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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 â€“ 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 â€“ 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?
Not for you? No problem.
30 day money back guarantee.
Forever yours.
Lifetime access.
Learn on the go.
Desktop, iOS and Android.
Get rewarded.
Certificate of completion.
Section 1: Getting Started | |||
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Lecture 1 | 04:54 | ||
What's it all about - why you should take this course. |
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Lecture 2 | 04:51 | ||
See the detailed structure of this course here. |
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Section 2: The Basics | |||
Lecture 3 | 02:41 | ||
How to create a file and open an existing file in SPSS. |
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Lecture 4 | 12:08 | ||
How to create variables and set variable properties. |
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Lecture 5 | 09:29 | ||
Learn when you need to recode your variables and how to do it. |
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Lecture 6 | 07:52 | ||
How to convert dichotomous and multinomial variables into dummy variables. |
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Lecture 7 | 07:11 | ||
How to filter out cases in an SPSS data set. |
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Lecture 8 | 02:52 | ||
How to split file using certain criteria in order to perform analyses on groups or strata of the population. |
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Lecture 9 | 11:09 | ||
Know when it is necessary to weigh your cases and how to perform this operation. |
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Section 3: Creating Charts in SPSS | |||
Lecture 10 | 06:41 | ||
Learn how to build column charts in SPSS. |
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Lecture 11 | 04:35 | ||
Learn how to build and interpret line charts. |
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Lecture 12 | 04:06 | ||
How to use the Chart Builder in order to create simple and grouped scatterplot charts. |
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Lecture 13 | 04:24 | ||
How to build and interpret boxplot charts (simple and grouped). |
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Section 4: Simple Analysis Techniques | |||
Lecture 14 | 05:47 | ||
How to use the Frequencies procedure to build frequency tables and to generate statistical indicators. |
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Lecture 15 | 01:56 | ||
How to generate the essential statistics for continuous variables. |
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Lecture 16 | 05:09 | ||
The Explore procedure helps you generate statistical indicators by groups or strata, create graphs and run normality tests. |
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Lecture 17 | 03:23 | ||
Another quick and easy procedure to compute the statistics for a continuous variable. |
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Lecture 18 | 03:21 | ||
How to build cross tables to visualize the relationship between categorical variables. |
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Section 5: Assumption Checking. Data Transformations | |||
Lecture 19 | 06:34 | ||
How to compute and interpret the statistical tests for normality. |
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Lecture 20 | 03:33 | ||
How to use charts in order to assess normality. |
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Lecture 21 | 02:08 | ||
How to handle the non normal distributions (which are not uncommon). |
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Lecture 22 | 03:38 | ||
How to use the boxplot diagram in order to check for outliers in your data. |
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Lecture 23 | 03:30 | ||
How to detect the outliers with the help of the standardized scores. |
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Lecture 24 | 03:12 | ||
What to do if you have extreme values in your data series. |
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Lecture 25 | 08:52 | ||
How to transform your variables in an attempt to get normal distributions (unfortunately, often this is not possible). |
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Section 6: One-Sample Tests | |||
Lecture 26 | 04:08 | ||
When and why to use the one-sample t test. |
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Lecture 27 | 03:17 | ||
How to perform the one-sample t test and interpret the results. |
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Lecture 28 | 04:51 | ||
How to perform the binomial test in order to analyze the dichotomous variables. |
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Lecture 29 | 03:45 | ||
How to use the binomial test when your data are weighted. |
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Lecture 30 | 05:40 | ||
The chi square test for goodness-of-fit is very useful when you study the categorical variables with more than two groups. |
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Lecture 31 | 02:43 | ||
How to perform the chi square test for goodness-of-fit when your data are weighted. |
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Section 7: Association Tests | |||
Lecture 32 | 03:56 | ||
When and how to use the Pearson correlation coefficient. |
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Lecture 33 | 03:58 | ||
How to check the assumptions of the Pearson correlation procedure. |
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Lecture 34 | 03:23 | ||
How to compute and interpret the Pearson correlation coefficient. |
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Lecture 35 | 05:12 | ||
When and why you should use the Spearman correlation. |
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Lecture 36 | 02:43 | ||
How to compute the Spearman correlation coefficient and interpret it. |
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Lecture 37 | 05:33 | ||
What is partial correlation? The four scenarios for analyzing the partial correlation coefficient. |
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Lecture 38 | 03:46 | ||
How to compute and interpret the partial correlation coefficient in a real-world situation. |
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Lecture 39 | 06:36 | ||
How to use the chi square test for association in order to analyze the relationship between categorical variables. |
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Lecture 40 | 03:54 | ||
How to use the chi square test for association when your data are weighted. |
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Lecture 41 | 10:19 | ||
What is loglinear analysis and when you can use it. |
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Lecture 42 | 07:30 | ||
How to define the optimal parcimonious model in a loglinear analysis. |
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Lecture 43 | 12:27 | ||
How to interpret the coefficients of the optimal loglinear model. |
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Section 8: Tests For Mean Difference | |||
