This document provides an overview and summary of the key features of the statistical software package SPSS (Statistical Package for the Social Sciences). It describes how SPSS can be used to perform statistical analysis and data management. The summary includes descriptions of the data editor, viewer, pivot tables, database access, transformations, and other core features. Procedures in SPSS can be accomplished through dialog boxes and menus using point and click functionality.
SPSS is widely used program for statistical analysis in social sciences, particularly in education and research. However, because of its potential, it is also widely used by market researchers, health-care researchers, survey organizations, governments and, most notably, data miners and big data professionals.
SPSS is widely used program for statistical analysis in social sciences, particularly in education and research. However, because of its potential, it is also widely used by market researchers, health-care researchers, survey organizations, governments and, most notably, data miners and big data professionals.
SPSS stands for Statistical package of sports sciences, it is a software package used for statistical analysis of data in field of education, physical education, medical, market etc. researches.
Aside from statistical analysis the software also feature data management which allow the user to create the variable, case selection, create a data drive and save it for further analysis when needed.
SPSS is beneficial for both qualitative and quantitative data equal importance has been given to both data set, SPSS provide graphical representation and also an appropriate result for data entered.
SPSS allow you to analysis the data using different kind of tests like t-test, z-test, further you can use ANOVA, MANOVA etc. for further analysis of result.
How to use SPSS (Statistical Package for Social Science) data. This software program is extensively used for Social Science data analysis. However it is also used by managers, scholars and Engineers also. In this document how to use SPSS for data analysis is explained step by step.
Microsoft Excel is a spreadsheet program used to record and analyse numerical and statistical data. Microsoft Excel provides multiple features to perform various operations like calculations, pivot tables, graph tools, macro programming, etc.
An Excel spreadsheet can be understood as a collection of columns and rows that form a table. Alphabetical letters are usually assigned to columns, and numbers are usually assigned to rows. The point where a column and a row meet is called a cell.
SPSS (Statistical Package for the Social Sciences) is a versatile and responsive program designed to undertake a range of statistical procedures. SPSS software is widely used in a range of disciplines and is available from all computer pools within the University of South Australia.
DOE is an essential tool to ensure products and processes satisfy Quality by Design requirements imposed by regulatory agencies. Using a QbD approach to develop your testing process can help you reduce waste, meet compliance criteria and get to market faster.
DOE helps you create a reliable QbD process for assessing formula robustness, determining critical quality attributes and predicting shelf life by using a few months of historical data.
Minitab is a statistics package developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner in conjunction with Triola Statistics Company in 1972.
It began as a light version of OMNITAB 80, a statistical analysis program by NIST, which was conceived by Joseph Hilsenrath in years 1962-1964 as OMNITAB program for IBM 7090. The documentation for OMNITAB 80 was last published 1986, and there has been no significant development since then.
R is a language and environment for statistical computing and graphics."
"R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible."
"One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.“
SPSS stands for Statistical package of sports sciences, it is a software package used for statistical analysis of data in field of education, physical education, medical, market etc. researches.
Aside from statistical analysis the software also feature data management which allow the user to create the variable, case selection, create a data drive and save it for further analysis when needed.
SPSS is beneficial for both qualitative and quantitative data equal importance has been given to both data set, SPSS provide graphical representation and also an appropriate result for data entered.
SPSS allow you to analysis the data using different kind of tests like t-test, z-test, further you can use ANOVA, MANOVA etc. for further analysis of result.
How to use SPSS (Statistical Package for Social Science) data. This software program is extensively used for Social Science data analysis. However it is also used by managers, scholars and Engineers also. In this document how to use SPSS for data analysis is explained step by step.
Microsoft Excel is a spreadsheet program used to record and analyse numerical and statistical data. Microsoft Excel provides multiple features to perform various operations like calculations, pivot tables, graph tools, macro programming, etc.
An Excel spreadsheet can be understood as a collection of columns and rows that form a table. Alphabetical letters are usually assigned to columns, and numbers are usually assigned to rows. The point where a column and a row meet is called a cell.
SPSS (Statistical Package for the Social Sciences) is a versatile and responsive program designed to undertake a range of statistical procedures. SPSS software is widely used in a range of disciplines and is available from all computer pools within the University of South Australia.
DOE is an essential tool to ensure products and processes satisfy Quality by Design requirements imposed by regulatory agencies. Using a QbD approach to develop your testing process can help you reduce waste, meet compliance criteria and get to market faster.
