spss lessons

USES OF SPSS

SPSS (Statistical Package for the Social Sciences) is a widely used software program for statistical analysis and data management. It is popular among researchers, statisticians, social scientists, and analysts for its versatility and robust capabilities. Here are some common uses of SPSS software:

Data Entry and Management:

SPSS allows users to input, edit, and manage datasets efficiently. It provides tools for cleaning and transforming data, handling missing values, and recoding variables.

Descriptive Statistics:

Users can generate various descriptive statistics, such as measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation, range), and frequency distributions.

Data Visualization:

SPSS offers graphical tools to create charts, graphs, and plots (e.g., histograms, bar charts, scatterplots) to visualize data distributions and relationships.

Hypothesis Testing:

SPSS supports a wide range of statistical tests for hypothesis testing, including t-tests, chi-squared tests, ANOVA (Analysis of Variance), correlation analysis, and regression analysis. Researchers can assess the significance of relationships between variables and test research hypotheses.

Nonparametric Statistics:

SPSS provides nonparametric tests like the Wilcoxon signed-rank test and Mann-Whitney U test for data that do not meet parametric assumptions.

Survival Analysis:

Researchers in medical and social sciences can use SPSS for survival analysis to examine time-to-event data, such as survival curves and Cox proportional hazards regression.

Factor Analysis and Structural Equation Modeling (SEM):

SPSS supports factor analysis and SEM, which are used for exploring latent factors and complex relationships in data.

Cluster Analysis:

Users can perform cluster analysis to group similar cases or observations based on selected variables.

Time Series Analysis:

SPSS can be used for time series analysis, including forecasting and modeling trends and seasonality in time-dependent data.

Custom Scripting and Syntax:

Experienced users can write custom scripts using SPSS syntax, enabling automation and customization of analyses.

Integration with Other Software:

SPSS can import and export data from various file formats, including Excel, CSV, and other statistical software formats, enhancing data compatibility and interoperability.

Reporting and Output:

SPSS generates comprehensive output reports that include analysis results, charts, and tables, making it easier to communicate findings to stakeholders and collaborators.

Survey and Questionnaire Analysis:

Researchers can analyze survey data, calculate reliability scores (e.g., Cronbach’s alpha), and perform factor analysis on questionnaire items.

Geospatial Analysis:

With the addition of optional extensions like “IBM SPSS Statistics – Spatial Statistics,” SPSS can handle geospatial data and perform spatial analysis.

Machine Learning Integration:

Recent versions of SPSS have integrated machine learning capabilities, allowing users to build predictive models and perform classification and regression tasks.

SPSS is a versatile tool that serves various purposes in academia, research, business, and government. Its user-friendly interface and wide range of statistical functions make it valuable for both beginners and advanced data analysts.

Epidata

Epidata is a software tool commonly used for data entry and management in epidemiological and public health research. While specific steps can vary slightly depending on the version of Epidata you are using, here are the general steps for data entry using Epidata:

Design Your Data Entry Form

Create a Data Entry Form: Open Epidata and create a new project. Design your data entry form by specifying the variables, data types, and any validation rules necessary to ensure data accuracy.

Data Entry

Enter Data: Enter the data into the form for each participant or case. Use the designed form to input information accurately.

Data Validation

Validation Checks: Epidata allows you to set up validation checks for data entered. These checks ensure that the data falls within specified ranges or meets certain criteria. Validate the data to identify and correct errors.

Data Cleaning

Identify and Correct Errors: Review the data for any inconsistencies or errors. Use Epidata’s features to identify outliers and discrepancies. Clean the data by correcting errors and ensuring consistency.

Save and Backup Data

Save Your Project: Save your Epidata project frequently to avoid data loss.

Backup Data: Regularly backup your data to prevent loss due to technical issues.

Export Data

Export Data: Once your data entry is complete and validated, export the data to the desired format (such as Excel, CSV) for further analysis.

