SPSS SLIDES

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SPSS OVERVIEW

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PURPOSE OF TRAINING

•Equip our clients with statistical SPSS

•Equip our clients with the skills to manage data.


MEANING OF STATISTICAL DATA ANALYSIS

• Collection of methods used to process raw data

and report the overall trends.

• Process of systematically applying statistical

and/or logical techniques to describe and

illustrate, condense and recap, and evaluate

data.


REASON FOR STATISTICAL ANALYSISTransform raw data into information

The general purpose of statistical analysis is to provide meaning to

what otherwise would be a collection of numbers and/or values.

Provide a way of drawing inductive inferences from data and

distinguishing the signal (the phenomenon of interest) from the

noise (statistical fluctuations) present in the data

Statistical analysis procedures are categorized according to the type

of statistics generated; i.e descriptive, associative, and inferential.


TYPES OF DATA ANALYSIS

Descriptive statistics portray individuals or events in terms of some predefined characteristics, like measure of central tendency and dispersion –Mean, Median, Range, Standard Deviation, etc.


Associative or relative statistics seek to identify meaningful interrelationships between or among data. Such statistics include; univariate, bivariate and multivariate analysis. For instance, "Is there a relationship between salt intake and diastolic blood pressure among middle-age women?" is a problem definition suitable for analysis by associative statistics.


3. Inferential statistics seek to assess the characteristics of a sample in order to make more general statements about the parent population, or about the relationship between different samples or populations.

• Measures of differences of the means and measures of statistical significance

• For Example; "Does a low sodium diet lower the diastolic blood pressure of middle-age women?" represents a problem definition suitable for inferential statistics.


ISSUES TO CONSIDER IN DATA ANALYSIS

• There are a number of issues to consider with respect to data analysis. These include:

• Having the necessary skills to analyze • Following acceptable norms for data analysis and

presentation • Choosing the appropriate statistical software• Providing honest and accurate analysis • Manner of presenting data • Extent of data analysis


A Statistical package is a computer programme

that specializes in statistical data analysis.


ROLE OF STATISTICAL SOFTWARES IN DATA ANALYSIS

• Input data into the computer• Organise data• Compare data• Manage data• Summarise data (transform raw data into information)• Generate tables and graphs• Facilitate presentation of information and preparation of

analytical reports


ISSUES TO CONSIDER WHEN CHOOSING A STATISTICAL PACKAGE

• Important to know more than one statistical software package

• Analyse your needs with respect to data management and analysis; and choose a package that addresses the needs

• Ease of importing and exporting data to other computer programmes

• Ease of transferring the output into word processing facilities

• General Vs Specialized purpose statistical software


Statistical package for Social Scientists(SPSS)


SPSS interface• Data view

• The place to enter data

• Columns: variables

• Rows: records

• Variable view

• The place to enter variables

• List of all variables

• Characteristics of all variables


Variables

• A variable is any characteristic to be measured that

varies from one individual member of the population

to another. Every question on the questionnaire is a

variable.


Example of Variable• Considering human in a study; variables include:

• Age, • Height, • Sex, • Weight, • Location • Race, etc.


Types of Scales• Nominal- objects or people are categorized according to some criterion (gender, job category)

• Ordinal- Categories which are ranked according to characteristics (income- low, moderate, high)

• Scale - contain numerical measures. They are solely quantitative.


Types of Data• A data set may contain a mixture of several data types.

• These data types may be broadly classified as either string, categorical or numerical data.


Categorical Data• These data have values that are described by words rather than

numbers. They are also called qualitative data.

• Examples include• Data on marital status of students (single, married, divorce);

• Sex distribution data of lecturers in Ghana (males, females).


Numerical Data• Data arise from counting, measuring something, or from some

kind of mathematical operation

• It is called quantitative data


PREPARING A CODEBOOK


Before you can enter the information from your questionnaire, interviews or experiment into SPSS it is necessary to prepare a ‘codebook’.

This is a summary of the instructions you will use to convert the information obtained from each subject or case into a format that SPSS can understand.


•Preparing the codebook involves deciding (and documenting) how you will go about:

1. defining and labelling each of the variables;

2. assigning numbers to each of the possible responses.


