SPSS
Introduction
SPSS COURSE Statistical Package for the Social Sciences (SPSS) is a comprehensive and flexible statistical analysis and data management solution produced by IBM and makes statistical data analysis straightforward. SPSS can take data from almost any type of file and use them to generate tabulated reports, charts, and plots of distributions and trends, descriptive statistics, and conduct complex statistical analyses
WHY THIS SPSS COURSE?
SPSS is a widely used program for statistical analysis in social science. SPSS is a powerful suite of data analytics, reporting and modeling software. It is the data analytics software package of choice for people who need to analyze large amounts of quantitative data and is used as standard by a wide range of users who need to quickly and accurately carry out statistical analysis of that data such as market researchers, health researchers, survey companies, government, education researchers, marketing organizations and data miners.
SPSS functionality ranges from simple plots and charts; through to much more sophisticated statistical tests. Although very broadly applicable, SPSS is very well-suited to analyzing survey data. It has a very broad range of applications from the relatively simple like calculating frequencies and creating lists and charts through to the far more advanced like variance analysis, multivariate statistical analysis and cluster analysis. It is also useful in building predictive models.
ABOUT THE COURSE:
This SPSS course will teach you the fundamentals of using SPSS. This SPSS course involves working on a number of practical, hands on examples and exercises in a workshop environment. Led by highly experienced SPSS trainers with many years of experience here at GOOLEE mean that our hands-on courses will leave you with lots of practical SPSS experience as well as useful tips and tricks. Our course will show you how to make the most of SPSS software package
COURSE GOAL:
The goal of the course is to increase knowledge and requisite skills of participants on the use of SPSS and to enable them make the most of this powerful software package while allowing them to work independently with SPSS on their own data and provide a solid foundation for advanced data analysis work.
OBJECTIVES OF THE COURSE:
To introduce participants to the basic practice of statistics by using SPSS Statistics, a statistical software program used for data management and data analysis and learn how to perform basic statistical analyses. v To introduce participants to the use of advanced SPSS for analyzing project data for reporting purposes focusing on database management tasks, descriptive statistics and graphics, and basic inferential statistics for comparisons and correlations.
COURSE CONTENT:
The course is organized in modules and each module covers a reasonable content to enable participants connect and progress successively in the next modules.
Module 1: SPSS interface and features
Module 2: Data storage types, measures & data management in SPSS
Module 3: Creating & editing quantitative & categorical graphs/charts
Module 4: Advanced data tabulation
Module 5: Point estimation & hypotheses testing
Module 6: Inferential analysis
Module 7: Time series & Panel analysis
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
WHAT YOU REQUIRE:
Interested organizations/institutions/individuals are expected to have laptop(s) while attending the workshop, and individuals attending should be from any discipline. Please feel free to bring your own datasets for practice while performing the tasks demonstrated by the trainer. If you have SPSS already installed on your machine the better. However it is not a must because you will be provided with a temporary version for the training.
Further more details:
