SPSS Linear Regression Complete Tutorial with PhD Professor
4.3 (7 ratings)
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SPSS Linear Regression Complete Tutorial with PhD Professor

Includes visusalizations, interactions, assumptions, data issues, power analysis, outliers, and detailed interpretations
4.3 (7 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
982 students enrolled
Created by Stats Friend
Last updated 2/2019
English
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Current price: $51.99 Original price: $74.99 Discount: 31% off
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This course includes
  • 11 hours on-demand video
  • 21 articles
  • 17 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • SPSS Linear Regression for Business or Dissertation
  • Visualizing Linear Regression Results
  • Advanced topics like interactions and categorical predictors
  • Dealing with data, distribution, and missing data problems
  • Deep understanding of the process and meaning of the results
Course content
Expand all 47 lectures 11:04:39
+ Refresher and Basics
6 lectures 53:57
Refresher and basics
00:10
Refresher - basics you should know
21:13
Importing Data
00:09
Importing Data
07:28
SPSS Basics
00:13
SPSS Basics
24:44
+ Linear Regression
10 lectures 02:47:50
Descriptive Statistics and Normality Testing
00:21

There's a lot here, foundational skills and some relevant decisions about how to move forward.

Attached file is for following along, or use your own data and parallel the actions. Attached file has a few extra variables, but also all the ones pictured in the video.

Descriptive Statistics and Normality Testing
54:50
Simple Linear Regression: 1 predictor
32:22
Simple Linear Regression (additional information)
00:23
Multiple Regression: 2+ predictors
00:44
Categorical Predictors
00:28
Stepwise Multiple Regression
01:14
Stepwise Multiple Regression
16:39
+ Interactions (also known as moderation)
11 lectures 01:58:22
Interactions
01:11
Interaction with 2 Continuous Predictors
45:22
Interaction with 2 Continuous Predictors - more information
00:54
Interaction with 2 Categorical Predictors
00:22
Interaction with 2 Binary Predictors
00:16
Interaction with 2 Binary Predictors
20:42
Interaction with Categorical and Continuous Predictors
00:14
Interaction with Categorical and Continuous Predictors
14:39
Interaction with 3 Continuous Predictors (3-way)
00:26
Interaction with 3 Continuous Predictors (3-way)
14:53
+ Visualizations
6 lectures 01:22:05
Visuals for Continuous Interactions
00:16
Plot Settings & Finishing for Continuous Interactions
00:27
Plot Settings & Finishing for Continuous by Continuous Interactions
26:21
Visuals for Categorical by Categorical Interactions
00:23
Visuals for Categorical by Categorical Interactions
21:13
+ Power Analysis for Linear Regression
7 lectures 01:22:34
Power Analysis
00:27
A Priori Power Analysis Text
02:10
A Priori Power Analysis
11:57

Effect size estimation is an essential part of planning the right-sized study you need.

Effect size, as part of a power analysis, ensures that you put enough, but not too much, effort and resources into your study. Whether this is proper budgeting of a corporation, or how many weeks it will take to collect data for a dissertation, the importance is obvious.

Consider the usual complexity of topics we do research on. Take the study of depression in psychology as an example. Depression is caused by a myriad of personal and situational factors. When we consider all possible things that contribute, we know that there are probably at least 10 important factors. If all equally important then each would explain about 10% of depression. That seems like a small amount, but anything that explains that much is actually very helpful in the overall effort to treat the problem. If your study tested a relationship between exercise and depression, explaining 10% of depression with exercise would be impressive. That would mean that of all possible predictors (e.g. genetics, experiences, cognitive factors, nutrition, attributions) exercise is one of the important predictors. By default, we tend to think something like this will have a 20% or 30% impact, because we are not thinking of how many other factors there are. Or, because we are invested in our own research topic that we usually care deeply about, or has been commissioned by someone who cares a lot.

Do you see how the above is ironic and counterproductive? It isn't immediately obvious, but the over-confidence of researchers leads them to build a lower-power study. That study fails to find an effect, even though they were right all along and there is a relationship. Believing the relationship is strong can lead to failing to detect it at all.

On the other hand, you could get a huge sample size to ensure you'll find an effect if it is there. But this can be wasteful or misguided as well. If you commission a huge study with 6,000 participants you can be pretty confident you fill find a significant effect, even if it is small. But is it worth it to spend all these resources to explain less than 1% of the variance in a dependent variable? Almost certainly not.

Useful questions:

  • What is the minimum effect you want to detect in this study?

  • How strong is the effect in similar research?

  • What size of effect is enough to have real-world consequences?

Below is a reference table. If you would guess that your IV might explain 10% of your DV, then you could use .3 as a good assumption of your correlation/regression coefficient.

From my experience, coefficients above .3 are uncommon in research on people. Coefficients above .5 almost never happen in real-world research. I suggest you assume a smaller relationship than your natural inclination, as over-estimation of the effect size is usually the problem, rather than underestimation.


  1. *unstandardized correlation or regression coefficient (r, B)

  2. Variance Explained is simply the coefficient squared.

  3. Percentage column is Variance Explained converted to % (x100)


Effect Size calculators:

https://www.psychometrica.de/effect_size.html

Or try searching things like "effect size calculator" for more options.

Or be more specific like "convert cohen's d to f".

More Power Analysis and Effect Size
21:57
Even More Power Analysis (not for everyone)
00:13
Even More Power Analysis
18:12
Post-hoc power analysis
27:38
+ Emergent Needs and Problems (view only what you need)
5 lectures 02:35:29
Univariate Outliers
29:09
Multivariate Outliers
30:23
Non-normal variable distributions
19:43
Missing data handling in SPSS procedures
07:29
Simple Slopes Analysis
01:08:45
Requirements
  • College math
Description

In-depth modular class - learn only what you need! Includes optional modules for basics, advanced, & emergent problems.

Anyone can follow this step-by-step, end-to-end, in-depth tutorial for linear regression. The modular course covers all the possible pitfalls (well, pretty close), but in optional modules so you don't get bogged down with stuff you don't need.

I've poured all my consulting knowledge into this, even complex problems are covered in tremendous detail. However, you can skip the advanced information you don't need or want.

Topics Covered:

  • Data Prep

  • Missing Data

  • Descriptive Statistics

  • Checking Normality / Assumptions

  • Variable Transformation

  • Simple Linear Regression

  • Multiple Linear Regression

  • Univariate and Multivariate Outliers

  • Interactions / Moderation

  • Interpretation of Results

  • Scatter Plots

  • Line Graphs

  • Graphing Interactions

  • Multicollinearity

  • Variable Centering

  • Non-Linear Relationships

  • Pre- & Post-Hoc Power Analysis

  • Other Emergent Issues

Who this course is for:
  • Graduate students completing thesis or dissertation
  • Job-seekers interested in adding Linear Regression as a data analysis skill
  • Academic researchers
  • Psychology students interested in data analysis