NOTE!!

•All this information should be recorded in a book or computer file. Keep this somewhere safe; there is nothing worse than coming back to a data file that you haven’t used for a while and wondering what the abbreviations and numbers refer to.


EXAMPLE OF CODEBOOKVariable SPSS Variable name Coding instructions

SEX SEX 1=FEMALE2=MALE

MARITAL STATUS MARITAL 1=MARRIED2=SINGLE3=DIVORCED4=WIDOWED


CODING RESPONSES


CODING CLOSED-ENDED QUESTIONS


•Each response must be assigned a numerical code before it can be entered into SPSS. Some of the information will already be in this format (e.g. age in years), other variables such as sex will need to be converted to numbers (e.g. 1=males, 2=females).


Example question• What is your current marital status? (please tick)Single In a relationship Married Divorced


•To code responses to the question above: if a person ticked single, they would be coded as 1; if in a relationship, they would be coded 2; if married, 3; and if divorced, 4


CODING OPEN-ENDED QUESTIONS


• For open-ended questions (where respondents can provide their own answers), coding is slightly more complicated.

• Example: What is the major source of your income?

• To code responses to this you will need to scan through the questionnaires and look for common themes. You might notice a lot of respondents listing their source of income.


• In your codebook you list these major groups of responses under the variable name stress, and assign a number to each (work=1, finances=2 and so on).

• You also need to add another numerical code for responses that did not fall into these listed categories (other=value).

• When entering the data for each respondent you compare his/her response with those listed in the codebook and enter the appropriate number into the data set under the variable name you give.


• Once you have drawn up your codebook, you are almost ready to enter your data. There are two things you need to do first:

1. Get to know SPSS, how to open and close files, become familiar with the various ‘windows’ and dialogue boxes that it uses.

2. Set up a data file, using the information you have prepared in your codebook


DEFINING THE VARIABLES


•Before you can enter your data, you need to tell SPSS about your variable names and coding instructions.

•This is called ‘defining the variables’


• NameIn this column, type in the variable name that will be used to identify each of the variables in the data file. These should be listed in your codebook.

• .


Rules for naming of variables

• Variable names: must be unique (i.E. Each variable in a data set must

have a different name);

• Must begin with a letter (not a number);

• Cannot include full stops, blanks or other characters (!, ? * ‘’);

• Cannot include words used as commands by SPSS (all, ne, eq, to, le,

lt, by, or, gt, and, not, ge, with); and


• Type• The default value for Type that will appear automatically as you enter your first variable name is Numeric. For most purposes this is all you will need to use. There are some circumstances where other options may be appropriate


• WidthThe default value for Width is 8. This is usually sufficient for

most data. If your variable has very large values you may need to change this default value, otherwise leave it as is.

DecimalsThe default value for Decimals (which I have set up using

the Options facility described earlier in this chapter) is 0. If your variable has decimal places, change this to suit your needs. If all your variables require decimal places, change this under Options (using the Data tab). This will save you a lot of time manually changing each of the variables.


• LabelThe Label column allows you to provide a longer description for your variable than the eight characters that are permitted under the Variable name. This will be used in the output generated from the analyses conducted by SPSS.

ValuesIn the Values column you can define the meaning of the values you have used to code your variables. I will demonstrate this process for the variable ‘Sex’.


1. Click on the three dots on the right-hand side of the cell. This opens the Value Label dialogue box.

2. Click in the box marked Value. Type in 1.3. Click in the box marked Value Label. Type in Male.4. Click on Add. You will then see in the summary box:

1=Male.5. Repeat for Females: Value: enter 2, Value Label: enter

Female. Add.6. When you have finished defining all the possible values

(as listed in your codebook),click on Continue.


• MissingSometimes researchers assign specific values to indicate

missing values for their data. This is not essential—SPSS will recognise any blank cell as missing data. So if you intend to leave a blank when a piece of information is not available, it is not necessary to do anything with this Variable View column.

ColumnsThe default column width is usually set at 8. This is

sufficient for most purposes— change it only if necessary to accommodate your values. To make your data file smaller (to fit more on the screen), you may choose to reduce the column width. Just make sure you allow enough space for the width of the variable name.


