Introduction to SPSS

Introduction to SPSS

The course is aimed to cover some of the most frequent applications of SPSS, such as displaying statistical data graphically, principal component analysis, factor analysis,hypothesis analysis, analysis of variance, linear and multilinear regressions

In the end of this course students will be able to handle statistical data analysis on their own. During the course both pedagogically prepared "artificial" data and data from real surveys are used. No advanced prior knowledge on statistics is required, all the needed concepts will be explained.

SPSS is regarded to be the most widely used statistical software in social sciences,and it has become a common tool also in other sciences (economics, biology etc.).

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Course details

Level: Beginner
Instructor:
Duration: 20 days
Requirements for pass: minimum attendance: 5 days; submitting a short statistical research on the discussed data set
Course handout: see at the Computer Center's wiki site.
When the course is offered: see the list of courses for the current semester and/or theUIS

Course outline

Day 1

Basics of SPSS. Descriptive statistics, charts and graphs, hypothesis analysis, testing dependence/independence.

• Different levels of measure: scale, ordinal, nominal.
• Basic descriptive statistics. Measures of central tendency: mean, median, mode. Measures of dispersion: range, standard deviation, variance.
• Graphs and charts: bar chart, pie chart, histogram, scatter plot.Which of them should be used in different situations?
• Hypothesis analysis with SPSS. Testing dependence/independence, Pearsons chi-square. Levels of significance.

Day 2

SPSS in practice, some useful tips. The "tricks" of dependence/independence testing

• Converting different types of files into ".sav" files. How can one enter raw data into SPSS efficiently, how to label the data.
• Transforming the data.
• Multiple independence analysis - a useful way to circumvent "significance level problems".
• Elementary principal component analysis.

Day 3

Principal Component Analysis with SPSS.

• Definition and meaning of the principal component.
• Communalities, extraction, variance.
• Usability of the method (the cases of scale and ordinal measures).
• Information content and the distribution of the principal component.
• Omission of variables with insufficient communalities.

Day 4

Factor Analysis with SPSS using a "real example".

• The factor matrix and its interpretation.
• The Maximum Likelihood method. Reparing the model.
• Factor rotation and the varimax method.
• Omission of variables belonging to more than one factor, the appearence of latent variables.
• Establishing factor scores. Statistics of factor scores.
• When the factors explain more than 100%. A common pitfall.

Day 5

Explanatory models. Analysis of Variance, Regression Analysis.

• Using SPSS for Analysis of Variance (ANOVA)
• Twofold ANOVA, interaction.
• Linear regression analysis. When should we accept a regression line?
• Two variable regressions.

Day 6

Charts and tables

• Simple charts: bar, pie, histogram
• Compund charts, chart builder
• Chart editing, 3D effects, color and fill
• Addig special effect to charts, jittering, pletora of graphs
• Saving and manipulating tables

Further more details: 

1. SPSS TRAINING IN KATHMANDU

1. SPSS TRAINING IN LALITPUR

SPSS THEORY

Why SPSS software?

With SPSS predictive analytics software, you can predict with confidence what will happen next so that you can make smarter decisions, solve problems and improve outcomes.

Overview

SPSS is a widely used program for statistical analysis in social science. It is also used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations, data miners,^[3] and others. The original SPSS manual (Nie, Bent & Hull, 1970) has been described as one of "sociology's most influential books" for allowing ordinary researchers to do their own statistical analysis.^[4] In addition to statistical analysis, data management (case selection, file reshaping, creating derived data) and data documentation (a metadata dictionary was stored in the datafile) are features of the base software.

Statistics included in the base software:

• Descriptive statistics: Cross tabulation, Frequencies, Descriptives, Explore, Descriptive Ratio Statistics
• Bivariate statistics: Means, t-test, ANOVA, Correlation (bivariate, partial, distances), Nonparametric tests
• Prediction for numerical outcomes: Linear regression
• Prediction for identifying groups: Factor analysis, cluster analysis (two-step, K-means, hierarchical), Discriminant

The many features of SPSS Statistics are accessible via pull-down menus or can be programmed with a proprietary 4GL command syntax language. Command syntax programming has the benefits of reproducibility, simplifying repetitive tasks, and handling complex data manipulations and analyses. Additionally, some complex applications can only be programmed in syntax and are not accessible through the menu structure. The pull-down menu interface also generates command syntax: this can be displayed in the output, although the default settings have to be changed to make the syntax visible to the user. They can also be pasted into a syntax file using the "paste" button present in each menu. Programs can be run interactively or unattended, using the supplied Production Job Facility.

Additionally a "macro" language can be used to write command language subroutines. A Python programmability extension can access the information in the data dictionary and data and dynamically build command syntax programs. The Python programmability extension, introduced in SPSS 14, replaced the less functional SAX Basic "scripts" for most purposes, although SaxBasic remains available. In addition, the Python extension allows SPSS to run any of the statistics in the free software package R. From version 14 onwards, SPSS can be driven externally by a Python or a VB.NET program using supplied "plug-ins". (From Version 20 onwards, these two scripting facilities, as well as many scripts, are included on the installation media and are normally installed by default.)

SPSS Statistics places constraints on internal file structure, data types, data processing, and matching files, which together considerably simplify programming. SPSS datasets have a two-dimensional table structure, where the rows typically represent cases (such as individuals or households) and the columns represent measurements (such as age, sex, or household income). Only two data types are defined: numeric and text (or "string"). All data processing occurs sequentially case-by-case through the file. Files can be matched one-to-one and one-to-many, but not many-to-many.

The graphical user interface has two views which can be toggled by clicking on one of the two tabs in the bottom left of the SPSS Statistics window. The 'Data View' shows aspreadsheet view of the cases (rows) and variables (columns). Unlike spreadsheets, the data cells can only contain numbers or text, and formulas cannot be stored in these cells. The 'Variable View' displays the metadata dictionary where each row represents a variable and shows the variable name, variable label, value label(s), print width, measurement type, and a variety of other characteristics. Cells in both views can be manually edited, defining the file structure and allowing data entry without using command syntax. This may be sufficient for small datasets. Larger datasets such as statistical surveys are more often created in data entry software, or entered during computer-assisted personal interviewing, by scanning and using optical character recognition and optical mark recognition software, or by direct capture from online questionnaires. These datasets are then read into SPSS.

SPSS Statistics can read and write data from ASCII text files (including hierarchical files), other statistics packages, spreadsheets and databases. SPSS Statistics can read and write to external relational database tables via ODBC and SQL.

SPSS TRAINING IN KATHMANDU

The SPSS logo used prior to the renaming in January 2010.

SPSS Statistics Server is a version of SPSS Statistics with a client/server architecture. It had some features not available in the desktop version, such as scoring functions. (Scoring functions are included in the desktop version from version 19.)

1. SPSS TRAINING IN KATHMANDU

1. SPSS TRAINING IN LALITPUR