DOE helps you create a reliable QbD process for assessing formula robustness, determining critical quality attributes and predicting shelf life by using a few months of historical data.
Minitab is a statistics package developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner in conjunction with Triola Statistics Company in 1972.
It began as a light version of OMNITAB 80, a statistical analysis program by NIST, which was conceived by Joseph Hilsenrath in years 1962-1964 as OMNITAB program for IBM 7090. The documentation for OMNITAB 80 was last published 1986, and there has been no significant development since then.
R is a language and environment for statistical computing and graphics."
"R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible."
"One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.“
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1. Data Analysis Using SPSS
(SSR 605a)
Leyte Normal University
MATHEMATICS DEPARTMENT
Tacloban City
Christian G. Abalos
Teacher
2. SPSS (Statistical Package for Social
Sciences) for Windows provides a powerful
statistical-analysis and data-management system in
a graphical environment, using descriptive menus
and simple dialog boxes to do most of the work for
you. Most tasks can be accomplished simply by
pointing and clicking the mouse.
OVERVIEW
3. FEATURES
Data Editor is a versatile spreadsheet-like system for defining,
entering, editing, and displaying data.
Viewer makes it easy to browse your results, selectively show
and hide output, change the display order results, and
move presentation-quality tables and charts between
SPSS and other applications.
Multidimensional pivot tables makes your results come alive
with multidimensional pivot tables. Explore your tables
by rearranging rows, columns, and layers. Uncover
important findings that can get lost in standard reports.
Compare groups easily by splitting your table so that only
one group is displayed at a time.
4. FEATURES
Database access can retrieve information from databases by using
the Database Wizard instead of complicated SQL queries.
Data transformations features help get your data ready for
analysis. You can easily subset data; combine categories;
add, aggregate, merge, split, and
transpose files; and more.
Electronic distribution sends e-mail reports to other people with
the click of a button, or export tables and charts in HTML
format for Internet and intranet distribution.
5. FEATURES
Online Help has detailed tutorials provide a comprehensive
overview; context-sensitive Help topics in dialog boxes
guide you through specific tasks; pop-up definitions in pivot
table results explain statistical terms; the Statistics Coach
helps you find the procedures that you need; Case Studies
provide hands-on examples of how to use statistical
procedures and interpret the results.
Command language is a powerful command language that allows
you to save and automate many common tasks. Complete
command syntax documentation is integrated into the
overall Help system and is available as a separate PDF
document, SPSS Command Syntax Reference, which is
also available from the Help menu.
6. SPSS WINDOWS
Data Editor displays the contents of the data file. You can
create new data files or modify existing data files with the Data
Editor. If you have more than one data file open, there is a
separate Data Editor window for each data file.
Viewer displays all statistical results, tables, and charts
are displayed in the Viewer. You can edit the output and save it for
later use. A Viewer window opens automatically the first time you
run a procedure that generates output.
Draft Viewer displays output as simple text (instead of
interactive pivot tables).
7. Pivot Table Editor displays in pivot tables that can be modified in
many ways with the Pivot Table Editor. You can edit text, swap
data in rows and columns, add color, create multidimensional
tables, and selectively hide and show results.
Chart Editor displays charts that can be modified in high-resolution
charts and plots in chart windows. One can change the colors,
select different type fonts or sizes, switch the horizontal and
vertical axes, rotate 3-D scatterplots, and even change the
chart type.
Text Output Editor displays text output that is not displayed in pivot
tables can be modified with the Text Output Editor. You can
edit the output and change font characteristics (type, style,
color, size).
SPSS WINDOWS
8. SPSS WINDOWS
Syntax Editor
One can paste your dialog box choices into a syntax
window, where your selections appear in the form of command
syntax. One can then edit the command syntax to use special
features of SPSS that are not available through dialog boxes. One
can save these commands in a file for use in subsequent SPSS
sessions.
Script Editor
Scripting and OLE automation allow you to customize and
automate many tasks in SPSS. Use the Script Editor to create
and modify basic scripts.
9. MENUS
Many of the tasks that you want to perform with SPSS are
available through menu selections. Each window in SPSS has its
own menu bar with menu selections that are appropriate for that
window type.
The Analyze and Graphs menus are available in all
windows, making it easy to generate new output without having to
switch windows.
