This document provides an overview of the Statistical Package for the Social Sciences (SPSS). It discusses how SPSS has made statistical analysis easier by reducing the need for complex calculations. It also describes some key features of SPSS, including its variable and data views, labeling variables, entering data, and performing statistical analyses. SPSS is one of the most commonly used statistical packages in research.
This document provides an introduction to the Statistical Package for the Social Sciences (SPSS). It describes some key features of SPSS, including how it allows users to define variables, enter data, and perform a variety of statistical analyses. SPSS has tools for describing and summarizing data through frequencies, descriptive statistics, cross-tabulations, and charts. It also contains functions for more advanced statistical tests. While powerful, SPSS requires time to learn due to its complexity. An understanding of statistics is still needed to properly interpret results.
SPSS (Statistical Package for the Social Sciences) is a statistical software package used for data management and analysis. It was developed in 1968 at Stanford University and acquired by IBM in 2009. SPSS allows users to easily obtain statistical results without programming by providing pre-programmed procedures for statistical analyses such as descriptive statistics, bivariate and multivariate analyses, predictive analytics, and graphics. Common uses of SPSS include market research, survey development and analysis, scientific research, and academic research.
SPSS (Statistical Package for the Social Sciences) is a statistical analysis software package that allows users to extract, manage, and analyze data. It provides features like generating reports, charts, descriptive statistics, and complex statistical analyses. While SPSS is easy to use and good for beginners, it has some limitations for advanced users in terms of customizing outputs and performing certain data manipulations. The document then describes the main SPSS interface windows including the data editor, output navigator, and syntax editor. It also covers how to open datasets, define variables, transform data, and create frequency tables and other outputs in SPSS.
This document summarizes OR/MS (operations research and management science) software available for microcomputers. It lists software packages for several common OR/MS techniques like linear programming, forecasting, and project management. It evaluates packages based on their functionality, size limitations, input and output features. The summary focuses on software for IBM PC and compatible machines running MS-DOS, as they have the greatest variety of software available. It aims to help readers select appropriate OR/MS software for their needs and applications.
This document discusses statistical packages used for data analysis. It describes the Statistical Package for the Social Sciences (SPSS) in detail. SPSS is a widely used statistical software package that can perform complex data analysis, including regression, ANOVA, and multivariate analysis. It has advantages such as easy data import, reliable results, and useful graphs. However, it also has limitations such as difficulty interpreting error logs and incomplete menus. The document also briefly mentions other packages like Microsoft Excel, SAS, Minitab, and Stata.
This document provides an introduction to the statistical software packages SPSS and STATA. It discusses why researchers may choose to use each package and highlights some of their key differences and strengths. SPSS is generally best for descriptive statistics and basic analyses, while STATA excels at more advanced econometric techniques and can handle larger datasets. The document also gives overviews of the basic structure and interfaces of each program.
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.
This document provides an introduction to the Statistical Package for the Social Sciences (SPSS). It describes some key features of SPSS, including how it allows users to define variables, enter data, and perform a variety of statistical analyses. SPSS has tools for describing and summarizing data through frequencies, descriptive statistics, cross-tabulations, and charts. It also contains functions for more advanced statistical tests. While powerful, SPSS requires time to learn due to its complexity. An understanding of statistics is still needed to properly interpret results.
SPSS (Statistical Package for the Social Sciences) is a statistical software package used for data management and analysis. It was developed in 1968 at Stanford University and acquired by IBM in 2009. SPSS allows users to easily obtain statistical results without programming by providing pre-programmed procedures for statistical analyses such as descriptive statistics, bivariate and multivariate analyses, predictive analytics, and graphics. Common uses of SPSS include market research, survey development and analysis, scientific research, and academic research.
SPSS (Statistical Package for the Social Sciences) is a statistical analysis software package that allows users to extract, manage, and analyze data. It provides features like generating reports, charts, descriptive statistics, and complex statistical analyses. While SPSS is easy to use and good for beginners, it has some limitations for advanced users in terms of customizing outputs and performing certain data manipulations. The document then describes the main SPSS interface windows including the data editor, output navigator, and syntax editor. It also covers how to open datasets, define variables, transform data, and create frequency tables and other outputs in SPSS.
