SPSS For Research
4.5 (220 ratings)
8,924 students enrolled
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# SPSS For Research

SPSS data analysis made easy. Become an expert in advanced statistical analysis with SPSS.
Bestselling
4.5 (220 ratings)
8,924 students enrolled
Created by Bogdan Anastasiei
Last updated 6/2015
English
Current price: \$10 Original price: \$50 Discount: 80% off
30-Day Money-Back Guarantee
Includes:
• 14 hours on-demand video
• 1 Article
• Access on mobile and TV
• Certificate of Completion
What Will I Learn?
• perform simple operations with data: define variables, recode variables, create dummy variables, select and weight cases, split files
• built the most useful charts in SPSS: column charts, line charts, scatterplot charts, boxplot diagrams
• perform the basic data analysis procedures: Frequencies, Descriptives, Explore, Means, Crosstabs
• test the hypothesis of normality (with numeric and graphic methods)
• detect the outliers in a data series (with numeric and graphic methods)
• transform variables
• perform the main one-sample analyses: one-sample t test, binomial test, chi square for goodness of fit
• perform the tests of association: Pearson and Spearman correlation, partial correlation, chi square test for association, loglinear analysis
• execute the analyses for means comparison: t test, between-subjects ANOVA, repeated measures ANOVA, nonparametric tests (Mann-Whitney, Wilcoxon, Kruskal-Wallis etc.)
• perform the regression analysis (simple and multiple regression, sequential regression, logistic regression)
• compute and interpret various tyes of reliability indicators (Cronbach's alpha, Cohen's kappa, Kendall's W)
• use the data reduction techniques (multidimensional scaling, principal component analysis, correspondence analysis)
• use the main grouping techniques (cluster analysis, discriminant analysis)
View Curriculum
Requirements
• the SPSS package (version 18 or newer recommended)
• very basic knowledge of statistics (mean, standard deviation, confidence interval, significance level, things like that)
Description

Become an expert in statistical analysis with the most extended SPSS course at Udemy: 146 video lectures covering about 15 hours of video!

Within a very short time you will master all the essential skills of an SPSS data analyst, from the simplest operations with data to the advanced multivariate techniques like logistic regression, multidimensional scaling or principal component analysis.

The good news – you don't need any previous experience with SPSS. If you know the very basic statistical concepts, that will do.

And you don't need to be a mathematician or a statistician to take this course (neither am I). This course was especially conceived for people who are not professional mathematicians – all the statistical procedures are presented in a simple, straightforward manner, avoiding the technical jargon and the mathematical formulas as much as possible. The formulas are used only when it is absolutely necessary, and they are thoroughly explained.

Are you a student or a PhD candidate? An academic researcher looking to improve your statistical analysis skills? Are you dreaming to get a job in the statistical analysis field some day? Are you simply passionate about quantitative analysis? This course is for you, no doubt about it.

Very important: this is not just an SPSS tutorial. It does not only show you which menu to select or which button to click in order to run some procedure. This is a hands-on statistical analysis course in the proper sense of the word.

For each statistical procedure I provide the following pieces of information:

• a short, but comprehensive description (so you understand what that technique can do for you)
• how to perform the procedure in SPSS (live)
• how to interpret the main output, so you can check your hypotheses and find the answers you need for your research)

The course contains 56 guides, presenting 56 statistical procedures, from the simplest to the most advanced (many similar courses out there don't go far beyond the basics).

The first guides are absolutely free, so you can dive into the course right now, at no risk. And don't forget that you have 30 full days to evaluate it. If you are not happy, you get your money back.

So, what do you have to lose?

Who is the target audience?
• students
• PhD candidates
• University teachers
• anyone looking for a job in the statistical analysis field
• anyone who is passionate about quantitative research
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Curriculum For This Course
149 Lectures
14:03:29
+
Getting Started
2 Lectures 09:45

What's it all about - why you should take this course.

Preview 04:54

See the detailed structure of this course here.

Preview 04:51
+
The Basics
7 Lectures 53:22

How to create a file and open an existing file in SPSS.

