SPSS For Research

SPSS data analysis made easy. Become an expert in advanced statistical analysis with SPSS.
SPSS For Research
File Size :
10.48 GB
Total length :
14h 3m

Category

Instructor

Bogdan Anastasiei

Language

Last update

Last updated 6/2015

Ratings

4.4/5

SPSS For Research

What you’ll 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)

SPSS For Research

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?

Overview

Section 1: Getting Started

Lecture 1 Introduction

Lecture 2 Course Outline

Section 2: The Basics

Lecture 3 Guide 1: Working With SPSS Files

Lecture 4 Guide 2: Defining Variables

Lecture 5 Guide 3: Variable Recoding

Lecture 6 Guide 4: Dummy Variables

Lecture 7 Guide 5: Selecting Cases

Lecture 8 Guide 6: File Splitting

Lecture 9 Guide 7: Data Weighting

Section 3: Creating Charts in SPSS

Lecture 10 Guide 8: Column Charts

Lecture 11 Guide 9: Line Charts

Lecture 12 Guide 10: Scatterplot Charts

Lecture 13 Guide 11: Boxplot Diagrams

Section 4: Simple Analysis Techniques

Lecture 14 Guide 12: Frequencies Procedure

Lecture 15 Guide 13: Descriptives Procedure

Lecture 16 Guide 14: Explore Procedure

Lecture 17 Guide 15: Means Procedure

Lecture 18 Guide 16: Crosstabs Procedure

Section 5: Assumption Checking. Data Transformations

Lecture 19 Guide 17: Checking for Normality – Numerical Methods

Lecture 20 Guide 17: Checking for Normality – Graphical Methods

Lecture 21 Guide 17: Checking for Normality – What to Do If We Do Not Have Normality?

