SPSS (STATISTICAL PACKAGE FOR SOCIAL SCIENCES)
A
Practical Submitted to the Department of Sports Psychology
in partial fulfilment of the requirements for the Award of Degree of
MASTER OF ARTS IN SPORTS PSYCHOLOGY
by
Tanishqa Datta
राष्ट्रीय खेल विश्वविद्यालय इफ
ं ाल, मविपरु
(भारत सरकार, युिा काययक्रम एिं खेल मंत्रालय)
(कें द्रीय विश्वविद्यालय)
NATIONAL SPORTS UNIVERSITY IMPHAL, MANIPUR
(Government of India, Ministry of Youth Affairs and Sports)
(Central University)
INDEX
S. N0.
TOPIC
1
Introduction to SPSS
PAGE NO.
Introduction of SPSSSPSS stands for statistical package for the Social Sciences. SPSS was originally developed in
1968 by Norman H. Nie, Dale H. Bent, and C. Handlai Hull, who later incorporated as SPSS
Inc in 1975. SPSS is a Windows based program that can be used to perform data entry and
analysis and to create tables and graphs. SPSS is capable of handling large amounts of data
and can perform all of the analyses covered in the text and much more. SPSS is commonly
used in the Social Sciences and in the business world, so familiarity with this program should
serve you well in the future.
SPSS provides an extensive array of statistical methods, encompassing both descriptive and
inferential statistics, regression studies, and factor analysis. Additionally, it supports data
representation via visual aids like graphs and charts.
SPSS Features
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Can import data from many different programs, such as MS Excel and SAS.
Provides analysis tools to generate reports, charts, plots, descriptive statistics, and run
advanced statistical analysis.
Provides a command syntax that can simplify certain things, such as running
repetitive tasks.
Opening SPSS
Examine the screen to see if there is an icon with the letters “SPSS” written beneath it. If
there is such an icon, double click on the icon to open the SPSS software.
After clicking on the icon, the screen will appear like this.
Layout of SPSS
This will be the screen, which you will see after opening the application.
Menu bar
Tool bar
(Figure-1)
At buttom of the screen, two options appear which meansData view- in this view, you see the data which you are using.
Variable view- Variable View is where you can specify the format of your data when you are
creating a file or where you can check the format of a pre-existing file.
SPSS menu bar
Now when you will click on file, you will see various options.
➢ New- Opening a new SPSS file it includes
• New Data- it allows the users to create a new data file from scratch.
• Syntax- it is nothing more than a text file; hence, you can type commands and
comments into it, and you can cut-and-paste in it as you would in any text
editor.
• Output- this tab displays the data objects in an IBM SPSS Statistics output
file, such as tables and charts. Only items selected on the Table of Contents tab
are displayed on the Output tab.
• Script- Script includes Python3 and Basic which means,
Python3 is used for a general-purpose language Python, allowing users to
write scripts in the Python programming language.
➢ Open- Open an existing data file or syntax file. It includes the same options as new in
previous option.
➢ Import data- Data Under Import we have Database; Excel; CSV Data; Text Data;
SAS; Stata; Cognos Business Intelligence; Cognos TM1. Then when we click on
database, a different tab open.
• New Query: Start creating a new Structured Query Language query.
• Editor Query: Compose and edit Structured Query Language commands in a
dedicated workspace.
• Run Query: Execute the Structured Query Language query to retrieve and
process data within SPSS.
➢
➢
➢
➢
General open
Save- Saving a file
Save As- Saving a file by choosing different file type
Save all Data- Saving all existing data on the file
➢ Save restore points- it allows you to save the current state of your data file and
analysis session so that you can return to that point later if needed.
➢ Export- exporting current existing file to database.
➢ Mark File Read Only: Marking a file as Read Only.
➢ Rename Dataset- renaming a file
➢ Display Data File Information- displaying the information of existing data file (It
includes Working File and external file.