Preview 02:41

How to create variables and set variable properties.

Preview 12:08

Learn when you need to recode your variables and how to do it.

Preview 09:29

How to convert dichotomous and multinomial variables into dummy variables.

Preview 07:52

How to filter out cases in an SPSS data set.

Preview 07:11

How to split file using certain criteria in order to perform analyses on groups or strata of the population.

Preview 02:52

Know when it is necessary to weigh your cases and how to perform this operation.

Preview 11:09
+
Creating Charts in SPSS
4 Lectures 19:46

Learn how to build column charts in SPSS.

Preview 06:41

Learn how to build and interpret line charts.

Preview 04:35

How to use the Chart Builder in order to create simple and grouped scatterplot charts.

Preview 04:06

How to build and interpret boxplot charts (simple and grouped).

Preview 04:24
+
Simple Analysis Techniques
5 Lectures 19:36

How to use the Frequencies procedure to build frequency tables and to generate statistical indicators.

Preview 05:47

How to generate the essential statistics for continuous variables.

Preview 01:56

The Explore procedure helps you generate statistical indicators by groups or strata, create graphs and run normality tests.

Preview 05:09

Another quick and easy procedure to compute the statistics for a continuous variable.

Preview 03:23

How to build cross tables to visualize the relationship between categorical variables.

Guide 16: Crosstabs Procedure
03:21
+
Assumption Checking. Data Transformations
7 Lectures 31:27

How to compute and interpret the statistical tests for normality.

Guide 17: Checking for Normality - Numerical Methods
06:34

How to use charts in order to assess normality.

Guide 17: Checking for Normality - Graphical Methods
03:33

How to handle the non normal distributions (which are not uncommon).

Guide 17: Checking for Normality - What to Do If We Do Not Have Normality?
02:08

How to use the boxplot diagram in order to check for outliers in your data.

Guide 18: Detecting Outliers - Graphical Methods
03:38

How to detect the outliers with the help of the standardized scores.

Guide 18: Detecting Outliers - Numerical Methods
03:30

What to do if you have extreme values in your data series.

Guide 18: Detecting Outliers - How to Handle the Outliers
03:12

How to transform your variables in an attempt to get normal distributions (unfortunately, often this is not possible).

Guide 19: Data Transformations
08:52
+
One-Sample Tests
6 Lectures 24:24

When and why to use the one-sample t test.

Preview 04:08

How to perform the one-sample t test and interpret the results.

Guide 20: One-Sample T Test - Running the Procedure
03:17

How to perform the binomial test in order to analyze the dichotomous variables.

Guide 21: Binomial Test
04:51

How to use the binomial test when your data are weighted.

Guide 21: Binomial Test with Weighted Data
03:45

The chi square test for goodness-of-fit is very useful when you study the categorical variables with more than two groups.

Guide 22: Chi Square for Goodness-of-Fit
05:40

How to perform the chi square test for goodness-of-fit when your data are weighted.

Guide 22: Chi Square for Goodness-of-Fit with Weighted Data
02:43
+
Association Tests
12 Lectures 01:09:17

When and how to use the Pearson correlation coefficient.

Preview 03:56

How to check the assumptions of the Pearson correlation procedure.

Guide 23: Pearson Correlation - Assumption Checking
03:58

How to compute and interpret the Pearson correlation coefficient.

Guide 23: Pearson Correlation - Running the Procedure
03:23

When and why you should use the Spearman correlation.

Guide 24: Spearman Correlation - Introduction
05:12

How to compute the Spearman correlation coefficient and interpret it.

Guide 24: Spearman Correlation - Running the Procedure
02:43

What is partial correlation? The four scenarios for analyzing the partial correlation coefficient.

Guide 25: Partial Correlation - Introduction
05:33

How to compute and interpret the partial correlation coefficient in a real-world situation.

Guide 25: Partial Correlation - Practical Example
03:46

How to use the chi square test for association in order to analyze the relationship between categorical variables.

Guide 26: Chi Square For Association
06:36

How to use the chi square test for association when your data are weighted.

