Ultimate Step-By-Step Practical Guide to Data Analysis Using Spss Software - N1,800 or $7




Do your want to start data analysis career using Spss statistical tool?  Worry no more as we have all you need: training material courses on Spss

Data and information could be viewed as historical records by many, it is however far more important for Professionals; data holds the key to forecasting the future. Let us help you acquire mastery of data analysis and modeling; we have helped many professionals acquire relevant skills.

Outlined are reasons never to miss this opportunity and skill



#Data tells more than historical, an understanding of your data would lead to Effective Planning


#Today’s businesses need timely information that helps the business people to take important decisions in business.


#Every business should have sound financial planning and forecasting to leverage the business.


#The emergence of a new business model,  the changing needs of the traditional financial department and the advancement in technology have all led to the need for financial analytics.


#Get a deeper insight into the financial status of your business and improve the profitability, cash flow and value of your business.



#It will help in making smart decisions to increase the business revenue and minimize the waste of the business


Acquire the data analysis Skills you require in this New Age And lot more……..


THE  FOLLOWING ARE WHAT YOU WILL BE LEARNING FROM THESE COURSES


PART ONE: Getting started


1. Designing a study.


 Planning the study.

Choosing appropriate scales and measures.


Preparing a questionnaire.


2. Preparing a codebook.


Variable names.

Coding responses.


Coding open-ended questions.


3. Getting to know SPSS


Starting SPSS.

Opening an existing data file.


Working with data files.


SPSS windows.


Menus.


Dialogue boxes


Closing SPSS.


PART TWO: Preparing the data file


4.  Creating a data file and entering data.


Changing the SPSS ‘Options’.

Defining the variables.


Entering data.


Modifying the data file.


Data entry using Excel.


5. Screening and cleaning the data


Step 1: Checking for errors.

Step 2: Finding the error in the data file.


Step 3: Correcting the error in the data file.



PART THREE: Preliminary analyses


6. Descriptive statistics.


Categorical variables.

Continuous variables.


Assessing normality.


Checking for outliers.


Additional exercises.


7.  Using graphs to describe and explore the data.


Histograms.

Bar graphs.


Scatterplots.


Boxplots.


Line graphs.


Editing a chart/graph.


Importing charts/graphs 
into Word documents.



8. Manipulating the data.


Calculating total scale scores.

Transforming variables.


Collapsing a continuous variable into groups.


Collapsing the number of categories of a categorical variable.



9. Checking the reliability of a scale


Details of example.

Interpreting the output from reliability.


Presenting the results from reliability.


10.  Choosing the right statistic.


Overview of the different statistical techniques.

The decision-making process.


Key features of the major statistical techniques.


Summary table of the characteristics of the main statistical techniques.


PART FOUR: Statistical techniques to explore relationships among variables


Techniques covered in Part Four.

Revision of the basics.


11. Correlation.


Details of example.

Preliminary analyses for correlation.


Interpretation of output from correlation.


Presenting the results from correlation.


Obtaining correlation coefficients between groups of variables.


Comparing the correlation coefficients for two groups.


Testing the statistical significance of the difference between 
correlation coefficients.


12. Partial correlation


Details of example.

Interpretation of output from partial correlation.


Presenting the results from partial correlation.




13. Multiple regression


Major types of multiple regression.

Assumptions of multiple regression.


Details of example.


Standard multiple regression.


Hierarchical multiple regression.


Interpretation of output from hierarchical multiple regression.


Presenting the results from multiple regression.


 14.  Logistic regression


Assumptions.

Details of example.


Data preparation: coding of responses.


Interpretion of output from logistic regression.


Presenting the results from logistic regression.



15.  Factor analysis


Steps involved in factor analysis.

Details of example.


Procedure for factor analysis.


Warning.


Presenting the results from factor analysis.


Additional exercises.


PART FIVE: Statistical techniques to compare groups



Techniques covered in Part Five.

Assumptions.


Type 1 error, Type 2 error and power.


Planned comparisons/Post-hoc analyses.


Effect size.


16.  T-testsIndependent-samples t-test


Paired-samples t-test.

Additional exercises.


17.  One-way analysis of variance


One-way between-groups ANOVA with post-hoc tests.

One-way between-groups ANOVA with planned comparisons.


One-way repeated measures ANOVA.


Additional exercises.


18. Two-way between-groups ANOVA.


Details of example.

Interpretation of output from two-way ANOVA.


Presenting the results from two-way ANOVA.


Additional analyses if you obtain a

significant interaction effect.


19.  Mixed between-within subjects analysis of variance.


Details of example.

Interpretation of output from mixed between-within ANOVA.


Presenting the results from mixed between-within ANOVA.


20.  Multivariate analysis of variance.


Details of example.

Assumption testing.


Performing MANOVA.


Interpretation of output from MANOVA.


Presenting the results from MANOVA.


Additional exercises.



21.  Analysis of covariance.


Uses of ANCOVA.

Assumptions of ANCOVA.


One-way ANCOVA.


Two-way ANCOVA.




22.  Non-parametric statistics.


Summary of techniques covered in this chapter.

Chi-square.


Mann-Whitney U Test.


Wilcoxon Signed Rank Test.


Kruskal-Wallis Test.


Friedman Test.


Spearman’s Rank Order Correlation.



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