10. STATUS BAR
Command status
For each procedure or command that you run, a case counter
indicates the number of cases processed so far. For statistical
procedures that require iterative processing, the number of iterations
is displayed.
Filter status
If you have selected a random sample or a subset of cases
for analysis, the message Filter on indicates that some type of case
filtering is currently in effect and not all cases in the data file are
included in the analysis.
Weight status
The message Weight on indicates that a weight variable is
being used to weight cases for analysis.
Split File status.
The message Split File on indicates that the data file has
been split into separate groups for analysis, based on the values of
one or more grouping variables.
11. DIALOG BOXES
Dialog boxes for statistical procedures and charts typically
have two basic components:
Source variable list.
A list of variables in the active dataset. Only variable types
that are allowed by the selected procedure are displayed in the
source list. Use of short string and long string variables is restricted
in many procedures.
Target variable list(s).
One or more lists indicating the variables that you have
chosen for the analysis, such as dependent and independent
variable lists.
12. One can display either variable names or variable labels in
dialog box lists.
To control the display of variable names or labels, choose Options
from the Edit menu in any window.
To define or modify variable labels, use Variable View in the Data
Editor.
For data that are imported from database sources, field names
are used as variable labels.
For long labels, position the mouse pointer over the label in the
list to view the entire label.
If no variable label is defined, the variable name is displayed
VARIABLE NAMES AND LABELS
14. Analyzing data with SPSS is easy. All you have to do is:
1. Get your data into SPSS. You can open a previously saved
SPSS data file, you can read a spreadsheet, database, or text
data file, or you can enter your data directly in the Data Editor.
2. Select a procedure. Select a procedure from the menus to
calculate statistics or to create a chart.
3. Select the variables for the analysis. The variables in the data
file are displayed in a dialog box for the procedure.
4. Run the procedure and look at the results. Results are
displayed in the Viewer.
BASIC STEPS IN DATA ANALYSIS
15.
16.
17. DATA EDITOR
The Data Editor provides two views of your data:
1. Data View. This view displays the actual data values or defined
value labels.
2. Variable View. This view displays variable definition information,
including defined variable and value labels, data type (for
example, string, date, or numeric), measurement level (nominal,
ordinal, or scale), and user-defined missing values.
In both views, you can add, change, and delete information
that is contained in the data file.
18. Many of the features of Data View are similar to the features that are
found in spreadsheet applications. There are, however, several important
distinctions:
1. Rows are cases. Each row represents a case or an observation. For
example, each individual respondent to a questionnaire is a case.
2. Columns are variables. Each column represents a variable or characteristic
that is being measured. For example, each item on a questionnaire is a
variable.
3. Cells contain values. Each cell contains a single value of a variable for a
case. The cell is where the case and the variable intersect. Cells contain
only data values. Unlike spreadsheet programs, cells in the Data Editor
cannot contain formulas.
4. The data file is rectangular. The dimensions of the data file are determined
by the number of cases and variables. You can enter data in any cell. If you
enter data in a cell outside the boundaries of the defined data file, the data
rectangle is extended to include any rows and/or columns between that cell
and the file boundaries. There are no “empty” cells within the boundaries of
the data file. For numeric variables, blank cells are converted to the system-
missing value. For string variables, a blank is considered a valid value.
DATA VIEW
19. VARIABLE VIEW
Variable View contains descriptions of the attributes of each
variable in the data file.
In Variable View:
Rows are variables.
Columns are variable attributes.
You can add or delete variables and modify attributes of
variables, including the following attributes:
Variable name
Data type
Number of digits or characters
Number of decimal places
Descriptive variable and value labels
User-defined missing values
Column width
Measurement level
All of these attributes are saved when you save the data file.
20. In addition to defining variable properties in Variable View, there
are two other methods for defining variable properties:
1. The Copy Data Properties Wizard provides the ability to use an external
SPSS data file or another dataset that is available in the current session
as a template for defining file and variable properties in the active
dataset. You can also use variables in the active dataset as templates
for other variables in the active dataset. Copy Data Properties is
available on the Data menu in the Data Editor window.
2. Define Variable Properties (also available on the Data menu in the Data
Editor window) scans your data and lists all unique data values for any
selected variables, identifies unlabeled values, and provides an auto-
label feature. This method is particularly useful for categorical variables
that use numeric codes to represent categories—for example, 0 = Male,
1 = Female.