➢ Cache data- caching data
➢ Stop Processor- stopping SPSS processor
➢ Switch Server- switching between servers
➢ Repository- repository refers to a centralized location where various assets related to
SPSS projects are stored, managed, and shared.
• Connect- this option allows users to establish a connection to the
collaboration and deployment services repository from within SPSS.
• Add a file- This option enables users to upload files to the C&DS repository.
Users can add various types of files, including SPSS data files.
• Retrieve to SPSS statistics- this option allows users to retrieve SPSS files
stored in the repository. Users can search for specific files or browse through
folders to locate the desired SPSS files.
•
Download a file- This feature enables users to obtain copies of SPSS files,
output files, or other assets stored in the repository for offline use or
archival purposes.
➢ Welcome dialog- Welcome dialog is the initial window that appears when you launch
the software. It serves as a central hub for accessing various resources, tools, and
recent projects within SPSS.
➢ Print- printing current SPSS file
➢ Recently used data- viewing recently used data
➢ Recently Used file- viewing recently weed files.
➢ Manage license- it allows users to view and control the licensing information
associated with their SPSS software installation.
Now you can exit the file section.
Now open edit option by clicking on it.
(figure-2)
Various options will appear like➢
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Undo- undoing an entered data or value.
Redo set cell value- adding value again
Cut- cutting data
Copy - copying data
Copy with variable names – it allows you to copy data from one spss data file to
another while retaining the variable names.
Copy with variable labels- allows you to copy data from one spss data file to
another while retaining the variable labels.
Paste - pasting data
Paste variables names- pasting variables
Paste with variable labels- allows you to paste data from one spss data file to
another while retaining the variable labels.
Clear- clearing data
Insert variables- inserting existing variables
Insert cases- inserting existing cases
Search data files- it allows you to search any data file in spss.
Find - finding data or variable
Find Next
Replace
Go to case- finding a particular case
Go to Variable
Go to Imputation- Imputation involves replacing missing data with estimated
values based on other available data in the dataset.
Options: various options like output labels, charts, scripts, etc. can be found.
Now click on view option. It has various options-
(figure-3)
➢ Status bar- viewing the SPSS status, clicking this will hide on show the status bar on
the SPSS windows.
➢ Toolbars- Viewing the SPSS toolbars, clicking this will pop up a dialogue box where
we can set a new toolbar or reset or customize existing toolbar on SPSS window.
➢ Menu Editor
➢ Fonts: viewing the dialogue box of fonts where font style, Size, and font are found.
➢ Grid lines - viewing the grid lines, clicking this will hide or show the grid lines on the
data editor
➢ Value labels - viewing labels given to numeric values, clicking this will hide or show
the labels
➢ Mark Imputed Data- feature that flags or identifies the data points that have been
imputed (replaced with estimated values) due to missing data.
➢ Customize variable view: viewing already made custom attributes.
Layout of data
(figure-4)
Data has various options, as you can see in (fig 4)
➢ Define Variable Properties- Define properties for variables such as name, type, and
measurement level.
➢ Set Measurement Level for Unknown- The Set Measurement Level for Unknown
dialog allows you to define measurement level for any variables with an unknown
measurement level without performing a data pass (which may be time-consuming
for large data files).
➢ Copy Data Properties -Allows to copy properties from one variable to others.
➢ New Custom Attribute- Custom attributes can be used to store any information you
like for each variable.
➢ Define Dates and times- Date/time variables have a format representing a date and
time, such as dd-mmm-yyyy hh:mm:ss
➢ Define Multiple Response Sets- Multiple response sets are constructed from
multiple variables in the data file.
➢ Validation- Checking the accuracy and integrity of data from (Load Predefined
Rules, Defined Rules, Validate data)
➢ Identify Duplicate Cases- An entry that has occurred more than once in the data-set.
➢ Identity Unusual Cases- This feature is a tool that helps users identify cases in their
dataset that exhibit unusual characteristics or stand out from the rest of the data.
➢ Compare Datasets- Uses to compares the active dataset to another dataset.