This document summarizes OR/MS (operations research and management science) software available for microcomputers. It lists software packages for several common OR/MS techniques like linear programming, forecasting, and project management. It evaluates packages based on their functionality, size limitations, input and output features. The summary focuses on software for IBM PC and compatible machines running MS-DOS, as they have the greatest variety of software available. It aims to help readers select appropriate OR/MS software for their needs and applications.
This document discusses statistical packages used for data analysis. It describes the Statistical Package for the Social Sciences (SPSS) in detail. SPSS is a widely used statistical software package that can perform complex data analysis, including regression, ANOVA, and multivariate analysis. It has advantages such as easy data import, reliable results, and useful graphs. However, it also has limitations such as difficulty interpreting error logs and incomplete menus. The document also briefly mentions other packages like Microsoft Excel, SAS, Minitab, and Stata.
This document provides an introduction to the statistical software packages SPSS and STATA. It discusses why researchers may choose to use each package and highlights some of their key differences and strengths. SPSS is generally best for descriptive statistics and basic analyses, while STATA excels at more advanced econometric techniques and can handle larger datasets. The document also gives overviews of the basic structure and interfaces of each program.
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.
Statistical software tools like MS Excel, SPSS, and MiniTab can be used for statistical analysis.
MS Excel is commonly used due to its convenience and low cost, but requires statistical knowledge. It provides functions for descriptive statistics. SPSS is commonly used in social sciences for tasks like frequencies, cross-tabulation, and regression without coding. MiniTab provides statistical analysis tools and graphical visualization for processes like Six Sigma. Each tool has advantages like ease of use, analysis capabilities, and limitations like learning curves, file sizes, and costs.
Role of Computers in Research, Data Processing, Data AnalysisRKavithamani
The computers are indispensable throughout the research process. The role of computer becomes more important when the research is on a large sample. Data can be stored in computers for immediate use or can be stored in auxiliary memories like floppy discs, compact discs, universal serial buses (pen drives) or memory cards, so that the same can be retrieved later. The computers assist the researcher throughout different phases of research process.
This document provides an overview and instructions for using SPSS Statistics 17.0. It describes the software's capabilities for statistical analysis and data management. It also provides information on technical support, training resources, and additional publications. SPSS Statistics 17.0 is a comprehensive system for analyzing data that can import data from various file types and generate statistics, charts, and complex analyses.
This document provides an overview of the statistical software IBM SPSS and its uses. It discusses SPSS's abilities in descriptive statistics, bivariate statistics, prediction, and identifying groups. The document also explores the basic use of SPSS, including how to enter data and define variable meanings. Finally, it examines the dataset "country.sav" that could be used for a class project, focusing on variables like GDP, life expectancy, and healthcare that reflect living standards across countries.
Application of Excel and SPSS software for statistical analysis- Biostatistic...Himanshu Sharma
This slide contains B.Pharm Biostatistics and Research methodology 8th Sem. Unit-3 L2 topic- "Statistical Analysis using Software"
It contains topics:
1. MS Excel
2. SPSS
3. MiniTab
#StatisticalAnalysisusingMSExcel
#StatisticalAnalysisusingMiniTab
#StatisticalAnalysisusingSPSS
SoftwareforDataAnalysisinSPSSOnoverview1.docxAyyanar k
This study deals with the most important aspects of software in SPSS stands for "Statistical Package for the Social Sciences". It's a very powerful program that can do all of the statistics that you are ever likely to want to use. When it comes to giving you statistical results, it will give you what you want - as well as a lot of extra stuff that you may not need! The secret to using SPSS is to take it one small step at a time. This paper discusses the objectives of SPSS,Statistics included in the base software, How to use of SPSS, Feature of SPSS and Statistics Application for Software and IBM SPSSstatistics.
The document discusses advanced analytics capabilities in Tableau. It summarizes that Tableau allows both technical and non-technical users to perform advanced analytics tasks like segmentation, cohort analysis, scenario analysis, sophisticated calculations, time series analysis, and predictive analysis without requiring programming. It provides intuitive interfaces and drag-and-drop functionality for these advanced tasks. Tableau's calculation language also allows power users to build complex expressions and manipulate result sets.
