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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:
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?
How to create a file and open an existing file in SPSS.
How to create variables and set variable properties.
Learn when you need to recode your variables and how to do it.
How to convert dichotomous and multinomial variables into dummy variables.
How to filter out cases in an SPSS data set.
How to split file using certain criteria in order to perform analyses on groups or strata of the population.
Know when it is necessary to weigh your cases and how to perform this operation.
How to use the Frequencies procedure to build frequency tables and to generate statistical indicators.
How to generate the essential statistics for continuous variables.
The Explore procedure helps you generate statistical indicators by groups or strata, create graphs and run normality tests.
Another quick and easy procedure to compute the statistics for a continuous variable.
How to build cross tables to visualize the relationship between categorical variables.
How to compute and interpret the statistical tests for normality.
How to use charts in order to assess normality.
How to handle the non normal distributions (which are not uncommon).
How to use the boxplot diagram in order to check for outliers in your data.
How to detect the outliers with the help of the standardized scores.
What to do if you have extreme values in your data series.
How to transform your variables in an attempt to get normal distributions (unfortunately, often this is not possible).
When and why to use the one-sample t test.
How to perform the one-sample t test and interpret the results.
How to perform the binomial test in order to analyze the dichotomous variables.
How to use the binomial test when your data are weighted.
The chi square test for goodness-of-fit is very useful when you study the categorical variables with more than two groups.
How to perform the chi square test for goodness-of-fit when your data are weighted.
When and how to use the Pearson correlation coefficient.
How to check the assumptions of the Pearson correlation procedure.
How to compute and interpret the Pearson correlation coefficient.
When and why you should use the Spearman correlation.
How to compute the Spearman correlation coefficient and interpret it.
What is partial correlation? The four scenarios for analyzing the partial correlation coefficient.
How to compute and interpret the partial correlation coefficient in a real-world situation.
How to use the chi square test for association in order to analyze the relationship between categorical variables.
How to use the chi square test for association when your data are weighted.
What is loglinear analysis and when you can use it.
How to define the optimal parcimonious model in a loglinear analysis.
How to interpret the coefficients of the optimal loglinear model.
What is the independent samples t test and when you should use it.
How to check the assumptions of the independent samples t test.
How to run the independent samples t test procedure and interpret the results.
What is the paired samples t test and when it is useful.
How to check the assumptions of the paired samples t test.
How to run the paired samples t test procedure and interpret the results.
The one-way ANOVA is useful when you want to compare the means of three or more groups.
How to check the assumptions for the one-way ANOVA.
How to interpret the F test (or Welch test, if the case) results.
How to perform pairwise comparisons for the groups in your population.
What is the two-way ANOVA and when you should use it.
How to check the assumptions for the two-way ANOVA.
How to interpret the interaction effect in a two-way ANOVA.
How to compute and interpret the simple main effects, if the interaction effect is statistically significant.
What is the three-way ANOVA and when it may be necessary to employ it.
How to check the assumptions for the three-way ANOVA.
How to interpret the third order interaction effect.
How to compute and interpret the simple second order interaction effects (if the third order interaction is significant).
How to compute and interpret the simple main effects (if one or more second order interaction effects are significant).
How to compute and interpret the simple comparisons between means.
(more examples).
What is the multivariate ANOVA and when you should use it.
How to check the assumptions for the multivariate ANOVA.
How to detect the multivariate outliers in a multivariate ANOVA.
How to interpret the results of a multivariate ANOVA.
What is the analysis of covariance and when it is useful.
How to check the main assumptions for the analysis of covariance.
Some more assumption checking for ANCOVA. :)
How to interpret the ANCOVA results.
What is the repeated measures ANOVA.
How to check the assumptions for the repeated measures ANOVA.
How to interpret the main output of the repeated measures ANOVA.
What is the within-within subjects ANOVA and when it is useful.
Assumption checking for the within-within subjects ANOVA.
How to interpret the interaction effect in a within-within subjects ANOVA.
How to compute and interpret the simple main effects (when the interaction effect is significant).
A bit more about the simple main effects in a within-within subjects ANOVA.
How to continue the analysis if the interaction effect is not significant.
What is the mixed ANOVA and when you can use it.
How to check the assumptions for a mixed ANOVA.
How to interpret the interaction effect in a mixed ANOVA.
How to compute and interpret the simple main effects (if the interaction is not statistically significant).
A bit more about the simple main effects in a mixed ANOVA.
How to go on with the analysis if the interaction effect is not significant.
What is the non-parametric Mann-Whitney test (for independent samples).
How to interpret the results of the Mann-Whitney test.
How to perform the Wilcoxon test (for paired samples) and how to interpret its results.
How to perform the sign test (for paired samples) and interpret the results.
How to perform the Kruskal-Wallis test for comparing the median of three or more groups.
How to run a median test to compare the medians of three or more groups.
How to compute and interpret the non-parametric Friedman test (for multiple measurements).
How to run and the McNemar test and interpret the results.
What is the simple linear regression and what it does.
How to check the assumptions for the simple linear regression.
How to check the assumptions for the simple linear regression (part 2).
How to get and interpret the results of a simple linear regression.
What is the multiple linear linear regression and when you need it.
How to check the assumptions for the multiple linear regression.
How to interpret the output of a multiple linear regression.
How to run a regression with dummy variables and how to interpret the coefficient of a dummy variable.
How to run a sequential or hierarchical regression (where the variables are introduced in the equation not all at once, but by blocks).
What is the binomial regression and what are its peculiarities.
How to check the assumption of a binomial regression.
How to interpret the goodness-of-fit indicators of a binomial regression.
How to interpret the coefficients of the categorical predictors in a binomial regression.
How to interpret the coefficients of the continuous predictors in a binomial regression.
How to read and interpret the classification table for a binomial regression.
What is the multinomial regression and when it is useful.
How to check the assumptions for a multinomial regression.
How to interpret the goodness-of-fit indicators for a multinomial regression.
How to interpret the coefficients of a multiple regression.
More about coefficient interpretation in a logistic regression.
...and a bit more about coefficient interpretation (just to make sure you understood everything right).
How to interpret the classification table for a multinomial regression.
What is the ordinal regression and when you can (and can not) use it.
How to check the assumptions of an ordinal regression.
How to interpret the goodness-of-fit indicators for an ordinal regression.
How to interpret the coefficients of the categorical predictors in a logistic regression.
How to interpret the coefficients of the continuous predictors of an ordinal regression model.
How to create and interpret the classification table for an ordinal regression.
How to compute and interpret the Cronbach's alpha in order to assess the internal consistency of your scales.
Computing the Cohen's kappa to assess the concordance of scores for two raters.
Computing and interpreting the Kendall's W to assess the concordance of scores for two or more raters.
What is multidimensional scaling and when it is used.
Running the ALSCAL procedures when data are not distances between cases.
Running the ALSCAL procedure when data are distances between cases.
Running the PROXSCAL procedure when data are not distances between cases.
Running the PROXSCAL procedure when data are distances between cases.
My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have about 20 years experience in teaching and about 10 years experience in business consulting.