Lecture 22 Guide 18: Detecting Outliers – Graphical Methods

Lecture 23 Guide 18: Detecting Outliers – Numerical Methods

Lecture 24 Guide 18: Detecting Outliers – How to Handle the Outliers

Lecture 25 Guide 19: Data Transformations

Section 6: One-Sample Tests

Lecture 26 Guide 20: One-Sample T Test – Introduction

Lecture 27 Guide 20: One-Sample T Test – Running the Procedure

Lecture 28 Guide 21: Binomial Test

Lecture 29 Guide 21: Binomial Test with Weighted Data

Lecture 30 Guide 22: Chi Square for Goodness-of-Fit

Lecture 31 Guide 22: Chi Square for Goodness-of-Fit with Weighted Data

Section 7: Association Tests

Lecture 32 Guide 23: Pearson Correlation – Introduction

Lecture 33 Guide 23: Pearson Correlation – Assumption Checking

Lecture 34 Guide 23: Pearson Correlation – Running the Procedure

Lecture 35 Guide 24: Spearman Correlation – Introduction

Lecture 36 Guide 24: Spearman Correlation – Running the Procedure

Lecture 37 Guide 25: Partial Correlation – Introduction

Lecture 38 Guide 25: Partial Correlation – Practical Example

Lecture 39 Guide 26: Chi Square For Association

Lecture 40 Guide 26: Chi Square For Association with Weighted Data

Lecture 41 Guide 27: Loglinear Analysis – Introduction

Lecture 42 Guide 27: Loglinear Analysis – Hierarchical Loglinear Analysis

Lecture 43 Guide 27: Loglinear Analysis – General Loglinear Analysis

Section 8: Tests For Mean Difference

Lecture 44 Guide 28: Independent-Sample T Test – Introduction

Lecture 45 Guide 28: Independent-Sample T Test – Assumption Testing

Lecture 46 Guide 28: Independent-Sample T Test – Results Interpretation

Lecture 47 Guide 29: Paired-Sample T Test – Introduction

Lecture 48 Guide 29: Paired-Sample T Test – Assumption Testing

Lecture 49 Guide 29: Paired-Sample T Test – Results Interpretation

Lecture 50 Guide 30: One-Way ANOVA – Introduction

Lecture 51 Guide 30: One-Way ANOVA – Assumption Testing

Lecture 52 Guide 30: One-Way ANOVA – F Test Results

Lecture 53 Guide 30: One-Way ANOVA – Multiple Comparisons

Lecture 54 Guide 31: Two-Way ANOVA – Introduction

Lecture 55 Guide 31: Two-Way ANOVA – Assumption Testing

Lecture 56 Guide 31: Two-Way ANOVA – Interaction Effect

Lecture 57 Guide 31: Two-Way ANOVA – Simple Main Effects

Lecture 58 Guide 32: Three-Way ANOVA – Introduction

Lecture 59 Guide 32: Three-Way ANOVA – Assumption Testing

Lecture 60 Guide 32: Three-Way ANOVA – Third Order Interaction

Lecture 61 Guide 32: Three-Way ANOVA – Simple Second Order Interaction

Lecture 62 Guide 32: Three-Way ANOVA – Simple Main Effects

Lecture 63 Guide 32: Three-Way ANOVA – Simple Comparisons (1)

Lecture 64 Guide 32: Three-Way ANOVA – Simple Comparisons (2)

Lecture 65 Guide 33: Multivariate ANOVA – Introduction

Lecture 66 Guide 33: Multivariate ANOVA – Assumption Checking (1)

Lecture 67 Guide 33: Multivariate ANOVA – Assumption Checking (2)

Lecture 68 Guide 33: Multivariate ANOVA – Result Interpretation

Lecture 69 Guide 34: Analysis of Covariance (ANCOVA) – Introduction

Lecture 70 Guide 34: Analysis of Covariance (ANCOVA) – Assumption Checking (1)

Lecture 71 Guide 34: Analysis of Covariance (ANCOVA) – Assumption Checking (2)

Lecture 72 Guide 34: Analysis of Covariance (ANCOVA) – Results Intepretation

Lecture 73 Guide 35: Repeated Measures ANOVA – Introduction

Lecture 74 Guide 35: Repeated Measures ANOVA – Assumption Checking

Lecture 75 Guide 35: Repeated Measures ANOVA – Results Interpretation

Lecture 76 Guide 36: Within-Within Subjects ANOVA – Introduction

Lecture 77 Guide 36: Within-Within Subjects ANOVA – Assumption Checking

Lecture 78 Guide 36: Within-Within Subjects ANOVA – Interaction

Lecture 79 Guide 36: Within-Within Subjects ANOVA – Simple Main Effects (1)

Lecture 80 Guide 36: Within-Within Subjects ANOVA – Simple Main Effects (2)

Lecture 81 Guide 36: Within-Within Subjects ANOVA – Case of Nonsignificant Interaction

Lecture 82 Guide 37: Mixed ANOVA – Introduction

Lecture 83 Guide 37: Mixed ANOVA – Assumption Checking

Lecture 84 Guide 37: Mixed ANOVA – Interaction

Lecture 85 Guide 37: Mixed ANOVA – Simple Main Effects (1)

Lecture 86 Guide 37: Mixed ANOVA – Simple Main Effects (2)

Lecture 87 Guide 37: Mixed ANOVA – Case of Nonsignificant Interaction

Lecture 88 Guide 38: Mann-Whitney Test – Introduction

Lecture 89 Guide 38: Mann-Whitney Test – Results Interpretation

Lecture 90 Guide 39: Wilcoxon and Sign Tests – Wilcoxon Test

Lecture 91 Guide 39: Wilcoxon and Sign Tests – Sign Test

Lecture 92 Guide 40: Kruskal-Wallis and Median Tests – Kruskal-Wallis Test

Lecture 93 Guide 40: Kruskal-Wallis and Median Tests – Median Test

Lecture 94 Guide 41: Friedman Test

Lecture 95 Guide 42: McNemar Test

Section 9: Predictive Techniques

Lecture 96 Guide 43: Simple Regression – Introduction

Lecture 97 Guide 43: Simple Regression – Assumption Checking (1)

Lecture 98 Guide 43: Simple Regression – Assumption Checking (2)