The document discusses statistical analysis packages including SPSS. It provides an overview of SPSS, describing it as a statistical analysis and data management software package that can perform various analyses and generate reports. The document outlines some key features of SPSS, such as its ease of use, data management and editing tools, and statistical and visualization capabilities. It also briefly describes the different windows in SPSS and how to define and manipulate data.
The document discusses using statistical software packages to teach high school statistics and mathematics. It compares several popular packages on ease of use, power, and cost. Some mid-range options that provide a good balance are DataDesk, Fathom, Minitab, MegaStat, and Arc. More powerful packages like SAS, SPSS and Stata are similar to what professionals use but are harder to learn. Free and student versions can work for classroom use with some limitations.
This document provides an overview and requirements for the Stat project, an open source machine learning framework for text analysis. It describes the background, motivation, scope, and stakeholders of the project. Key requirements for the framework include being simplified, reusable, and providing built-in capabilities to naturally support text representation and processing tasks.
This document provides an introduction and overview of the Stat project, which aims to create an open source machine learning framework in Java for text analysis. The Stat framework is designed to be simple, extensible, and performant. It aims to simplify common text analysis tasks for researchers and engineers by providing reusable tools and wrappers for existing NLP and machine learning packages. The document outlines the goals, scope, stakeholders and provides an initial requirements analysis for the Stat framework.
This white paper outlines a 10-stage foundational methodology for data science projects. The methodology provides a framework to guide data scientists through the full lifecycle from defining business problems, collecting and preparing data, building and evaluating models, deploying solutions, and getting feedback to continually improve models. Some key stages include business understanding to define objectives, analytic approaches to determine techniques, data preparation which is often time-consuming, modeling to develop predictive or descriptive models, and evaluation of models before deployment. The iterative methodology helps data scientists address business goals through data analysis and gain ongoing insights for organizations.
European Pharmaceutical Contractor: SAS and R Team in Clinical ResearchKCR
Statistical analysis constitutes an essential part of every serious scientific research. Without data and a formal process of searching for evidences supporting or disproving stated hypotheses, there is nothing but mere opinion. Evidence-based medicine is no exception
Various statistical software's in data analysis.SelvaMani69
The document provides an overview of various statistical software used for data analysis. It discusses the history and emergence of statistical software, as well as common software packages for quantitative (e.g. SPSS, STATA, SAS) and qualitative (e.g. Atlas ti, HyperResearch) analysis. SPSS is described in more detail, including its point-and-click interface, ability to perform various analyses like regression and ANOVA, and examples of using it to code data, edit variable names, and create contingency tables. The document emphasizes that statistical software makes data analysis easier by automating calculations and reducing mathematical errors.
Dr. Pragyan Paramita Parija's presentation outlines statistics and biostatistics concepts and discusses various statistical software tools used in public health. It introduces statistics and defines biostatistics as applying statistical tools to biological data from medicine and public health. The presentation describes steps for research, applications of statistical software in public health, advantages of using computer software, and commonly used software like Excel, Epi Info, SPSS, SAS, and R. It provides an overview of each software including costs, pros, and cons to help users select the appropriate software.
1. The document discusses various topics related to data processing and analysis including defining data and information, the steps of data processing, types of data processing, what data analysis is, important types of data analysis methods, and qualitative study design and data analysis approaches.
2. It provides details on data editing, coding, classification, entry, validation, and tabulation as steps in data processing. Common statistical packages, tools, and software for data analysis are also outlined.
3. Qualitative research methods and coding systems are explained as well as qualitative data analysis software packages that can be used.
This document provides an overview and instructions for using IBM SPSS Statistics 19. It includes tutorials for basic functions like opening data files, running analyses, and viewing results. It also covers more advanced topics such as reading different data file types, using the Data Editor to enter and define variable properties, handling missing data, and working with multiple data sources. The document is intended to help new users learn the main capabilities and interface of IBM SPSS Statistics.