• MeasureThe column heading Measure refers to the level of measurement of each of your variables. The default is Scale, which refers to an interval or ratio level of measurement. If your variable consists of categories (e.g. sex), then click in the cell, and then on the arrow key that appears. Choose Nominal for categorical data, and Ordinal if your data involve rankings, or ordered values.


Opening an existing data file

• If you wish to open an existing data file click on File from the menu across the top of the screen, and then choose Open, and then Data. The Open File dialogue box will allow you to search through the various directories on your computer to find where your data file is stored.


• There are a number of different ways to start SPSS:

• The simplest way is to look for an SPSS icon on your desktop. Place your cursor on the icon and double-click.

• You can also start SPSS by clicking on Start, move your cursor up to Programs, and then across to the list of programs available. Move up or down until you find SPSS for Windows.

• SPSS will also start up if you double-click on an SPSS data file listed in

• Windows Explorer—these files have a .sav extension.


CAUTION

•You should always open data files from the hard drive of your computer, not the Floppy or A:

drive. If you have data on a floppy disk, transfer it to a

folder on the hard drive of your computer before opening it.


Saving a data file• Remember to save your data file. This does not happen automatically, as in some word processing programs. If you don’t save regularly, and there is a power blackout or you accidentally press the wrong key, you will lose all of your work. So save yourself the heartache and save regularly. If you are entering data, this may need to be as often as every ten minutes or after every five or ten questionnaires.


•To save a file you are working on, go to the File menu (top left-hand corner) and choose Save. Or, if you prefer, you can also click on the icon that looks like a floppy disk, which appears on the toolbar at the top, left of your screen.


CREATING A DATA FILE AND ENTERING DATA


Process

Step 3. Enter the data—that is, the values obtained from each participant or respondent for each

variable.

Step 2. Set up the structure of the data file by ‘defining’ the variables.

Step 1. Check and modify, where necessary, the options that SPSS uses to display the data and the

output that is produced.


DEFINING MULTIPLE RESPONSE SETS


From the menus, choose:DataDefine Multiple Response SetsSelect two or more variables. If your variables are coded

as dichotomies, indicate which value you want to have counted.

Enter a unique name for each multiple response set. The name can be up to 63 bytes long. A dollar sign is automatically added to the beginning of the set name.

Enter a descriptive label for the set. (This is optional.)Click Add to add the multiple response set to the list of

defined sets.


DATA TRANSFORMATIONS


Computing Variables

• Use the Compute dialog box to compute values for a variable based on numeric transformations of other variables.

• You can compute values for numeric or string �(alphanumeric) variables.

• You can create new variables or replace the values of �existing variables. For new variables, you can also specify the variable type and label.


• From the menus choose:• Transform• Compute Variable• Type the name of a single target variable. It can be an

existing variable or a new variable to be Added to the active dataset.

• To build an expression, either paste components into the Expression field or type directly in the Expression field.


ENTERING DATA


Procedure for entering data1. To enter data you need to have the Data View active. Click on

the Data View tab at the bottom left-hand side of the screen. A spreadsheet should appear with your newly defined variable names listed across the top.

2. Click on the first cell of the data set (first column, first row). A dark border should appear around the active cell.

3. Type in the number (if this variable is ID this should be 1, that is case or questionnaire number 1).

4. Press the right arrow key on your keyboard; this will move the cursor into the second cell, ready to enter your second piece of information for case number 1


5. Move across the row, entering all the information for case 1, making sure that the values are entered in the correct columns.

6. To move back to the start, press the Home key on your keypad. Press the down arrow to move to the second row, and enter the data for case 2.

7. If you make a mistake and wish to change a value: Click in the cell that contains the error. The number will appear in the section above the table. Type the correct value in and then press the right arrow key.


DATA ENTRY USING EXCEL


•Data files can be prepared in the Microsoft Excel program and then imported into SPSS for analysis.


• Step 1: Set up the variable names. Set up an Excel spreadsheet with the variable names in

the first row across the page. The variable names must conform to the SPSS rules for naming variables.

