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**Statistics / Data Analysis in SPSS: Inferential Statistics**

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Genre: Business / Data & Analytics | Language: English | +Project Files

Learn how to use SPSS to analyze and interpret t tests, ANOVA, correlation, regression and chi-square statistical tests.

Statistics play a key role in the process of making sound business decisions that will generate higher profits. Without statistics, it's difficult to determine what your target audience wants and needs.

Inferential statistics, in particular, help you understand a population's needs better so that you can provide attractive products and services.

This course is designed for business professionals who want to know how to analyze data. You'll learn how to use IBM SPSS to draw accurate conclusions on your research and make decisions that will benefit your customers and your bottom line.

**Use Tests in SPSS to Correctly Analyze Inferential Statistics**

Use the One Sample t Test to Draw Conclusions about a Population

Understand ANOVA and the Chi Square

Master Correlation and Regression

Learn Data Management Techniques

**Analyze Research Results Accurately to Make Better Business Decisions**

With SPSS, you can analyze data to make the right business decisions for your customer base. And by understanding how to use inferential statistics, you can draw accurate conclusions about a large group of people, based on research conducted on a sample of that population.

This easy-to-follow course, which contains illustrative examples throughout, will show you how to use tests to assess if the results of your research are statistically significant.

You'll be able to determine the appropriate statistical test to use for a particular data set, and you'll know how to understand, calculate, and interpret effect sizes and confidence intervals.

You'll even know how to write the results of statistical analyses in APA format, which you can then adapt to other formats.

**Contents and Overview**

This course begins with a brief introduction before diving right into the One Sample t Test, Independent Samples t Test, and Dependent Samples t Test. You'll use these tests to analyze differences and similarities between sample groups in a population. This will help you determine if you need to change your business plan for certain markets of consumers.

Next, you'll tackle how to use ANOVA (Analysis of Variance), including Post-hoc Tests and Levene's Equal Variance Test.

These tests will also help you determine what drives consumer decisions and behaviors between different groups.

When ready, you'll master correlation and regression, as well as the chi-square. As with all previous sections, you'll see illustrations of how to analyze a statistical test, and you'll access additional examples for more practice.

Finally, you'll learn about data management in SPSS, including sorting and adding variables.

By the end of this course, you'll be substantially more confident in both IBM SPSS and statistics. You'll know how to use data to come to the right conclusions about your market.

By understanding how to use inferential statistics, you'll be able to identify consumer needs and come up with products and/or services that will address those needs effectively.

**What are the requirements?**

Introduction to statistics course (either currently taking or already have completed) is recommended but not absolutely necessary

Access to IBM SPSS Statistical software (strongly recommended)

**What am I going to get from this course?**

Over 31 lectures and 4.5 hours of content!

In this course, you will gain proficiency in how to analyze a number of statistical procedures in SPSS.

You will learn how to interpret the output of a number of different statistical tests

Learn how to write the results of statistical analyses using APA format

**What is the target audience?**

Students seeking help with SPSS, especially how to analyze and interpret the results of statistical analyses

Professionals desiring to augment their statistical skills

Anyone seeking to increase their data analytic skills

**Curriculum**

Lecture 1 Course Preview: One Sample t Test - Example 1 10:51

The one sample t test is covered in this lecture.

**The SPSS data files (for the entire course) are available under "downloadable materials" in this lecture.**

Also, a pdf file of the results (the output file) is also available. The output file for this lecture is located below and is titled, "One sample t example 1 output"

All other output files are located within their respective lecture. For example, the output file for the second example on the one sample t test is located in the lecture "one sample t_example 2".

Lecture 2 Course Introduction 09:43

An overview of the course is provided in this lecture, including highlighting how to download the data files and the output files for the course.

Section 1: One Sample t Test

Lecture 3 One Sample t Test - Confidence Interval 04:40

This lecture continues with the example from the previous lecture, with a focus on how to interpret the section of the output labeled, "95% confidence interval of the difference".

Learning Tip: If the confidence interval includes the value of zero, the test is not statistically significant. If it does not include zero, the test is statistically significant.

Lecture 4 One sample t Test - Effect Size 03:48

In this lecture, how to calculate and interpret the effect size for the one sample t test is presented.

Learning Tip: Cohen's effect size standards for t are: small = .20, medium = .50, large = .80. The effect size indicates the number of standard deviation units of a difference that exist between two groups. For example, an effect size of 1.00 indicates one standard deviation of a difference between the sample mean (the treated group) and the population mean (the untreated group).

Lecture 5 One Sample t Test - Example 2 07:20

In this lecture, a second example utilizing the one sample t test is illustrated.

Learning Tip: Try running and interpreting the one sample t test on your own (using the data file "one sample t_example 2.sav") prior to watching this lecture. This will help both increase your understanding and retention of the subject matter.

Section 2: Independent Samples t Test

Lecture 6 Independent Samples t Test - Example 1 15:45

In this lecture, the first example on the independent samples t test is covered.

Learning Tip: The independent samples t test is used when two separate or unrelated groups are compared. Mathematically, unrelated groups are known as being "independent".

Lecture 7 Independent Samples t Test - Confidence Interval 01:49

In this lecture, the confidence interval for the independent samples t test is covered.

Learning Tip: If the confidence interval includes the value of zero, the test is not statistically significant. If it does not include zero, the test is statistically significant.