SDA (Survey Documentation and Analysis) is software that allows users to access and analyze numeric microdata from repositories without needing specialized statistical software. It generates descriptive and inferential statistics, and basic visualizations. SDA benefits researchers by providing statistical analysis capabilities and easy access to metadata. It benefits repositories by facilitating secondary use of data while protecting sensitive information. SDA shows the value of numeric data for teaching and research.
Mla Format Citation For Website With No Author - FoAllison Thompson
1. The document discusses building effective service learning programs in local communities to help change attitudes about teenagers and encourage their personal development.
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3. Proper facilitation to discuss social issues and designing content around student development are important for maximizing the benefits of community service programs.
Free Images Writing, Word, Keyboard, Vintage, Antique, RetroAllison Thompson
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Statistical software tools like MS Excel, SPSS, and MiniTab can be used for statistical analysis.
MS Excel is commonly used due to its convenience and low cost, but requires statistical knowledge. It provides functions for descriptive statistics. SPSS is commonly used in social sciences for tasks like frequencies, cross-tabulation, and regression without coding. MiniTab provides statistical analysis tools and graphical visualization for processes like Six Sigma. Each tool has advantages like ease of use, analysis capabilities, and limitations like learning curves, file sizes, and costs.
Role of Computers in Research, Data Processing, Data AnalysisRKavithamani
The computers are indispensable throughout the research process. The role of computer becomes more important when the research is on a large sample. Data can be stored in computers for immediate use or can be stored in auxiliary memories like floppy discs, compact discs, universal serial buses (pen drives) or memory cards, so that the same can be retrieved later. The computers assist the researcher throughout different phases of research process.
This document provides an overview and instructions for using SPSS Statistics 17.0. It describes the software's capabilities for statistical analysis and data management. It also provides information on technical support, training resources, and additional publications. SPSS Statistics 17.0 is a comprehensive system for analyzing data that can import data from various file types and generate statistics, charts, and complex analyses.
This document provides an overview of the statistical software IBM SPSS and its uses. It discusses SPSS's abilities in descriptive statistics, bivariate statistics, prediction, and identifying groups. The document also explores the basic use of SPSS, including how to enter data and define variable meanings. Finally, it examines the dataset "country.sav" that could be used for a class project, focusing on variables like GDP, life expectancy, and healthcare that reflect living standards across countries.
Application of Excel and SPSS software for statistical analysis- Biostatistic...Himanshu Sharma
This slide contains B.Pharm Biostatistics and Research methodology 8th Sem. Unit-3 L2 topic- "Statistical Analysis using Software"
It contains topics:
1. MS Excel
2. SPSS
3. MiniTab
#StatisticalAnalysisusingMSExcel
#StatisticalAnalysisusingMiniTab
#StatisticalAnalysisusingSPSS
SoftwareforDataAnalysisinSPSSOnoverview1.docxAyyanar k
This study deals with the most important aspects of software in SPSS stands for "Statistical Package for the Social Sciences". It's a very powerful program that can do all of the statistics that you are ever likely to want to use. When it comes to giving you statistical results, it will give you what you want - as well as a lot of extra stuff that you may not need! The secret to using SPSS is to take it one small step at a time. This paper discusses the objectives of SPSS,Statistics included in the base software, How to use of SPSS, Feature of SPSS and Statistics Application for Software and IBM SPSSstatistics.
The document discusses advanced analytics capabilities in Tableau. It summarizes that Tableau allows both technical and non-technical users to perform advanced analytics tasks like segmentation, cohort analysis, scenario analysis, sophisticated calculations, time series analysis, and predictive analysis without requiring programming. It provides intuitive interfaces and drag-and-drop functionality for these advanced tasks. Tableau's calculation language also allows power users to build complex expressions and manipulate result sets.
The document discusses statistical analysis packages including SPSS. It provides an overview of SPSS, describing it as a statistical analysis and data management software package that can perform various analyses and generate reports. The document outlines some key features of SPSS, such as its ease of use, data management and editing tools, and statistical and visualization capabilities. It also briefly describes the different windows in SPSS and how to define and manipulate data.