• Step 2: Enter the dataEnter the information for the first case on one line across

the page, using the appropriate columns for each variable. Repeat for each of the remaining cases. Click on File, Save. In the section marked Save as Type make sure ‘Microsoft Excel Workbook’ is selected. Type in an appropriate file name.


• Step 3: Converting to SPSS format• After you have entered the data, save your file and then close Excel. Start SPSS and, with the Data Editor open on the screen, click on File, Open, Data, from the menu at the top of the screen. In the section labelled Files of Type choose Excel. Excel files have a .xls extension. Find the file that contains your data. Click on it so that it appears in the File name section. Click on the Open button. A screen will appear labelled Opening Excel Data Source. Make sure there is a tick in the box: Read variable names from the first row of data. Click on OK.


• Step 4: Saving as an SPSS file• When you have completed this process of fully defining the variables, you need to save your file as an SPSS file. Choose File, and then Save As from the menu at the top of the screen. Type in a suitable file name. Make sure that the Save as Type is set at SPSS (*.sav). Click on Save. When you wish to open this file later to analyse your data using SPSS, make sure you choose the file that has a .sav extension (not your original Excel file that has an .xls extension).


Summary

Set up the variable names

• 1

Enter the data

• 2

Convert to SPSS format

• 3

Save as an SPSS file

• 4


SCREENING AND CLEANING THE DATA


•Before you start to analyse your data it is essential that you check your data set for errors. It is very easy to make mistakes when entering data, and unfortunately some errors can completely mess up your analyses. For example, entering 35when you mean to enter 3 can distort the results.


The data screening process involves a number of steps:

Step 1: Checking for errors. First, you need to check each of your variables for scores that are out of range (i.e. not within the range of possible scores).

Step 2: Finding the error in the data file. Second, you need to find where in the data file this error occurred (i.e. which case is involved).

Step 3: Correcting the error in the data file. Finally, you need to correct the error in the data file itself.


Checking for errors• When checking for errors you are primarily looking for

values that fall outside the range of possible values for a variable. For example, if sex is coded 1=male, 2=female, you should not find any scores other than 1 or 2 for this variable.


• To check for errors you will need to inspect the frequencies for each of your variables. This includes all of the individual items that make up the scales.


Procedure for checking categorical variables1. From the main menu at the top of the screen click on:

Analyze, then click on Descriptive Statistics, then Frequencies.

2. Choose the variables that you wish to check (e.g. sex, marital, educ.).

3. Click on the arrow button to move these into the variable box.

4. Click on the Statistics button. Tick Minimum and Maximum in the Dispersion section.

5. Click on Continue and then on OK. The output generated using this procedure is displayed

below (only selected output is displayed).


Data Cleaning

Wrong entry


DATA ANALYSIS


• Once you are sure there are no errors in the data file (or at least no out-of-range values on any of the variables), you can begin the descriptive phase of your data analysis.

• Descriptive statistics have a number of uses. These include:

To describe the characteristics of your sample in the method section of your report;

To check your variables for any violation of the assumptions underlying the statistical techniques that you will use to address your research questions; and

To address specific research questions.


Categorical variables• To obtain descriptive statistics for categorical variables

you should use Frequencies. • This will tell you how many people gave each response

(e.g. how many males, how many females). • It doesn’t make any sense asking for means, standard

deviations etc. for categorical variables, such as sex or marital status.


Descriptive Statistics• Descriptive statistics have a number of uses. These

include: • to describe the characteristics of your sample in the

Method section of your report; • to check your variables for any violation of the

assumptions underlying the statistical techniques that you will use to address your research questions; and

• to address specific research questions.


Frequencies (mostly for categorical variables)

• Analyze, then click on • Descriptive Statistics, • Frequencies.• Select the categorical variables you are interested in (e.g. sex) and move them into the Variables box.

• Click on the Statistics button. In the Dispersion section tick Minimum and Maximum.

• Click on Continue and then OK.


Descriptive (mostly for continuous variables)

• Analyze, then click on • Descriptive Statistics, • Descriptives.• Select all the continuous variables that you wish to obtain

descriptive statistics• Click on the arrow button to move them into the Variables

box (e.g. age).• Click on the Options button. • Click on mean, standard deviation, minimum,• maximum, • Click on Continue, and then OK


EXPLORE


• It gives a detailed descriptive analysis of the variables. It presents a number of information about the variables.