➢ Sort Cases- sorts the cases in the data file by one or more variables
➢ Sort variables- Sorting variables will rearrange the rows based on a given variable
(or variables)
➢ Transpose- Transpose creates a new data file in which the rows and columns in the
original data file are transposed so that cases (rows) become variables and variables
(columns) become cases.
➢ Adjust string widths across files- Ensure consistency in the width of the string
variables across multiple files or datasets.
➢ Merge Files- It includes add cases and merge files.
➢ Restructure- Restructures data from wide format into a long format or vice versa.
➢ Propensity Score Matching- It is used to reduce bias in observational studies or
non-randomized experiments.
➢ Rake weights- It allows you to use multiple categorical variables as input and set
different targets for each of the categories.
➢ Case control matching- A Tool to Reduce Selection Bias in Common IR
[international Relation] Studies.
➢ Aggregate- New data are computed from an existing data set, and cases in the new
data set refer to a group of cases.
➢ Split into files- Allows you to split your dataset into subgroups.
➢ Select Cases- One can carry out an analysis on a particular subset of data.
➢ Weight cases- It allows us to assign "importance" or "weight" to the cases
in your dataset.
Layout of transform
(figure -5)
Transform include options like➢ Compute variable- Compute function allows users to create new variables or modify
existing ones by performing mathematical or logical operations.
➢ Programmability transformation- this feature allows users to apply custom data
transformations using scripts written in the Python programming language.
➢ Count values within cases- refers to a feature that calculates the frequency of
specific values within individual cases (rows) of the dataset.
➢ Shift values: Creates new variables that contain the values of existing variables from
previous or subsequent cases.
➢ Recode into Same Variables- Allows to modify or change the values of existing
variables without creating new variables.
➢ Recode into Different Variables- Create new variables based on the values of
existing variables.
➢ Automatic Recode- Allows to quickly and automatically recode a categorical
variable into a new numeric variable.
➢ Create dummy variables- A numerical value used to represent categorical data like
gender, race, etc.
➢ Visual Binning- Grouping continuous data into categories or bins based on their
values.
➢ Optimal Binning- Discretizes one or more scale variables by distributing the values
of each variable into bins.
➢ Prepare data for modeling: Allows you to identify unusual cases and invalid cases,
variables, and data values in your active dataset, and prepare data for modeling.
➢ Rank Cases- It can be used to compute the rank transform of a variable.
➢ Date and time Wizard- It simplifies a number of common tasks associated with date
and time variables.
➢ Create Time Series- Creates new time series variables as functions of existing time
series variables.
➢ Replace Missing Values- Allows to create new time series variables from existing
ones
➢ Random Number Generators- Allows to select the random number generator and
set the starting sequence value so you can reproduce a sequence of random numbers.
➢ Run Pending Transforms- If you have pending transformations, you will get a
warning the first time that you try to edit data in the Data Editor.
Layout of analyze
(figure-6)
In analyze, there are options like➢ Power analysis- power analysis helps researchers determine the sample size needed
to detect a significant effect in their study with a specified level of statistical power. It
has four options1) Means- refers to the effect size parameter. It can be checked by• one-sample t-test is a statistical test used to determine whether the
mean of a single sample differs significantly from a known or
hypothesized population mean.
• paired-sample t-test is a statistical test used to compare the means of
two related groups or conditions.
• independent t-test is a statistical test used to compare the means of
two independent groups
• one-way ANOVA (Analysis of Variance) is a statistical test used to
compare the means of three or more independent groups.
2) Proportions- Proportion refers to the relative frequencies or percentages of
different categories within a variable.
It has various tests like•
•
•
One Sample Binomial Test.
Related Sample Binomial Test.
Independent Sample Binomial Test.
3) Correlations- It is the Statistical relationship between two variables and can
be calculated by
• Pearson Product-moment.
• Spearman Rank Order.