The document discusses using statistical software packages to teach high school statistics and mathematics. It compares several popular packages on ease of use, power, and cost. Some mid-range options that provide a good balance are DataDesk, Fathom, Minitab, MegaStat, and Arc. More powerful packages like SAS, SPSS and Stata are similar to what professionals use but are harder to learn. Free and student versions can work for classroom use with some limitations.
This document provides an overview and requirements for the Stat project, an open source machine learning framework for text analysis. It describes the background, motivation, scope, and stakeholders of the project. Key requirements for the framework include being simplified, reusable, and providing built-in capabilities to naturally support text representation and processing tasks.
This document provides an introduction and overview of the Stat project, which aims to create an open source machine learning framework in Java for text analysis. The Stat framework is designed to be simple, extensible, and performant. It aims to simplify common text analysis tasks for researchers and engineers by providing reusable tools and wrappers for existing NLP and machine learning packages. The document outlines the goals, scope, stakeholders and provides an initial requirements analysis for the Stat framework.
This white paper outlines a 10-stage foundational methodology for data science projects. The methodology provides a framework to guide data scientists through the full lifecycle from defining business problems, collecting and preparing data, building and evaluating models, deploying solutions, and getting feedback to continually improve models. Some key stages include business understanding to define objectives, analytic approaches to determine techniques, data preparation which is often time-consuming, modeling to develop predictive or descriptive models, and evaluation of models before deployment. The iterative methodology helps data scientists address business goals through data analysis and gain ongoing insights for organizations.
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The document provides an overview of various statistical software used for data analysis. It discusses the history and emergence of statistical software, as well as common software packages for quantitative (e.g. SPSS, STATA, SAS) and qualitative (e.g. Atlas ti, HyperResearch) analysis. SPSS is described in more detail, including its point-and-click interface, ability to perform various analyses like regression and ANOVA, and examples of using it to code data, edit variable names, and create contingency tables. The document emphasizes that statistical software makes data analysis easier by automating calculations and reducing mathematical errors.
Dr. Pragyan Paramita Parija's presentation outlines statistics and biostatistics concepts and discusses various statistical software tools used in public health. It introduces statistics and defines biostatistics as applying statistical tools to biological data from medicine and public health. The presentation describes steps for research, applications of statistical software in public health, advantages of using computer software, and commonly used software like Excel, Epi Info, SPSS, SAS, and R. It provides an overview of each software including costs, pros, and cons to help users select the appropriate software.
1. The document discusses various topics related to data processing and analysis including defining data and information, the steps of data processing, types of data processing, what data analysis is, important types of data analysis methods, and qualitative study design and data analysis approaches.
2. It provides details on data editing, coding, classification, entry, validation, and tabulation as steps in data processing. Common statistical packages, tools, and software for data analysis are also outlined.
3. Qualitative research methods and coding systems are explained as well as qualitative data analysis software packages that can be used.
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Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
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There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
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𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
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Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
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6. An Introduction To Statistical Package For The Social Sciences
1. 53
6. An Introduction to Statistical Package for the
Social Sciences
Nick Emtage and Stephen Duthy
This module provides an introduction to statistical analysis, particularly in regard to survey
data. Some of the features of the Statistical Package for the Social Sciences (SPSS) are
then explained, with reference to a farm forestry survey. Of necessity, this is a brief overview
to the highly complex and powerful SPSS package.
1. INTRODUCTION
Computer based statistical packages are an
important tool for researchers in the social
sciences. The prospect of using statistics is
sometimes either repugnant or simply
frightening for people, yet most researchers
recognise the potential utility of statistical
analysis to aid them to describe, analyse,
interpret and report their data. The
mathematics behind statistical analysis can
be daunting for those who have little formal
training in either mathematics or the use of
statistics. The development of specialist
statistical analysis packages has greatly
reduced the mathematical challenge of
undertaking many analyses. It should be
emphasised, however, that these packages
have not reduced the need for researchers
to understand the assumptions behind
statistical analyses, and to be able to
interpret their results. The packages have
however reduced the need for researchers
to be able to undertake many of the
calculations that are required for statistical
analyses. In this way they allow researchers
to concentrate on understanding the
assumptions behind the various methods,
as well as the potential applications and
limitations of various statistical tests.