• It provides results for:Mean Median Variance PercentileKurtosisSkewness Etc..


CROSS TABULATION


• Analyze, then click on • Descriptive Statistics, • Cross tabs.• Select the categorical variables you are interested in (e.g. sex) and move them into the Variables boxes.

• Click on the Statistics button. In the Dispersion section tick Minimum and Maximum.

• Click on Continue and then OK.


ASSOCIATIVE STATISTICS


Associative Statistics• Associative statistics seek to identify meaningful

interrelationships between or among data. Such statistics include; univariate, bivariate and multivariate analysis. It focus is on detecting and describing relationships among variables. These techniques can be used to:

explore the association between pairs of variablespredict scores on one variable from scores on another

variable (bivariate regression);predict scores on a dependent variable from scores of a

number of independent variables (multiple regression);


•Correlation •Regression (Linear, Multiple)


CORRELATION


•Correlation is used when you wish to describe the strength and direction of the relationship between two variables (usually continuous). It can also be used when one of the variables is dichotomous—that is, it has only two values (e.g. sex: males/females).


• Partial correlation is used when you wish to explore the relationship between two variables while statistically controlling for a third variable. This is useful when you suspect that the relationship between your two variables of interest may be influenced, or confounded, by the impact of a third variable..


•Partial correlation statistically removes the influence of the third variable, giving a cleaner picture of the actual relationship between your two variables


Summary of bivariate correlation.Example of research question:

Is there a relationship between the amount of control people have over their internal states and their levels of perceived stress?

What you need:

Two variables: both continuous, or one continuous and the other categorical.

What it does Correlation describes the relationship between two variables, in terms of both the strength of the relationship and the direction.


Analyze, Correlate, BivariateSelect your variables and move them into the box marked as variables.

Check that the Pearson box and the 2 tail box have a cross in them. The two-tail test of significance means that you are not making any specific prediction concerning the direction of the relationship between the variables (positive/negative). You can choose a one-tail test of significance if you have reasons to support a specific direction.

Click OK


Summary of partial correlation.Example of research question:

After controlling for age, is there still a significant relationship between perceived control of internal states (PCOISS) and levels of perceived stress?

What you need: three variables: all continuous;• two variables that you wish to explore the relationshipbetween (e.g. total PCOISS, total perceived stress); and• one variable that you wish to control for (age)

What it does It allows you to explore the relationship between two variables, while statistically controlling for (getting rid of) the effect of another variable that you think might be contaminating or influencing the relationship


Analyze, Correlate, Partial Click on the two continuous variables that you want to correlate.

Click on the variable that you wish to control for (e.g. age) and move into the Controlling box.

Choose whether you want one-tail or two-tail significance

Check that the Pearson box and the 2 tail box have a cross in them.

Click OK


NOTE• Pearson product-moment coefficient is designed for

interval level (continuous) variables. It can also be used if you have one continuous variable and one dichotomous variable

• Spearman rank order correlation (designed for use with ordinal level or ranked data)


REGRESSION


• Multiple regression is not just one technique but a family of techniques that can be used to explore the relationship between one continuous dependent variable and a number of independent variables or predictors (usually continuous). Multiple regression is based on correlation, but allows a more sophisticated exploration of the interrelationship among a set of variables. It can tell you how well a set of variables is able to predict a particular outcome.


Types • Linear – between two variables (1 independent variable, 1

dependent variable)• Multiple between more than two variables (2 or more

independent variables, 1 dependent variable)


Summary of partial correlation.

Example of research question:

What is the impact of feeding practices on child growth

What you need: One continuous dependent variable; andOne, Two or more continuous independent variables

What it does Multiple regression tells you how much of the variance in your dependent variable can be explained by your independent variables. It also gives you an indication of the relative contribution of each independent variable.


Analyze, then click on Regression, then on Linear.Click on your continuous dependent variable and move it

into the Dependent box.Click on your independent variables and move them into

the Independent box.For Method, make sure Enter is selected (this will give you

standard multiple regression).Click on OK


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