Lecture 44 | 04:13 | ||
What is the independent samples t test and when you should use it. |
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Lecture 45 | 01:36 | ||
How to check the assumptions of the independent samples t test. |
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Lecture 46 | 05:09 | ||
How to run the independent samples t test procedure and interpret the results. |
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Lecture 47 | 03:13 | ||
What is the paired samples t test and when it is useful. |
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Lecture 48 | 02:50 | ||
How to check the assumptions of the paired samples t test. |
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Lecture 49 | 02:48 | ||
How to run the paired samples t test procedure and interpret the results. |
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Lecture 50 | 05:17 | ||
The one-way ANOVA is useful when you want to compare the means of three or more groups. |
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Lecture 51 | 02:34 | ||
How to check the assumptions for the one-way ANOVA. |
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Lecture 52 | 05:04 | ||
How to interpret the F test (or Welch test, if the case) results. |
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Lecture 53 | 06:47 | ||
How to perform pairwise comparisons for the groups in your population. |
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Lecture 54 | 07:15 | ||
What is the two-way ANOVA and when you should use it. |
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Lecture 55 | 04:16 | ||
How to check the assumptions for the two-way ANOVA. |
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Lecture 56 | 08:46 | ||
How to interpret the interaction effect in a two-way ANOVA. |
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Lecture 57 | 13:14 | ||
How to compute and interpret the simple main effects, if the interaction effect is statistically significant. |
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Lecture 58 | 09:04 | ||
What is the three-way ANOVA and when it may be necessary to employ it. |
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Lecture 59 | 03:04 | ||
How to check the assumptions for the three-way ANOVA. |
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Lecture 60 | 04:48 | ||
How to interpret the third order interaction effect. |
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Lecture 61 | 03:55 | ||
How to compute and interpret the simple second order interaction effects (if the third order interaction is significant). |
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Lecture 62 | 06:26 | ||
How to compute and interpret the simple main effects (if one or more second order interaction effects are significant). |
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Lecture 63 | 13:19 | ||
How to compute and interpret the simple comparisons between means. |
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Lecture 64 | 03:07 | ||
How to compute and interpret the simple comparisons between means (more examples). |
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Lecture 65 | 04:37 | ||
What is the multivariate ANOVA and when you should use it. |
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Lecture 66 | 07:34 | ||
How to check the assumptions for the multivariate ANOVA. |
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Lecture 67 | 04:39 | ||
How to detect the multivariate outliers in a multivariate ANOVA. |
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Lecture 68 | 09:43 | ||
How to interpret the results of a multivariate ANOVA. |
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Lecture 69 | 05:08 | ||
What is the analysis of covariance and when it is useful. |
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Lecture 70 | 05:16 | ||
How to check the main assumptions for the analysis of covariance. |
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Lecture 71 | 07:08 | ||
Some more assumption checking for ANCOVA. :) |
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Lecture 72 | 03:26 | ||
How to interpret the ANCOVA results. |
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Lecture 73 | 03:32 | ||
What is the repeated measures ANOVA. |
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Lecture 74 | 01:52 | ||
How to check the assumptions for the repeated measures ANOVA. |
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Lecture 75 | 10:31 | ||
How to interpret the main output of the repeated measures ANOVA. |
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Lecture 76 | 03:58 | ||
What is the within-within subjects ANOVA and when it is useful. |
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Lecture 77 | 06:52 | ||
Assumption checking for the within-within subjects ANOVA. |
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Lecture 78 | 04:11 | ||
How to interpret the interaction effect in a within-within subjects ANOVA. |
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Lecture 79 | 07:29 | ||
How to compute and interpret the simple main effects (when the interaction effect is significant). |
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Lecture 80 | 05:01 | ||
A bit more about the simple main effects in a within-within subjects ANOVA. |
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Lecture 81 | 02:49 | ||
How to continue the analysis if the interaction effect is not significant. |
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Lecture 82 | 03:20 | ||
What is the mixed ANOVA and when you can use it. |
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Lecture 83 | 02:45 | ||
How to check the assumptions for a mixed ANOVA. |
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Lecture 84 | 08:24 | ||
How to interpret the interaction effect in a mixed ANOVA. |
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Lecture 85 | 03:50 | ||
How to compute and interpret the simple main effects (if the interaction is not statistically significant). |
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Lecture 86 | 06:20 | ||
A bit more about the simple main effects in a mixed ANOVA. |
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Lecture 87 | 01:39 | ||
How to go on with the analysis if the interaction effect is not significant. |
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Lecture 88 | 04:04 | ||
What is the non-parametric Mann-Whitney test (for independent samples). |
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Lecture 89 | 06:58 | ||
How to interpret the results of the Mann-Whitney test. |
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Lecture 90 | 08:02 | ||
How to perform the Wilcoxon test (for paired samples) and how to interpret its results. |
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Lecture 91 | 02:52 | ||
How to perform the sign test (for paired samples) and interpret the results. |
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Lecture 92 | 08:29 | ||
How to perform the Kruskal-Wallis test for comparing the median of three or more groups. |
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.