Documentation

Documentation: Document any changes made to the data, the validation checks applied, and any cleaning procedures performed. Clear documentation ensures transparency and reproducibility of your research.

Step 9: Quality Control

Quality Control: Implement quality control measures by having another team member review the data independently to identify any missed errors or inconsistencies.

Step 10: Analysis and Reporting

Data Analysis: Import the cleaned and validated data into statistical software (e.g., SPSS, R, SAS) for analysis.

Reporting: Generate reports, charts, and visualizations based on your analysis. Interpret the results and draw conclusions.

Remember that specific steps might vary based on the version of Epidata you are using, so always refer to the official documentation or user guides provided by the Epidata team for detailed and version-specific instructions.

TYPES OF DATA

Data can be categorized into several types based on its nature, characteristics, and measurement scales. The main types of data include:

Nominal Data:

Nominal data consists of categories or labels with no inherent order or ranking. Each category represents a distinct group, but there is no meaningful numeric value associated with them. Examples include gender (male, female), colors (red, blue, green), and types of fruits (apple, banana, orange).

Ordinal Data:

Ordinal data represents categories or labels with a natural order or ranking, but the intervals between categories are not equal or defined. Ordinal data allows us to determine the relative order of values, but we cannot make precise statements about the magnitude of the differences between them. Examples include education levels (high school, bachelor’s, master’s), customer satisfaction ratings (very dissatisfied, dissatisfied, neutral, satisfied, very satisfied), and socioeconomic status (low, middle, high).

Interval Data:

Interval data has numeric values with equal intervals between them, but it lacks a meaningful zero point. In interval data, mathematical operations like addition and subtraction are meaningful, but multiplication and division are not. Examples include temperature in Celsius or Fahrenheit and calendar dates (e.g., years, months, days).

Ratio Data:

Ratio data also has numeric values with equal intervals between them, but it includes a meaningful zero point, indicating the absence of the measured attribute. In ratio data, all four arithmetic operations (addition, subtraction, multiplication, and division) are meaningful. Examples include age, height, weight, income, and distance.

Discrete Data:

Discrete data consists of separate, distinct values that are typically counted as whole numbers. Discrete data is countable and often represents items that can be individually identified. Examples include the number of students in a class, the number of cars in a parking lot, and the number of books on a shelf.

Continuous Data:

Continuous data represents values that can take on an infinite number of values within a given range. It includes fractional and decimal values, and there are no gaps or discontinuities between values. Examples include height measured in centimeters, temperature measured in degrees, and weight measured in kilograms.

Qualitative Data:

Qualitative data, also known as categorical data, includes nominal and ordinal data. It represents categories, labels, or qualitative attributes that do not have a numeric value.

Quantitative Data:

Quantitative data includes interval and ratio data. It represents measurable quantities with numeric values and allows for mathematical operations.

Understanding the type of data you are working with is crucial for selecting appropriate statistical analyses and visualization techniques. Different data types require different methods for analysis and interpretation.

SPSS is statistical package for the social sciences. This is general software tailored to the needs of social scientist. This software is very important in Econometrics, social sciences, Banks, medical Research and general Research. Compared to other software, it is more intuitive and Easier to learn however the main challenges include;

• Less flexibility
• Fewer options

In advanced statistical then some other statistical software like S – PLVS, R and SAS.

SPSS is generally good for organizing and analyzing data. The reader needs to first install Spss software which is available on line. It is an extensive package with facilities for data entry, data manipulation and statistical analysis.

SPSS has modules for;

• Survey analysis
• Graphical display
• Time series

BASIC STRUCTURE OF SPSS

There are basically two windows in SPSS

1^st – Data Editor Window – this consists mainly of data view and variable view.

DATA VIEW

The Data View displays data for each variable.

VARIABLE VIEW

Displays the specifications for each variable in SPSS data set and is used for creating and modifying the variables. It is where variables are designed from.

Variable output window

Saving contents of data window

• Click on file
• Select save or save as
• Assign a file name and click save

2^nd – output viewer window –    shows results of data Analysis.