Statistical software packages have, like
other software packages, changed greatly
since the advent of the personal computer a
little over 20 years ago. Some of the
authors still remember programming
mainframe computers with paper cards.
Holes were punched into the cards and
these were then fed into the computer.
Computers in those days were scarce,
especially the big ones with four megabyte
memory! Needless to say that statistical
tests were difficult to perform unless the
user had an advanced understanding of the
mathematics required. More recent
computer software packages are
reasonably easy to use for people with
some familiarity with computers. Most of the
packages have features such as drop-down
menus, ‘tree’ structure diagrams and on-line
help systems. This said, it should be
remembered that the packages discussed
here are large and highly complex. While
they are considerably easier to use today
than they were even 10 years ago, like
other large software packages, familiarity
and ease of use are only developed through
practice with the package. A user can
become functionally proficient with a
package such as Excel and Word after
several weeks, use but development of a
high level of expertise can take many
months or even several years.
When choosing which package to use for
statistical analyses a number of factors
must be considered. These include the
availability of a package, its cost, the
functions it can perform, familiarity with the
package and the availability of an expert
statistician to assist with the analysis
process. As discussed above, the packages
take time and effort to learn, and many
researchers prefer to continue using a
particular package once they learned how
to use it. Other factors may affect this
however. Availability of a package is an
important factor in deciding whether to use
it or not. If an institution has already
obtained the rights to use a particular
package, it may be the only choice
available. Buying copies of the latest
versions of the specialist statistical
packages is expensive, as is the cost of
maintaining the license to use the package.
If an institution already has a package that
2. Socio-economic Research Methods in Forestry
54
can provide the functions required then the
researcher may be forced to use that
package despite preferences for other
software because of limited funds.
Where expert statisticians are available to
assist with data analyses then the preferred
package of the expert is likely to be the one
used. As discussed in other modules it is
important to discuss research projects with
expert statisticians during their design to
ensure that the data collected will be in a
format that allows the use of the desired
analysis techniques. It is also important at
this stage to discuss the packages available
to the researcher and the time available to
access a computer for data entry, analysis
and reporting. Where access to the
computers with the statistical software is
limited it may be possible for the researcher
to enter the data into a spreadsheet
program like Excel and then transfer the
data set to the statistics package in order to
carry out the analyses. In this case it is
important to have some understanding of
the formatting required by the statistical
package to be used so as to avoid
unnecessary reformatting of the data in the
statistical package. Where possible data
should be entered directly into the statistical
package to avoid the potential need to
reformat the data.
2. THE STATISTICAL PROGRAM FOR
SOCIAL SCIENTISTS (SPSS)
The SPSS Corporation first produced the
SPSS software package in the early 1980’s
and has recently released version 11.0. It is
presently one of the most commonly used
statistical packages in Australian research
institutions and is available at all Australian
universities. The advantages of the
package are its relative ease of use, its
familiarity to many statistical experts and its
functionality. One of SPSS’s major
disadvantages is its cost. The SPSS
corporation appears to be progressively
breaking up the program into different
sections that can be purchased separately.
For Australian students an individual users’
license (one year) costs approximately
$A100 for a ‘base’ student version and
$A350 for a ‘graduate pack’ licensed for 5
years (as of March 2002). The different
versions have varying analytical functions
and different capacities in terms of the
number of cases and variables that can be
used. An institutional license costs even
more, depending on the number of
expected users. The different packages
have licenses that also differ. In most cases
licenses are set up to expire automatically
after a limited period after which the
package can no longer be used. The
package is developed for a number of
operating systems including Windows and
Unix. Information about SPSS products is
available on-line at www.SPSS.com.
Organisation of the SPSS package
The set-up of the version 10.0 package
(used for illustration here) is organised into
two main sections, for defining and entering
data and for output. When defining and
entering data, users can move between the
‘variable’ and ‘data’ ‘views’ by clicking on
the tabs at the bottom of the screen. The
third ‘output’ section opens in a separate
window and displays the results of the
statistical analyses. The ‘output’ data are
saved as a separate file to the data set.
