Udemy_SPSS_For_Research

با این دوره در مدت زمان بسیار کوتاهی شما تمام مهارت های ضروری یک تحلیلگر داده  SPSS از ساده ترین عملیات با داده ها تا تکنیک های چند متغیره پیشرفته ای مانند  logistic regression، مقیاس گذاری چند بعدی و یا تجزیه و تحلیل مولفه های اصلی را می آموزید.

این دوره آموزشی محصول موسسه Udemy است.

سرفصل های این دوره:

  • مقدمه
  • راهنمای 1: کار کردن با فایل های SPSS
  • راهنمای 2: تعریف متغیرها
  • راهنمای 3: ضبط متغیر
  • راهنمای 4: متغیر ساختگی
  • راهنمای 5: انتخاب موارد
  • راهنمای 6: تقسیم فایل
  • راهنمای 7: توزین داده  ها
  • ایجاد نمودار در نرم افزار SPSS
  • راهنمای 8: نمودار ستونی
  • راهنمای 9: نمودارها خطی
  • راهنمای 10: نمودارهای پراکندگی
  • راهنمای 11: نمودارهای Boxplot
  • تکنیک های تجزیه و تحلیل ساده
  • راهنمای 12: روش فرکانس
  • راهنمای 13: روش Descriptives
  • راهنمای 14: کاوش روش
  • راهنمای 15: معنای روش
  • راهنمای 16: روش Cross tabs
  • تحولات داده
  • راهنمای 17: چک کردن برای نرمالیته – روش های عددی
  • راهنمای 17: روش گرافیکی
  • راهنمای 18: تشخیص Outlier ها  – روش گرافیکی
  • راهنمای 18: روش های عددی
  • راهنمای 19: تحولات داده
  • تست یک نمونه
  • راهنمای 20: آزمون t تک نمونه – مقدمه
  • راهنمای 20: آزمون t تک نمونه – اجرای روش
  • راهنمای 21: تست دو جمله ای
  • راهنمای 21: تست دو جمله ای با داده وزن دار
  • راهنمای 22: Chi Square for Goodness-of-Fit
  • تست انجمن
  • راهنمای 23: همبستگی Pearson – مقدمه
  • راهنمای 23: همبستگی Pearson – فرض بررسی
  • راهنمای 23: همبستگی Pearson – اجرای روش
  • راهنمای 24: همبستگی Spearman – مقدمه
  • راهنمای 24: همبستگی Spearman – اجرای روش
  • راهنمای 25: همبستگی Partial – مقدمه
  • راهنمای 25: همبستگی Partial – مثال کاربردی
  • راهنمای 26: Chi Square برای انجمن
  • راهنمای 26: Chi Square برای ارتباط با داده وزن دار
  • راهنمای 27: تجزیه و تحلیل Loglinear – مقدمه
  • راهنمای 27: تجزیه و تحلیل Loglinear – تجزیه و تحلیل سلسله مراتب Loglinear
  • راهنمای 27: لگار تحلیل – تجزیه و تحلیل عمومی Loglinear
  • آزمایشاتی که برای تغییر میانگین
  • تکنیک های پیش بینی شده
  • مقیاس گذاری تکنیک ها

عنوان دوره: Udemy SPSS For Research

نویسنده:  Bogdan Anastasiei


توضیحات:

Udemy SPSS For Research
SPSS data analysis made easy. Become an expert in advanced statistical analysis with SPSS.
Author: Bogdan Anastasiei
Link: https://www.udemy.com/spss-for-research/


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
academic researchers
business researchers
University teachers
anyone looking for a job in the statistical analysis field
anyone who is passionate about quantitative research
Curriculum For This Course
 Expand All 149 Lectures
Collapse All 149 Lectures
 14:03:29
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Getting Started
 2 Lectures
 09:45
What's it all about - why you should take this course.
 Introduction
 04:54
See the detailed structure of this course here.
 Course Outline
 04:51
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The Basics
 7 Lectures
 53:22
How to create a file and open an existing file in SPSS.
 Guide 1: Working With SPSS Files
 02:41
How to create variables and set variable properties.
 Guide 2: Defining Variables
 12:08
Learn when you need to recode your variables and how to do it.
 Guide 3: Variable Recoding
 09:29
How to convert dichotomous and multinomial variables into dummy variables.
 Guide 4: Dummy Variables
 07:52
How to filter out cases in an SPSS data set.
 Guide 5: Selecting Cases
 07:11
How to split file using certain criteria in order to perform analyses on groups or strata of the population.
 Guide 6: File Splitting
 02:52
Know when it is necessary to weigh your cases and how to perform this operation.
 Guide 7: Data Weighting
 11:09
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Creating Charts in SPSS
 4 Lectures
 19:46
 Learn how to build column charts in SPSS.
 Guide 8: Column Charts
 06:41
Learn how to build and interpret line charts.
 Guide 9: Line Charts
 04:35
How to use the Chart Builder in order to create simple and grouped scatterplot charts.
 Guide 10: Scatterplot Charts
 04:06
How to build and interpret boxplot charts (simple and grouped).
 Guide 11: Boxplot Diagrams
 04:24
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Simple Analysis Techniques
 5 Lectures
 19:36
How to use the Frequencies procedure to build frequency tables and to generate statistical indicators.
 Guide 12: Frequencies Procedure
 05:47
How to generate the essential statistics for continuous variables.
 Guide 13: Descriptives Procedure
 01:56
The Explore procedure helps you generate statistical indicators by groups or strata, create graphs and run normality tests.
 Guide 14: Explore Procedure
 05:09
Another quick and easy procedure to compute the statistics for a continuous variable.
 Guide 15: Means Procedure
 03:23
How to build cross tables to visualize the relationship between categorical variables.
 Guide 16: Crosstabs Procedure
 03:21
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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
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One-Sample Tests
 6 Lectures
 24:24
When and why to use the one-sample t test.
 Guide 20: One-Sample T Test - Introduction
 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
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Association Tests
 12 Lectures
 01:09:17
When and how to use the Pearson correlation coefficient.
 Guide 23: Pearson Correlation - Introduction
 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
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Tests For Mean Difference
 52 Lectures
 04:45:23
What is the independent samples t test and when you should use it.
 Guide 28: Independent-Sample T Test - Introduction
 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.
 Guide 33: Multivariate ANOVA - Introduction
 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
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Predictive Techniques
 28 Lectures
 02:48:41
What is the simple linear regression and what it does.
 Guide 43: Simple Regression - Introduction
 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
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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.
 Guide 51: Multidimensional Scaling - Introduction
 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
 4 More Sections