• Partial
4) Regression- It refers to a Statistical analysis technique used to model and
examine the relationship between a dependent variable and one or more
independent variables. It can be calculated by
• Univariate linear
➢ Meta analysis- It is used to combine and analyze data from multiple studies on a
particular topic. It has options like1) Continuous outcomes- It refers to Variables that can take on a wide range
of numerical values on a continuous scale without distinct categories or
intervals.
• Raw data
•
Pre- calculated effect size
2) Binary outcomes- It refers to variables that can take on only two possible
values, typically represented as 0 and 1.
• Raw Data.
• Pre-Calculated Effect Size.
3) Meta regression- Method of combining multiple studies on a particular
topic.
➢ Reports- Output generated by the software after performing Statistical analyses on
your data. Like• Codebook
• OLAP cubes
• Case summaries
• Report summaries in rows
• Report summaries in columns
➢ Descriptives statistics- The Descriptives procedure is used to find the measures of
central tendency (mean, median, mode) and measures of dispersion (range, standard
deviation, variance, minimum and maximum) and measures of kurtosis and skewness.
This procedure is best suited to describe continuous variables. It has• Frequencies-To find the number of times an event Occurs.
• Descriptives-To find measures of central tendency and measures of
variability.
• Explore-To be used to deeply investigate a single numeric variable.
• Crosstabs - To describe the relationship between two Categorical variables.
• TURF Analysis- It is a statistical research methodology that enables the
assessment of potential of market research for a combination of products and
services.
• Ratio- provides a comprehensive list of summary statistics for describing the
ratio between two scale variables.
• Proportion Confidence Intervals- It refers to the range of values within
which we can estimate the true proportion of a certain characteristics or
outcome in a population.
• P-P Plots- it compares the cumulative distribution function (CDF) of the
observed data to the CDF of a theoretical distribution (e.g.,
normal distribution).
• Q - Q Plot- it compares the quantiles of the observed data to the quantiles of a
theoretical distribution.
➢ Bayesian statistics- It is a branch of Statistics that focuses on using Bayes’ theorem
to update and revise probabilities based on new evidence.
• One Sample Normal
• One Sample Binomial.
• One Sample Poisson.
• Related Samples Normal.
• Independent Samples Normal.
• Pearson Correlation.
• Linear Regression.
• One-way ANOVA.
•
•
Loglinear Models.
One-Way Repeated Measures ANOVA.
➢ Tables- it allows users to generate various types of tables to summarize and display
descriptive statistics or results of statistical analyses.
• Custom tables- to organize data into rows and columns structured format
• Multiple response sets- setting multiple variables to record responses to
questions where the respondent can give more than one answer
• Convert multiple category set
• Define category order
➢ Compare means and proportions- It is to use different Statistical Tests depending
on the nature of your data and research question by:
• Means- to find the average of a given set of data
• One Sample T Test- to examine whether the mean of population is
statistically different from a Known or hypothesized value
• Independent Sample T Test- to compare the means of two independent
groups in order to determine whether there is statistical evidence that the
associated population means are significantly different.
• Summary Independent Sample T Test - to determine whether the mean
difference between two sets of observation is zero.
• Paired Sample T Test
• One way ANOVA.
•
•
•
One Sample Proportions.
Independent Sample Proportions.
Paired Sample Proportions.
➢ General linear model- Allows to analyze the relationship between one or more
independent variables and a continuous dependent variable.
• Univariate
• Multivariate
• Repeated measures
• Variance components
➢ Generalized Linear Models - Under this, we find Generalized Linear Models,
Generalized estimating equations.
➢ Mixed models- refers to a statistical analysis method used to analyze data with both
fixed and random effects, often in longitudinal or repeated measures designs.
• Linear
• Generalized linear
➢ Correlate- it used to calculate correlation coefficients between pairs of variables in a
dataset.
• Bivariate with confidence intervals- refers to a correlation analysis that
calculates correlation coefficients between pairs of variables along with
confidence intervals for these correlation coefficients.