In the ‘variable view’ (Figure 1) the users
sets up the data entry and analysis cells by
naming and defining the variables included
in the data set. Users are required to use
names for the variables of eight or fewer
characters. Names must begin with an
alphabetic character. Longer descriptions of
the variables can be added using the
‘Labels’ dialog box (Figure 2). A quick way
to define the variable format (including the
variable type, the number of characters
used and labels) if a number of variables
have a similar format is to copy the
attributes of a variable then paste them into
other variable fields.
Once the variables to be recorded have
been named and defined the user can
access the ‘data view’ to enter in the values
for each variable. The SPSS data view
looks similar to a spreadsheet program. The
variables are organised as columns with
each row as a single ‘case’ in the data set
containing values for the variables relating
to that case. It is common practice to use
codes to enter data into the package and
labels can be used to describe values
where needed. For example, codes may be
used to record the types of agriculture
practiced on a landholding, or respondent’s
3. An Introduction to Statistical Package for the Social Sciences 55
educational levels. The defined labels will
appear, by clicking the drop-down list arrow
on the right side of the cell, and the user
can select the relevant value (Figure 3).
Figure 1. Variable view in SPSS 10.0
Figure 2. Defining variable labels using the Value labels dialog box
4. Socio-economic Research Methods in Forestry
56
Figure 3. Entering data into the SPSS
This is handy when there is a large number
of possible responses, and thus codes, for
a variable, and the user cannot remember
all of them. The user can choose to have
the codes or the labels displayed in the data
view by selecting the ‘Value labels’ option
under the ‘View’ menu.
Data analysis using SPSS
The SPSS ‘student pack’ has a wide range
of analytical functions, from basic
descriptive statistics to advanced general
linear modeling capabilities. Specific
functions are also included to allow the
transformation of variables as preparation
for different tests (e.g. for creating
standardised or logarithmic values, or the
calculation of scales from a number of
variables) (Figure 4). The use of these
functions allows researchers to calculate
quickly new variables based on the values
of other variables, test variations in
category schemes used to classify
responses to ‘open ended’ questions, and
collapse categories where necessary.
Once the data are entered into the SPSS
program it is important to check the
database for typographic errors that may
affect the results of statistical analyses. One
means of achieving this is to examine the
frequencies of categorical (nominal) data,
and descriptive statistics of numeric
(ordinal, scale or interval) data. All of the
analytical functions available in SPSS can
be accessed using the ‘Analyse’ menu
(Figure 6). If the ‘Descriptive statistics’ then
the ‘Frequencies’ options are selected, the
dialog box illustrated in Figure 5 appears.
This dialog box enables users to select the
variables for which frequencies are
computed as well as control the types and,
to a limited extent, the formatting of displays
of the analyses.
If calculation of descriptive statistics is
required, users should select ‘Descriptive
statistics’ and the ‘Descriptives’ options
under the Analysis menu to reveal the
‘Descriptives’ dialog box (Figure 7).
5. An Introduction to Statistical Package for the Social Sciences 57
Figure 4. Data transformation functions in SPSS
Figure 5. Frequencies dialog box
6. Socio-economic Research Methods in Forestry
58
Figure 6. Analysis options available in SPSS
Figure 7. Descriptives function dialog box
7. An Introduction to Statistical Package for the Social Sciences 59
Once the ‘Descriptives’ dialog box is shown,
the variables to be included in the analyses
are selected from the list on the left side of
the box (Figure 7), and transferred to the list
on the right side of the box (labeled
‘Variables’ in Figure 7) using the arrow in
the centre of the box. The types of
descriptive statistics that will be Calculated
using this function can be selected by
clicking on the ‘Options…’ button (Figure 7).
This reveals the ‘Options’ dialog box for the
Descriptives function (Figure 8).
Other analytical functions included in the
SPSS student pack (Version 10) include
chi-square tests, correlations, regressions,
principal components analyses, ANOVA,
cluster analyses, general linear modeling
and more.
Whilst this paper does not attempt to
provide the reader with statistical skills, the
flowchart in Figure 9 may act as a guide for
the reader to access quickly those functions
in SPSS that will best serve their statistical
analysis needs.