OUTPUT WINDOW

This displays all your results after command is processed.

Saving contents of output window

• Click on file
• Select save or save as
• Assign a file name and c lick save
• An extension SPO By default will be assigned.

NOTE:

You must save the Data Editor Window and Output Viewer Window separately. Ensure that you save both windows if you want to save your changes in data or analysis.

Output of data

The other option

After running any analysis when the resorts appear on the output window, the best way is to copy the results from the output window to a Microsoft window.

Explanation of Data view

• In the Data view Rows are cases.
• Columns are variables.

Definition of variables

A variable is any characteristics, number or quantity that can be measured or counted such as Age, sex, income and country.

Types of variables:

Quantitative/ continuous variables

These have values that describe a measurable Quantity such as a number like how many, how much such as height, age time and weight.

Qualitative/ categorical variables

These have values that describe a quality or “characteristic” of a data unit, like “what type or which category”.

These values are usually coded with values assigned to different categories in that variable e.g Educational level, gender.

All always remember

Categorical variables are coded.

Stating SPSS program

There basically two different ways of opening/ launching SPSS program.

• The first one is to simply click on the SPSS icon shown on your desktop.
• If you cannot find the icon, you can click start on the Bottom of your screen, then program files, when the SPSS for windows, when the SPSS window launches, a dialogue box will pop up as shown below.

Diagram

When there is a former existing SPSS file on your computer you can just type ctrl N (this is the same as opening a new Microsoft word in windows).

The output will look like this.

1             2

THE SCREEN OF SPSS

Arrow 1 shows Data view

Arrow 2 shows variable view

The variable view SPSS

Explanation of variable view:

First column-Name

This section of the variable view you need to insert the Name or title for example; when the sentence is Age of respondents.

The Name can be “Age” Remember you do not Need to Leave space when typing.

Second column-Type

This section indicates the type of data to be entered and it includes;

• Numeric
• Comma
• Scientific notation
• Date
• Dollar
• Custom currency
• String
• Restrict Numeric (Integer with leading zeros).

Third column-Width

Type the variable width e.g 2 (width adjustable).

Forth column- Decimals

This section indicates the total number of Decimal places a specific number has e.g/ or for example 20.00, this indicates that the Decimal places are 2.

However if the number does not have decimals indicate 0 for example a number like 20

Fifth column-Label

This section describes name in details e.g if name is “gender” then it’s label is “gender of respondents”.

Sixth column-Values

This has to be specified each time for categorical variables.

e.g 1. male and 2. Female

Missing Values

Codes which are not interest in data of data analysis.

For example, if you have sex and codes 1- male or 2- female but a respondent says he is both.

Measures

The tenth measure

Measure is the nature of the kind of data that is being entered.

Measure has three options:

Scale: [Measurable data such as age and income]

Ordinal: This is data that is usually coded such as Education level (primary, secondary and tertiary).

Nominal Data

This is data is Random with no linear sequence.

Entering Data and Editing Data

Once all the variables have been entered.

Click Data view then proceed with Data entry.

Example

Entering the following Data in SPSS.

The variable would appear like this;

CALCULATING MEAN, FREQUENCY, STANDARD DEVIATION

Click _ Analyze

Descriptive statistics

Frequencies

Select the variable you want to analyze

Then click charts (if you want the Diagram)

(click statistics)

Then select mean, std…..

Click continue

Click ok.

CREATING ADUMMY VARIABLE

• Transform
• Select the variable and sent it into variable output.
• Area of Name indicate the New Name of the variable (D male)
• Label – indicate the label name your changing.
• Click OLD and New Values.
• OLD value – may be if the label for male is 1 indicate 1 New value also indicate 1.
• Old value which does not indicate your intended label indicate it then New value indicate 0.

The click adds.

Then click continue.

Then click ok.

CROSS TABULATION:

This helps us in understanding how much of a specific variable has a relationship with the other.

For example

STEPS

• Analyze
• Descriptive statistics
• Cross tabulations
• The select variables for now
• Select variables for columns

DATA ANALYSIS

This involves 5 major steps

1. Enter your Data in the Data Editor.
2. Select the procedure from the menu.
3. Select variables for the Analysis.
4. Examine the results in the output window.
5. Interpret the Results in the word document.

NOTE

Before any data analysis is done, first identify whether the variable(s) is/are Quantitative or categorical.

It includes Univariate analysis, Bivariate analysis and Multi- variate analysis.

For Univariate analysis, a single variable is analyzed at a time e.g what is the Average age of students?

Bivariate- two variables

Does the income of the respondent depend on age?

Multivariate- more than two variables

Does income of the respond depend on age, Education level and sex of the respond.

UNIVARIATE ANALYSIS

Descriptive statistics

These are computed only for Quantitative/ continuous variables such as age, height and weight.

Procedure

• Analyze _ descriptive statistics _ descriptive.
• Select variables from the LH box into RH box.
• The user can specify the particular statistics required by selecting “options” or statistics button.
• Press ok

Interpret the resorts i.e  mean, median, mode, frequency, Quantile sum, variable, standard deviation, minimum, maximum, range, kurtosis and skewness.

Example

To get the minimum, maximum, mean, stdn, variable.

Click

Analyze_ descriptive statistics_ descriptive.

The output will be as;

Frequency Distribution

This done for categorical/ Qualitative variables such as sex, marital status and age group.

GRAPHING

Steps

Graphs

Legacy dialogues

The choose the type of diagram you want.

A PIE CHART

This is done for categorical/ Qualitative variables such as sex and Educational.

BOX PLOT

• Graph _ legacy dialogs_ box plots simple
• Select summaries of separate variables
• Define
• Select the continuous variables to be charted
• Press ok

HISTOGRAM

• Graphs legacy dialogs_ histogram
• Select variables
• Select display normal curve
• Press ok

LINE GRAPH

• Graph_ legacy dialogue_ line_ simple
• Select values of individual cases
• Define
• Select Y and X-axis variables
• Press ok

Multiple responses/open ended questions

This is the case where each respondent gives one or more than one answer to a particular question e.g what are reasons for high children dropouts in some parts of Uganda.

Example

Opinion poll about Rwanda elections

People were asked to give their opinion as to why Museveni won the recent presidential election in Museveni here below is data of their responses.

1. Best candidate
2. Rigged the election
3. He is a dictator
4. He was the only candidate

Response

• 1
• 2,3
• 2,3,4
• 3,4
• 2,3,4
• 2,4
• 3,2

According to the survey if Museveni win election?

Method 1 / Dichotomics method

STEPS

DATA ENTRY

• Analyze
• Multiple Responses
• Define sets
• Move the desired variables from set definition box to variables set box
• Click on dichotomies counted values
• Put 1
• Go to name Reasons
• Label why Kagame won
• Click on add
• Close
• Go to analyze
• Multiple responses
• Frequencies

The output appears as below show output

Interpretation

The above analysis shows that the main reasons for Museveni’s win were that he rigged the Election and that He is a dictator as reported by 30.8% of the response in either cases. This is followed by reason that he was the only candidate as reported by 15.4% of the response. The other minor reason was that he was the best candidate reported by only 7.7% of the reason.

Method 2 / categories method

STEPS

Data Entry

• Analyze
• Multiple Responses
• Define sets
• Move the desired variable from set definition box to variables set box
• Click on categorical values
• Put 1 through 4 (it depends on the number of reasons you have)
• Go to name-reasons
• Label-why-Museveni won
• Click on add
• Close
• Go to analyze
• Multiple response
• Frequencies

The table will appear

NB: Interpretation will appear as in table 1

Bivariate Analysis

This is only done for categorical variables.

CORRELATION ANALYSIS

This is a measure of relationship between two variables.

It tells us how strong the correlation between the two variables.

The relationship could be negative (-) or positive (+) if the correlation coefficient (p) =1, then there is perfect positive correlation between the variables and if it is = -1 then there is perfect negative correlation between variables.

If p>0.5, there is a strong relationship between the variables.

If p= 0.5, the relationship is moderate.

If p<0.5, there is weak relationship between the variables.

If p<0, then the relationship is very weak.

NOTE

In correlation analysis, we analyze the strength, direction and significance of relationship.

DIRECTION

If the correlation coefficient is negative, it implies the two variables are moving in the different directions, as one variables increases another one decreases. If the correlation coefficient positive, it implies that the two variables are moving in the same direction, as one variable increases, another variable also increases.

Significance of the relationship

If the P-value is less than the level of significance such as 0.05, and 0.01, then the relationship is significance otherwise it is insignificant.

Testing for correlation

1. Graphical approach Ascalter plot is used.

The scalter plot illustrates relationship between the variables which can be positive, negative or non-existing.

• Graphs legacy Dialogs_ scalter_ simple define
• Select the Y and X-axis variables
• Press ok
• To add the line of best fit, double click in the plot and click on add a reference line from equation.

The following are scalter plots for visual interpretations of types of correlations

Example

Using the data

Is there any relationship between the variables?

Statistical tests

Reason correlation coefficient

This is used for qualitative variable such as age and income.

For example

Is there any significant correlation between age and income of the respondent.

The Hypotheses are stated as follows

Ho: There is no significant correlation between age and income of the respondent.

Ha: There is a significant correlation between age and income of the respondent.

STEPS

• Analyze _ correlate_ bivariate.
• Select the variables from the LH box into the RH box.
• Select flag significant correlations.
• Select type of correlation coefficient person.
• Press ok.
• Interpret the result.

Example

Interpretation

The correlation coefficient is -0.259, this implies that there a weak negative correlation between highest year of school completed and age of the respondent. The correlation significant at 1% level of significance since the P-value (0.000) < or thus the null hypothesis is rejected and conclusion made there is significant relationship between highest year of school complete and age of the respondent.

1. Spearman- deals with ranked.
2. Kendal’s- categorical variables of some order such as Education level.

Example

Using the

If the confidence internal does not include the hypothesized value the population parameter, the null hypothesis is rejected otherwise it is accepted.

Chi-square test

It is a test of dependence or association between two variables which must be categorical such as marital status, education level, and religion e.t.c.

Example

Does religion of the respondent depend on marital status.

Procedure

Analyze >> descriptive statistics >> cross tabs

Select one variable for arrow and another for a column.

Click statistics >>………..square >> continue

Cells >>> row and column percent- age >> continue

Press ok

NOTE:

First state the Hypothesis

Ho: religion of the respondent does not depend on marital status.

Ha: Religion of the respondent depends on marital.

one-tailed test or a two-tailed test

Should you use a one-tailed test or a two-tailed test for your data analysis?

Quantitative Methodology

Quantitative Results

When creating your data analysis plan or working on your results, you may have to decide if your statistical test should be a one-tailed test or a two-tailed test (also known as “directional” and “non-directional” tests respectively). So, what exactly is the difference between the two? First, it may be helpful to know what the term “tail” means in this context.

The tail refers to the end of the distribution of the test statistic for the particular analysis that you are conducting. For example, a t-test uses the t distribution, and an analysis of variance (ANOVA) uses the F distribution. The distribution of the test statistic can have one or two tails depending on its shape (see the figure below). The black-shaded areas of the distributions in the figure are the tails. Symmetrical distributions like the t and z distributions have two tails. Asymmetrical distributions like the F and chi-square distributions have only one tail. This means that analyses such as ANOVA and chi-square tests do not have a “one-tailed vs. two-tailed” option, because the distributions they are based on have only one tail

SAMPLE T-TESTS

This is used for testing means.

SAMPLE TESTS IN SPSS

• One sample t-tests.
• Paired sample t-test.
• Independent sample t-test.
• ANOVA test.

Please always remember that;

• One sample t-test is used to compare the mean of one variable from a target value.
• Paired sample t-test is used to compared the mean of two variables for a single group.
• Independent sample t-test is used to compare means of two distinct groups of cases e.g alive or dead, on off, men or women e.t.c.
• ANOVA is used for testing several means.

One sample t-test

One sample t-test is performed when you want to determine if the mean value of a target variable is different from any pothesized value.

Examples

• A researcher might want to test whether the average age to respondent differs from 52.
• A researcher might want to test whether the average marked students differs from 75.

Assumptions for the one sample t-test

• The dependent variable is normally distributed with in the population.
• The data are independent.

Example 1

A study on the physical strength measured in Kilograms on 7 subjects before and after a specified training period gave the following results.

Is there a difference in the physical strength before and after a specified training period?

• State the hypothesis.
• Use t-test to show that there is no mean difference in the physical strength before and after a specified training period.

Solution

First compute a new variable diff in the physical strength before and after a specified training period?

• State the hypothesis.
• Use t-test to show that there is no mean difference in the physical strength before and after a specified training period.

Solution

First compute a new variable diff-the difference between the value and the before value.

STEPS

• Transform- compute.
• For target variable type diff for numeric, expression type and after-before.
• Click ok.
• Analyze-compare means- one sample test.
• Select diff as the test variables and test value to be 0.
• Click on option and put 95%.
• Under missing value select “exclude cases analysis by analysis”
• Continue

Interpretation of the results

Ho: there is no significant mean difference in physical strength before and after a specified training period.

Ha: there is a significant mean difference in physical strength before and after a specified training period.

Since the P-value (0.000)<0.05, the null hypothesis is rejected implying that there is a significant mean difference in physical strength before and after a specified training period.

THE PAIRED T-TEST

• The paired sample t-test produce compares the means of two variables for a single group.
• It computes the difference between values of the two variables for each case and tests whether the average differs from 0.
• It is usually used in the mate….. pairs or case- control study.

STEPS

• Analyze – compare means- sample t-test.
• Select a pair of variables, as follows.
• Click each of two variables.

The first variables appear.

In the current selection group as variable 1 and the second appears as variable 2.

• After you have selected a pair of variables.
• Click the arrow button to move the pair into the paired variables lists click ok.
• You may select move pairs of variables.
• To move a pair of variables from the analysis.
• Select a pair in the paired variable list and click the arrow button.
• Click options to control treatment of missing data and the level of the confidence interval.

Example (page 48)

Independent sample t-test

This is used for testing means of a variable which has two disadvantaged groups of cases e.g is there a significant difference in income between the male and female respondent?

Assumptions for the independent sample t-test

• The variables of the dependent variable in the two populations are equal.
• The dependent variable is normally distributed with in the population.
• The data are independent (scores of one participant are not dependent on scores of others).

The independent sample t-test procedure

• It compares means for groups of cases.
• The subjects should be randomly assigned to two groups so that any difference in the response is due to the treatment or lack of treatment but not to other factors.
• Always ensure that the difference in other factors are not making or enhancing a significance difference in mean.

Example

• The researcher is interested to see if in the population men and women have the same scores in a test.
• If there is a difference in the highest year of school completed between the males and females.

STEPS

• Analyze- compare means- independent- sample t-test.
• Select one or more quantitative test variables.
• Select a single grouping variable.
• Click defines groups to specify two codes for the groups you want to compare.
• Click options to control the treatment of missing data the level of the confidence.

Example

Page 50

Procedure

• Analyze- compare means- independent- sample t-test.
• Select highest year of school completed for test variable.
• Select sex for grouping variable.
• Click on define groups- use specified values, put 1 for group 1 and 2 for group 2. This is because 1 stands for male 2 stands for female.

The results show sets of test statistics

• Equal variance assumed.
• Equal variance not assumed.
• If the F-statistics is significant (null is rejected) we used of equal variance not assumed for interpreting the t-test.
• If the F-statistics is not significant (null is accept) we use the row oof equal variance assumed for interpreting the t-test.