Guide 26: Chi Square For Association with Weighted Data
03:54

What is loglinear analysis and when you can use it.

Guide 27: Loglinear Analysis - Introduction
10:19

How to define the optimal parcimonious model in a loglinear analysis.

Guide 27: Loglinear Analysis - Hierarchical Loglinear Analysis
07:30

How to interpret the coefficients of the optimal loglinear model.

Guide 27: Loglinear Analysis - General Loglinear Analysis
12:27
+
Tests For Mean Difference
52 Lectures 04:45:23

What is the independent samples t test and when you should use it.

Preview 04:13

How to check the assumptions of the independent samples t test.

Guide 28: Independent-Sample T Test - Assumption Testing
01:36

How to run the independent samples t test procedure and interpret the results.

Guide 28: Independent-Sample T Test - Results Interpretation
05:09

What is the paired samples t test and when it is useful.

Guide 29: Paired-Sample T Test - Introduction
03:13

How to check the assumptions of the paired samples t test.

Guide 29: Paired-Sample T Test - Assumption Testing
02:50

How to run the paired samples t test procedure and interpret the results.

Guide 29: Paired-Sample T Test - Results Interpretation
02:48

The one-way ANOVA is useful when you want to compare the means of three or more groups.

Guide 30: One-Way ANOVA - Introduction
05:17

How to check the assumptions for the one-way ANOVA.

Guide 30: One-Way ANOVA - Assumption Testing
02:34

How to interpret the F test (or Welch test, if the case) results.

Guide 30: One-Way ANOVA - F Test Results
05:04

How to perform pairwise comparisons for the groups in your population.

Guide 30: One-Way ANOVA - Multiple Comparisons
06:47

What is the two-way ANOVA and when you should use it.

Guide 31: Two-Way ANOVA - Introduction
07:15

How to check the assumptions for the two-way ANOVA.

Guide 31: Two-Way ANOVA - Assumption Testing
04:16

How to interpret the interaction effect in a two-way ANOVA.

Guide 31: Two-Way ANOVA - Interaction Effect
08:46

How to compute and interpret the simple main effects, if the interaction effect is statistically significant.

Guide 31: Two-Way ANOVA - Simple Main Effects
13:14

What is the three-way ANOVA and when it may be necessary to employ it.

Guide 32: Three-Way ANOVA - Introduction
09:04

How to check the assumptions for the three-way ANOVA.

Guide 32: Three-Way ANOVA - Assumption Testing
03:04

How to interpret the third order interaction effect.

Guide 32: Three-Way ANOVA - Third Order Interaction
04:48

How to compute and interpret the simple second order interaction effects (if the third order interaction is significant).

Guide 32: Three-Way ANOVA - Simple Second Order Interaction
03:55

How to compute and interpret the simple main effects (if one or more second order interaction effects are significant).

Guide 32: Three-Way ANOVA - Simple Main Effects
06:26

How to compute and interpret the simple comparisons between means.

Guide 32: Three-Way ANOVA - Simple Comparisons (1)
13:19

How to compute and interpret the simple comparisons between means

(more examples).

Guide 32: Three-Way ANOVA - Simple Comparisons (2)
03:07

What is the multivariate ANOVA and when you should use it.

Preview 04:37

How to check the assumptions for the multivariate ANOVA.

Guide 33: Multivariate ANOVA - Assumption Checking (1)
07:34

How to detect the multivariate outliers in a multivariate ANOVA.

Guide 33: Multivariate ANOVA - Assumption Checking (2)
04:39

How to interpret the results of a multivariate ANOVA.

Guide 33: Multivariate ANOVA - Result Interpretation
09:43

What is the analysis of covariance and when it is useful.

Guide 34: Analysis of Covariance (ANCOVA) - Introduction
05:08

How to check the main assumptions for the analysis of covariance.

Guide 34: Analysis of Covariance (ANCOVA) - Assumption Checking (1)
05:16

Some more assumption checking for ANCOVA. :)

Guide 34: Analysis of Covariance (ANCOVA) - Assumption Checking (2)
07:08

How to interpret the ANCOVA results.