The analytical functions are adequate for all
but the most advanced researchers or
those requiring highly specific analyses.
Most advanced or specific applications can
be met as well, with SPSS open to
manipulation via user compiled ‘Sax Basic’
computer code (also known as ‘scripts’ in
SPSS). This is similar to the use of the
Visual Basic programming language to
develop and execute macros in Microsoft
Excel. The Sax Basic language is
compatible with Visual Basic for
Applications.
Figure 8. Options dialog box for the
‘Descriptives’ function dialog box
8. Purpose of
statistical analysis
Summarizing
univariate data
Exploring
relationships
between variables
Testing
significance of
differences
Descriptive
statistics
(mean,
standard
deviation,
variance, etc)
Form of data Number of groups
Frequencies
Number of
variables
Measurements
Number of
variables
One: compared to
theoretical
distribution
Two: tested for
association
Chi-square
goodness-of-fit
test
Chi-square test for
association
Two: degree of
relationship
Multiple: effect
of 2+ predictors
on a dependant
variable
Multiple
regression
Level of
measurement
Spearman's
rho
Pearson's
correlation
cooeficient
One: mean
compared to
a specified
value
One-sample
t-test
Two
Independent
samples
Related
samples
Form of
data
Form of data
Ordinal Interval
Mann-Whitney
U test
Independent-
samples t-test
Ordinal Interval
Ordinal Interval
Wilcoxon
matched-
pairs test
Paired-
samples
t-test
Multiple
Independent
samples
Related
samples
Repeated-
measures
ANOVA
One
independent
variable
One-way
ANOVA
Multiple
independent
variables
Multifactorial
ANOVA
Source: Corston and Colman (2000)
Figure 9. Choosing an appropriate statistical procedure
9. An Introduction to Statistical Package for the Social Sciences 61
Using SPSS to Describe Data
Whilst computer-based statistical packages
provide a high degree of functionality with
regard to data analysis, they also provide a
number of highly useful tools for the
description and presentation of summaries
of the dataset.
These functions include Descriptives and
Frequencies as explained earlier and
Crosstabs, also found under the Descriptive
Statistics menu, and Basic Tables, General
Tables, Multiple Response Tables and
Tables of Frequencies all located under the
Custom Tables menu item (Figure 10). It is
often useful to undertake one or more of
these processes before commencing data
analysis to identify any weaknesses in the
dataset such as poorly represented groups
within the sample that may limit the
statistical validity of some forms of analysis.
Crosstabs are also an efficient way of
presenting data summaries in research and
project reports.
The charting functions available in SPSS
also provide a number of techniques for the
initial exploration and the presentation of
data. Scatter Plots (Figures 11 and 12) can
be used to identify quickly the presence and
nature of any correlations between
variables while Histograms (Figures 13 and
14) can be used to present a graphical
representation of the shape of the
distribution of the data for important
variables.
There is a reasonable amount of literature
available to assist users of the SPSS
package produced by the SPSS
Corporation and by independent authors.
The tutorial and help facilities for the
package are comprehensive, generally
easy to understand and include the on-line
Statistics Coach and Syntax Guide.
Figure 10. Custom Tables drop-down menu selections
10. Socio-economic Research Methods in Forestry
62
Figure 11. Design options for a Simple Scatterplot
Figure 12. Simple Scatterplot displayed in the Output Viewer
11. An Introduction to Statistical Package for the Social Sciences 63
Figure 13. Design options for a histogram
Figure 14. Histogram displayed in the Output Viewer
12. Socio-economic Research Methods in Forestry
64
3. CONCLUDING COMMENTS
Researchers frequently collect large
quantities of data, from surveys,
experiments and other forms of
observation. A statistical computing
package provides a convenient means to
store these data, and derive descriptive and
inferential statistics. The Statistical Package
for the Social Sciences (SPSS) is a widely
used general-purpose survey analysis
package, and hence a useful one to master.
It is necessary to allow some learning time
to become familiar with this package, and
annual license fees can be a disincentive.
REFERENCES
Corston, R. and Colman, A. (2000), A
Crash Course in SPSS for Windows,
Blackwell, Oxford.