• Bivariate- used to determine the existence of relationships between two
different variables.
• Partial - to measure the degree of association between two random variables,
with the effect of a set of controlling random variables removed.
• Distances - to measure dependence between two paired random vectors of
arbitrary, net necessarily equal, dimension
• Canonical correlation- used to explore the relationships between sets of
variables.
➢ Regression- refers to a statistical method used to model and analyze the relationship
between one dependent variable and one or more independent variables.
• Automatic linear modeling- it is a feature that automatically builds linear
regression models to predict a target variable based on a set of predictor
variables.
• Linear - to model the relationship between a scalar response and one or more
explanatory variables (also known as dependent and independent variable).
• Curve estimation- to produce curve estimation regression statistics and
related plots for 11 different
• Partial least squares- it is a multivariate statistical technique used for
modeling the relationships between sets of independent and dependent
variables.
• Binary logistic- is a statistical method used to model the relationship between
a binary outcome variable.
• Multinomial logistic- statistical method used to model the relationship
between a categorical outcome variable with more than two categories and one
or more predictor variables.
• Ordinal
• Probit
• Nonlinear
• Weight estimation
•
•
•
2 stage least squares
Quantile
Optimal scaling
➢ Loglinear- is a statistical method used to analyze the relationships between
categorical variables by examining the frequencies of co-occurrences across multiple
categories. It includes
• General
• Logit
• Model selection
➢ Classify- it is used to assign cases (individual observations) to predefined categories
or groups based on their characteristics or attributes. It includes
• Twostep cluster
• K- means cluster
• Hierarchical cluster
• Cluster silhouettes
• Naïve bayes
• Tree
• Discriminant
• Nearest neighbour
➢ Dimension reduction- refers to a set of techniques used to reduce the number of
variables in a dataset while preserving the most important information. It includes• Factor
• Correspondence analysis
• Optimal scaling
➢ Scale- is used to assess the reliability and validity of measurement scales or constructs
in a dataset.
• Reliability analysis
• Weighted kappa
• Multidimensional unfolding (PREFSCAL)
• Multidimensional scaling (PROXSCAL)
• Multidimensional scaling (ALSCAL)
➢ Nonparametric tests- statistical methods used to analyze data when the assumptions
of parametric tests are not met or when the data are not normally distributed. It
includes• One sample
• Independent sample
• Related sample
• Quade nonparametric ANCOVA
• Legacy dialogs- it has chi- square, binomial, runs, 1- sample K-S, 2
independent sample, K independent sample, 2 related sample, K related
sample.
➢ Forecasting- Provides advanced capabilities that enable both novice and experienced
users to develop reliable forecasts using time-series data. It includes• Create Temporal Causal Models.
• Create Traditional Models.
• Apply Temporal Causal Models.
• Apply Traditional Models.
• Seasonal Decomposition.
• Spectral Analysis.
• Sequence Charts.
• Autocorrelations
• Cross-Correlations
➢ Survival- To analyze the time it takes for an event of interest to occur.
• Life Tables.
• Kaplan Meier.
• Cox-Regression.
• Cox w/Time-Dep Cov...
• Parametric Accelerated Failure Time (AMT) Models.
➢ Multiple response- are Constructed from multiple variables in the data file. A
multiple response set is a special construct within a data file.
• Define Variable Sets.
• Frequencies.
• Crosstabs
➢ Missing value analysis- it involves identifying patterns of missing data within a
dataset and determining how to handle them appropriately.
➢ Multiple imputation- Performs multiple imputation of missing data values. It
includes- analyze patterns, impute missing data values.
➢ Simulation- Simulating input data to predictive models using the Monte Carlo
method and evaluating the model based on the simulated data.
➢ Quality control- involves ensuring the accuracy, reliability, and consistency of
data and analyses. It includes- control charts and pareto charts.
➢ Spatial and temporal modeling- It can be used to predict target values at any
location within the shape data that is used in the analysis.