Lecture 99 Guide 43: Simple Regression – Results Interpretation

Lecture 100 Guide 44: Multiple Regression – Introduction

Lecture 101 Guide 44: Multiple Regression – Assumption Checking

Lecture 102 Guide 44: Multiple Regression – Results Interpretation

Lecture 103 Guide 45: Regression with Dummy Variables

Lecture 104 Guide 46: Sequential Regression

Lecture 105 Guide 47: Binomial Regression – Introduction

Lecture 106 Guide 47: Binomial Regression – Assumption Checking

Lecture 107 Guide 47: Binomial Regression – Goodness-of-Fit Indicators

Lecture 108 Guide 47: Binomial Regression – Coefficient Interpretation (1)

Lecture 109 Guide 47: Binomial Regression – Coefficient Interpretation (2)

Lecture 110 Guide 47: Binomial Regression – Classification Table

Lecture 111 Guide 48: Multinomial Regression – Introduction

Lecture 112 Guide 48: Multinomial Regression – Assumption Checking

Lecture 113 Guide 48: Multinomial Regression – Goodness-of-Fit Indicators

Lecture 114 Guide 48: Multinomial Regression – Coefficient Interpretation (1)

Lecture 115 Guide 48: Multinomial Regression – Coefficient Interpretation (2)

Lecture 116 Guide 48: Multinomial Regression – Coefficient Interpretation (3)

Lecture 117 Guide 48: Multinomial Regression – Classification Table

Lecture 118 Guide 49: Ordinal Regression – Introduction

Lecture 119 Guide 49: Ordinal Regression – Assumption Testing

Lecture 120 Guide 49: Ordinal Regression – Goodness-of-Fit Indicators

Lecture 121 Guide 49: Ordinal Regression – Coefficient Interpretation (1)

Lecture 122 Guide 49: Ordinal Regression – Coefficient Interpretation (2)

Lecture 123 Guide 49: Ordinal Regression – Classification Table

Section 10: Scaling Techniques

Lecture 124 Guide 50: Reliability Analysis – Cronbach’s Alpha

Lecture 125 Guide 50: Reliability Analysis – Cohen’s Kappa

Lecture 126 Guide 50: Reliability Analysis – Kendall’s W

Lecture 127 Guide 51: Multidimensional Scaling – Introduction

Lecture 128 Guide 51: Multidimensional Scaling – ALSCAL procedure (1)

Lecture 129 Guide 51: Multidimensional Scaling – ALSCAL procedure (2)

Lecture 130 Guide 51: Multidimensional Scaling – PROXSCAL procedure (1)

Lecture 131 Guide 51: Multidimensional Scaling – PROXSCAL procedure (2)

Section 11: Data Reduction

Lecture 132 Guide 52: Principal Component Analysis – Introduction

Lecture 133 Guide 52: Principal Component Analysis – Running the Procedure

Lecture 134 Guide 52: Principal Component Analysis – Testing For Adequacy

Lecture 135 Guide 52: Principal Component Analysis – Obtaining a Final Solution

Lecture 136 Guide 52: Principal Component Analysis – Interpreting the Final Solutions

Lecture 137 Guide 52: Principal Component Analysis – Final Considerations

Lecture 138 Guide 53: Correspondence Analysis – Introduction

Lecture 139 Guide 53: Correspondence Analysis – Running the Procedure

Lecture 140 Guide 53: Correspondence Analysis – Results Interpretation

Lecture 141 Guide 53: Correspondence Analysis – Imposing Category Constraints

Section 12: Grouping Methods

Lecture 142 Guide 54: Cluster Analysis – Introduction

Lecture 143 Guide 54: Cluster Analysis – Hierarchical Cluster

Lecture 144 Guide 54: Cluster Analysis – K-Means Cluster

Lecture 145 Guide 55: Discriminant Analysis – Introduction

Lecture 146 Guide 55: Discriminant Analysis – Simple DA

Lecture 147 Guide 55: Discriminant Analysis – Multiple DA

Section 13: Addenda

Lecture 148 Guide 56: Multiple Response Analysis

Section 14: Course Materials

Lecture 149 Download Links

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

Course Information:

Udemy | English | 14h 3m | 10.48 GB
Created by: Bogdan Anastasiei

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