaction.

From the menus choose: Graphs�Chart Builder...

Click OK

Slide 73

Exercise 02: Second Part – Additional Topics

Ressources => www.schwarzpartners.ch/ZNZ_2014 => Exercises SPSS => Exercise 02

Slide 74

Notes: