Data Analysis using SPSS, STATA, EVIEWS

Unlock the Power of Data with Research Consult Uganda

In today’s fast-paced world, data is more than just numbers – it’s a strategic asset that drives informed decisions, fuels growth, and uncovers opportunities. At Research Consult Uganda, we specialize in turning raw data into actionable insights using industry-leading statistical tools such as SPSS, Stata, and EViews.

Why Choose Research Consult Uganda?

Our team of experienced analysts understands that every dataset tells a story. Whether you are a business, academic institution, government agency, or non-profit organization, we provide tailored data analysis solutions to meet your unique needs.

Comprehensive Data Analysis Services

• SPSS Analysis: We offer descriptive statistics, regression analysis, hypothesis testing, and advanced analytics to help you understand trends and relationships in your data.

• Stata Analysis: Ideal for econometrics, panel data, and survey data analysis, our Stata expertise ensures accurate and robust results for complex datasets.

• EViews Analysis: For time series, forecasting, and econometric modeling, we leverage EViews to provide predictive insights that support strategic planning.

Our Approach

1. Understanding Your Goals: We start by understanding your research objectives to ensure the analysis addresses your key questions.

2. Data Cleaning & Preparation: Accurate analysis begins with clean, well-structured data. Our team ensures your dataset is ready for meaningful interpretation.

3. Advanced Statistical Analysis: Using SPSS, Stata, or EViews, we conduct rigorous analysis to reveal patterns, trends, and relationships in your data.

4. Interpretation & Reporting: We present findings in clear, actionable reports, visualizations, and presentations that make complex data easy to understand.

Who We Serve

Our services cater to a wide range of clients including:

• Academic Researchers & Students – Enhance thesis, dissertations, and research projects with expert statistical analysis.

• Businesses & Corporates – Gain insights into customer behavior, market trends, and operational efficiency.

• Government & NGOs – Support policy-making, program evaluation, and social research with reliable data interpretation.

Why Data Analysis Matters

In an era driven by evidence, data analysis is the key to smarter decision-making. At Research Consult Uganda, we empower you to:

• Identify opportunities and risks

• Measure performance and impact

• Validate assumptions and hypotheses

• Make data-driven strategic decisions

Get Started Today

Don’t let your data sit idle. Unlock its full potential with Research Consult Uganda. Contact us today for professional, reliable, and accurate data analysis services using SPSS, Stata, and EViews.

Introduction:

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.

Data Analysis using SPSS, STATA, EVIEWS

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.

Data Analysis using SPSS, STATA, EVIEWS

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

Data Analysis using SPSS, STATA, EVIEWS

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 specif