Lecture 8 Independent Samples t Test - Effect Size 05:25

In this lecture, the effect size for the independent samples t test is covered.

Learning Tip: Cohen's effect size standards for t are: small = .20, medium = .50, large = .80. The effect size indicates the number of standard deviation units of a difference that exist between two groups. For example, an effect size of .50 indicates one-half of a standard deviation difference between the two groups.

Lecture 9 Independent Samples t Test - Example 2 05:28

In this lecture, the second example on the independent samples t test is covered.

Section 3: Dependent Samples t Test

Lecture 10 Dependent Samples t Test - Example 1 10:35

In this lecture the dependent samples t test is covered.

Learning Tip: The dependent samples t test is used when the two samples are naturally dependent. This usually consists of the same people in each group such as a when people take a pretest and then a posttest. However, the two groups can also be related, such as with identical twins.

Lecture 11 Dependent Samples t Test - Effect Size 02:30

In this lecture, the effect size for the dependent samples t test is covered.

Learning Tip: Cohen's effect size standards for t are: small = .20, medium = .50, large = .80. The effect size indicates the number of standard deviation units of a difference that exist between the two groups. For example, an effect size of .25 indicates one-quarter of a standard deviation difference between the two groups.

Lecture 12 Dependent Samples t Test - Example 2 07:25

In this lecture, the second example on the dependent samples t test is covered.

Quiz 1 T test quiz - choosing the correct test 4 questions

Section 4: ANOVA - Analysis of Variance

Lecture 13 ANOVA (between subjects) - Example 1 10:16

In this lecture, the one-way between subjects ANOVA is covered.

Learning Tip: The one-way between subjects ANOVA may be used when 2 or more separate or unrelated groups are compared. Many people think of this test being used with 3 or more groups, but it is perfectly fine to use it for two groups as well. (Either the ANOVA or the independent samples t test can be used when there are two unrelated groups).

Lecture 14 ANOVA - Post-hoc Tests 10:13

In this lecture, post-hoc tests are covered.

Learning Tip: "Post-hoc" means "after the fact"; post-hoc tests are typically conducted after a significant result is found for the ANOVA. If the ANOVA is not significant, then post-hoc tests typically are not interpreted.

While there are many different post-hoc tests available, Tukey's test is covered here as (1) it is one of the more commonly used post-hoc tests and (2) research has shown that Tukey's test does a good job at keeping the overall alpha level at .05 (assuming one is using an alpha of .05).

Lecture 15 ANOVA (between subjects) - Example 2 08:27

In this lecture, a second example using the one-way between subjects ANOVA is covered. Post-hoc tests are also covered in this lecture.

Lecture 16 Levene's Equal Variance Test 12:54

In this video, we take a look at Levene's test of equal variances.

Lecture 17 Within ANOVA - Example 1 11:14

In this lecture, the one-way within subjects ANOVA is covered.

Learning Tip: The one-way within subjects ANOVA may be used when 2 or more dependent or related groups are compared. Many people think of this test being used with 3 or more groups, but it is perfectly fine to use it for two groups as well. (Either the within ANOVA or the dependent samples t test can be used when there are two related groups).

Lecture 18 Within ANOVA - Post-hoc Tests 12:21

In this lecture, post-hoc tests are covered. The appropriate post-hoc test to use for the within subjects ANOVA is the dependent samples t test, with a separate t test used for each pair of groups.

Lecture 19 Within ANOVA - Example 2 08:14

A second example on the one-way within subjects ANOVA is covered here.

Section 5: Correlation and Regression

Lecture 20 Correlation - Example 1 14:23

This lecture covers the Pearson r correlation coefficient. How to produce a scatterplot of the two variables in SPSS is also illustrated towards the end of the lecture.

Lecture 21 Correlation - Example 2 06:26

This lecture covers a second example on correlation.

Lecture 22 Regression - Example 1 17:07

This lecture covers simple regression, which is used when there is one predictor (independent variable) and one criterion (dependent variable).

Lecture 23 Regression - Example 2 14:09

A second example using simple regression is covered in this lecture.

Section 6: Chi-Square

Lecture 24 Chi-Square Goodness of Fit Test - Example 1 08:25

In this lecture the chi-square goodness of fit test is covered.

Lecture 25 Chi-Square Goodness of Fit Test - Example 2 13:03

In this lecture, a second example on the chi-square goodness of fit test is provided.

Lecture 26 Chi-Square Test of Independence - Example 1 21:38

In this lecture, the chi-square test of independence is covered.

Lecture 27 Chi-Square Test of Independence - Example 2 12:20

This lecture provides a second example on the chi-square test of independence.

Section 7: Bonus Material - Data Management in SPSS

Lecture 28 Adding Variables in SPSS Using the Compute Procedure 04:14

In this lecture, how to add a number of variables together to create a total score using the compute procedure is illustrated.

Lecture 29 Adding Variables using the Sum Function 06:27

In this lecture, how to add a number of variables together to create a total score using the compute procedure is illustrated. Whereas the previous lecture manually added the variables (using SPSS), in this lecture the variables are added together using the SUM function in SPSS.

Lecture 30 Sort One or More Variables 05:16

This lecture illustrates how to use the sort command in SPSS. The sort command is illustrated first on a single variable in SPSS; afterwards, a set of cases is sorted on two variables simultaneously.

Section 8: Conclusion

Lecture 31 Course Conclusion 02:32

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