Layout of graph
(figure-7)
It includes, as we can see (figure-7)
➢ Chart builder- graphical tool that allows users to create various types of charts and
graphs to visually explore and present their data.
➢ Graph board template chooser- is a feature that allows users to select pre-designed
templates for creating graphs and charts quickly.
➢ Relationship map- it is a visual tool used to display connections and associations
between variables in a dataset.
➢ Weibull plot- is a graphical representation of data that follows a Weibull distribution,
a commonly used probability distribution for modeling lifetime data and survival
analysis.
➢ Compare subgroups- refers to a statistical analysis method used to compare the
means or distributions of a continuous variable across different subgroups within a
dataset.
➢ Regression variable plots- provide visualizations that help assess the relationship
between predictor variables and the dependent variable in regression analysis.
➢ Bar- allows users to create bar charts to visually represent the distribution or
comparison of categorical data.
➢ 3-D bar- an option for creating three-dimensional bar charts.
➢ Line- an option for creating line charts.
➢ Area- creates area charts, which visually represent the trend or distribution of data
over time or categories by filling in the area below the lines connecting data points.
➢ Pie- generates pie charts, which display the proportions of different categories within
a dataset as segments of a circular pie.
➢ High- low- option is used to create high-low-close charts, commonly used to visualize
stock market data or time series data.
➢ Boxplots- option generates box-and-whisker plots, which provide a visual summary
of the distribution of a continuous variable.
➢ Error bar- option is used to add error bars to a graph. Error bars represent the
variability or uncertainty associated with each data point or group in a graph.
➢ Population pyramid- option creates a graphical representation of the age and gender
distribution within a population.
➢ Scatter/ dot- option allows users to create scatterplots or dot plots, which display the
relationship between two continuous variables.
➢ Histogram- option generates a graphical representation of the distribution of a single
continuous variable.
Layout of utilities
(Figure-8)
Utilities has options like➢ Variables- allows users to perform various tasks related to managing variables in a
dataset.
➢ OMS control panel- (Output Management System) is a tool used to customize and
manage the output produced during analyses.
➢ OMS identifiers- (Output Management System Identifiers) are unique names
assigned to specific output tables or objects generated during an analysis.
➢ Scoring wizard- is a tool used to apply predictive models or algorithms to new data.
➢ Merge model XML- is a feature used to combine or merge predictive models saved
in XML format.
➢ Calculate with pivot table- is a feature that allows users to perform calculations and
create new variables based on pivot table summaries.
➢ Data file comments- is a feature that allows users to add annotations or notes to a
dataset to provide context, explanations, or reminders about the data.
➢ Define variable macro- is a user-defined macro that simplifies the process of
defining variables by storing a set of variable definitions or properties.
➢ Merge viewer tables- is a feature that allows users to combine or merge multiple
tables from the Output Viewer into a single table for further analysis or reporting.
➢ Define variable sets- is a feature that allows users to organize variables into logical
groups or sets for easier management and analysis.
➢ Censor table- is typically associated with survival analysis, specifically with the
Kaplan-Meier estimator.
➢ Format correlation matrix- is a feature that allows users to customize the
appearance and layout of the correlation matrix table.
➢ Use variable sets- are used to organize related variables into logical groups for easier
management and analysis.
➢ Create text output- refers to the process of generating text-based reports or
summaries of analysis results.
➢ Process data files- typically refers to the steps involved in loading, manipulating, and
analyzing datasets.
➢ Run script- allows users to execute custom scripts written in languages such as
Python, R, or SPSS Syntax.
➢ Modify table appearance- allows users to customize the appearance and formatting
of tables generated in the output.
➢ Modify output titles- option enables users to customize the titles of tables, charts, or
sections within the output.
➢ Production facility- is a feature that enables users to automate and streamline
repetitive data analysis tasks, such as generating reports or running analyses on large
datasets.
➢ Map conversion utility- option provides tools for converting geographic data files
between different formats.