Guide 34: Analysis of Covariance (ANCOVA) - Results Intepretation
03:26

What is the repeated measures ANOVA.

Guide 35: Repeated Measures ANOVA - Introduction
03:32

How to check the assumptions for the repeated measures ANOVA.

Guide 35: Repeated Measures ANOVA - Assumption Checking
01:52

How to interpret the main output of the repeated measures ANOVA.

Guide 35: Repeated Measures ANOVA - Results Interpretation
10:31

What is the within-within subjects ANOVA and when it is useful.

Guide 36: Within-Within Subjects ANOVA - Introduction
03:58

Assumption checking for the within-within subjects ANOVA.

Guide 36: Within-Within Subjects ANOVA - Assumption Checking
06:52

How to interpret the interaction effect in a within-within subjects ANOVA.

Guide 36: Within-Within Subjects ANOVA - Interaction
04:11

How to compute and interpret the simple main effects (when the interaction effect is significant).

Guide 36: Within-Within Subjects ANOVA - Simple Main Effects (1)
07:29

A bit more about the simple main effects in a within-within subjects ANOVA.

Guide 36: Within-Within Subjects ANOVA - Simple Main Effects (2)
05:01

How to continue the analysis if the interaction effect is not significant.

Guide 36: Within-Within Subjects ANOVA - Case of Nonsignificant Interaction
02:49

What is the mixed ANOVA and when you can use it.

Guide 37: Mixed ANOVA - Introduction
03:20

How to check the assumptions for a mixed ANOVA.

Guide 37: Mixed ANOVA - Assumption Checking
02:45

How to interpret the interaction effect in a mixed ANOVA.

Guide 37: Mixed ANOVA - Interaction
08:24

How to compute and interpret the simple main effects (if the interaction is not statistically significant).

Guide 37: Mixed ANOVA - Simple Main Effects (1)
03:50

A bit more about the simple main effects in a mixed ANOVA.

Guide 37: Mixed ANOVA - Simple Main Effects (2)
06:20

How to go on with the analysis if the interaction effect is not significant.

Guide 37: Mixed ANOVA - Case of Nonsignificant Interaction
01:39

What is the non-parametric Mann-Whitney test (for independent samples).

Guide 38: Mann-Whitney Test - Introduction
04:04

How to interpret the results of the Mann-Whitney test.

Guide 38: Mann-Whitney Test - Results Interpretation
06:58

How to perform the Wilcoxon test (for paired samples) and how to interpret its results.

Guide 39: Wilcoxon and Sign Tests - Wilcoxon Test
08:02

How to perform the sign test (for paired samples) and interpret the results.

Guide 39: Wilcoxon and Sign Tests - Sign Test
02:52

How to perform the Kruskal-Wallis test for comparing the median of three or more groups.

Guide 40: Kruskal-Wallis and Median Tests - Kruskal-Wallis Test
08:29

How to run a median test to compare the medians of three or more groups.

Guide 40: Kruskal-Wallis and Median Tests - Median Test
03:57

How to compute and interpret the non-parametric Friedman test (for multiple measurements).

Guide 41: Friedman Test
05:59

How to run and the McNemar test and interpret the results.

Guide 42: McNemar Test
08:13
+
Predictive Techniques
28 Lectures 02:48:41

What is the simple linear regression and what it does.

Preview 04:29

How to check the assumptions for the simple linear regression.

Guide 43: Simple Regression - Assumption Checking (1)
02:15

How to check the assumptions for the simple linear regression (part 2).

Guide 43: Simple Regression - Assumption Checking (2)
07:31

How to get and interpret the results of a simple linear regression.

Guide 43: Simple Regression - Results Interpretation
05:04

What is the multiple linear linear regression and when you need it.

Guide 44: Multiple Regression - Introduction
02:55

How to check the assumptions for the multiple linear regression.

Guide 44: Multiple Regression - Assumption Checking
12:20

How to interpret the output of a multiple linear regression.