Layout of extensions
(Figure-9)
Extension includes, as we can see in (figure-9)
➢ Extension hub- option is a platform that provides access to additional features,
functionalities, and resources developed by SPSS users and third-party
developers.
➢ Install local extension bundle- option in SPSS enables users to add custom
extensions, scripts, or add-on modules to their SPSS environment directly from a
local file bundle.
➢ Install python and R modules- option in SPSS allows users to add additional
functionality and capabilities to SPSS by integrating Python and R programming
languages.
➢ Custom dialog builder for extensions- option enables developers to create userfriendly interfaces, or dialog boxes, for their custom SPSS extensions.
➢ Benfords law analysis- allows users to analyze datasets to determine whether the
distribution of first digits conforms to Benford's Law.
➢ Utilities- provides additional tools and functionalities to enhance the SPSS
environment.
• Create extension bundle- enables users to package together custom scripts,
extensions, or add-on modules into a single bundle for distribution or sharing.
• Edit extension bundle- allows users to modify or update existing extension
bundles that have been previously created.
• Custom dialog builder (compatibility mode)- allows users to create custom
dialog boxes for their extensions using the older, compatibility mode interface.
•
Install custom dialog (compatibility mode)- this option facilitates the
integration of custom dialog boxes created using the older, compatibility mode
interface into SPSS.
➢ Descriptive statistics example- this option provides users with sample datasets and
predefined analyses to demonstrate how to generate descriptive statistics for variables
within the dataset.
Layout of window
(figure-10)
This option includes,
➢ Split- it divides the current active window into multiple panes or sections, allowing
users to view different parts of the document simultaneously.
➢ Minimize all windows- it allows users to quickly minimize all open windows within
the SPSS environment.
➢ Go to designated viewer window- option provides a quick way to navigate to a
specific viewer window within the SPSS environment.
➢ Go to designated syntax window- option enables users to quickly navigate to a
specific syntax window within the SPSS environment.
➢ Reset dialog sizes and positions- allows users to restore the default sizes and
positions of dialog boxes within the SPSS interface.
➢ 1 untitled1 [dataset0]- IBM SPSS statistics data editor- refers to a default dataset
view within the SPSS Data Editor.
Layout of help
Help includes, options like➢ Search applications- provides a quick way for users to search for specific
applications, tools, or functionalities within the SPSS software.
➢ Topics- provides users with access to a comprehensive list of help topics and
documentation covering various aspects of the software.
➢ SPSS support- provides users with access to resources, assistance, and technical
support options to address their questions, issues, or concerns related to SPSS
software.
➢ SPSS forums- provides users with access to online discussion forums where they can
engage with other SPSS users, ask questions, share knowledge, and seek assistance
related to SPSS software and data analysis.
➢ IBM SPSS statistics community- this option provides users with access to an online
community platform specifically dedicated to SPSS Statistics users.
➢ Documentations in PDF formats- this option provides users with access to
comprehensive documentation resources for the software in PDF format.
➢ Command syntax reference- this option provides users with a comprehensive guide
to the SPSS command syntax language.
➢ About- provides users with information about the specific version and build of SPSS
Statistics software they are using.
➢ Diagnostic tool- this provides users with a utility to troubleshoot and diagnose issues
with the SPSS Statistics software.
➢ Give feedback- this provides users with a channel to share their thoughts,
suggestions, or concerns directly with IBM regarding the SPSS Statistics software.
➢ Report an issue- allows users to notify IBM about any problems, bugs, or technical
issues they encounter while using the software.
Specifications of tool bar
This is the Open File icon, to open files.
this is the Save button for saving data files you are currently working on.
You can use this to print.
review previously used dialogs in SPSS.
Use this icon to open a dialog to go to a particular case in your data file.
to get to a particular variable in your set of data.
to see a list of your variables and their characteristics, click on this button.
quick descriptive statistics.
this icon is for Find and Replace,
split your data file into smaller files. click here.
select specific cases for manipulation of analysis.
This button will switch the data view between the actual values and the value labels.