Guide 44: Multiple Regression - Results Interpretation
05:01

How to run a regression with dummy variables and how to interpret the coefficient of a dummy variable.

Guide 45: Regression with Dummy Variables
07:13

How to run a sequential or hierarchical regression (where the variables are introduced in the equation not all at once, but by blocks).

Guide 46: Sequential Regression
08:48

What is the binomial regression and what are its peculiarities.

Guide 47: Binomial Regression - Introduction
05:16

How to check the assumption of a binomial regression.

Guide 47: Binomial Regression - Assumption Checking
02:44

How to interpret the goodness-of-fit indicators of a binomial regression.

Guide 47: Binomial Regression - Goodness-of-Fit Indicators
08:43

How to interpret the coefficients of the categorical predictors in a binomial regression.

Guide 47: Binomial Regression - Coefficient Interpretation (1)
03:59

How to interpret the coefficients of the continuous predictors in a binomial regression.

Guide 47: Binomial Regression - Coefficient Interpretation (2)
04:03

How to read and interpret the classification table for a binomial regression.

Guide 47: Binomial Regression - Classification Table
03:42

What is the multinomial regression and when it is useful.

Guide 48: Multinomial Regression - Introduction
03:41

How to check the assumptions for a multinomial regression.

Guide 48: Multinomial Regression - Assumption Checking
10:53

How to interpret the goodness-of-fit indicators for a multinomial regression.

Guide 48: Multinomial Regression - Goodness-of-Fit Indicators
05:24

How to interpret the coefficients of a multiple regression.

Guide 48: Multinomial Regression - Coefficient Interpretation (1)
11:27

More about coefficient interpretation in a logistic regression.

Guide 48: Multinomial Regression - Coefficient Interpretation (2)
06:25

...and a bit more about coefficient interpretation (just to make sure you understood everything right).

Guide 48: Multinomial Regression - Coefficient Interpretation (3)
07:20

How to interpret the classification table for a multinomial regression.

Guide 48: Multinomial Regression - Classification Table
03:11

What is the ordinal regression and when you can (and can not) use it.

Guide 49: Ordinal Regression - Introduction
07:59

How to check the assumptions of an ordinal regression.

Guide 49: Ordinal Regression - Assumption Testing
06:55

How to interpret the goodness-of-fit indicators for an ordinal regression.

Guide 49: Ordinal Regression - Goodness-of-Fit Indicators
05:03

How to interpret the coefficients of the categorical predictors in a logistic regression.

Guide 49: Ordinal Regression - Coefficient Interpretation (1)
11:19

How to interpret the coefficients of the continuous predictors of an ordinal regression model.

Guide 49: Ordinal Regression - Coefficient Interpretation (2)
01:26

How to create and interpret the classification table for an ordinal regression.

Guide 49: Ordinal Regression - Classification Table
03:35
+
Scaling Techniques
8 Lectures 46:07

How to compute and interpret the Cronbach's alpha in order to assess the internal consistency of your scales.

Guide 50: Reliability Analysis - Cronbach's Alpha
08:04

Computing the Cohen's kappa to assess the concordance of scores for two raters.

Guide 50: Reliability Analysis - Cohen's Kappa
06:05

Computing and interpreting the Kendall's W to assess the concordance of scores for two or more raters.

Guide 50: Reliability Analysis - Kendall's W
04:04

What is multidimensional scaling and when it is used.

Preview 04:51

Running the ALSCAL procedures when data are not distances between cases.

Guide 51: Multidimensional Scaling - ALSCAL procedure (1)
08:32

Running the ALSCAL procedure when data are distances between cases.

Guide 51: Multidimensional Scaling - ALSCAL procedure (2)
05:29

Running the PROXSCAL procedure when data are not distances between cases.

Guide 51: Multidimensional Scaling - PROXSCAL procedure (1)
04:28

Running the PROXSCAL procedure when data are distances between cases.

Guide 51: Multidimensional Scaling - PROXSCAL procedure (2)
04:34
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