Statistics / Data Analysis in SPSS: Inferential Statistics from Udemy

What's inside

Learning objectives

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

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

Syllabus

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".

SPSS Data file for this video: one sample t_example 1.

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.

One Sample t Test

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.

SPSS Data file for this video: one sample t_example 1.

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).

SPSS Data file for this video: one sample t_example 1.

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.

SPSS Data file for this video: one sample t_example 2.

Independent Samples t Test

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".

SPSS Data file for this video: independent t_example 1.

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.

SPSS Data file for this video: independent t_example 1.

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.

SPSS Data file for this video: independent t_example 1.

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

SPSS Data file for this video: independent t_example 2.

Dependent Samples t Test

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, instead of being the same people, the two groups can also be related, such as with identical twins. The key with this test is that the groups are naturally related in some way (as opposed to the independent samples t test).

SPSS Data file for this video: dependent t_example 1.

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.

SPSS Data file for this video: dependent t_example 1.

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

SPSS Data file for this video: dependent t_example 2.

A very important (and marketable) skill is knowing how to select the correct test for a set of data. The questions on this quiz all involve t tests. Your job is to select the most appropriate test for each situation. Good luck!

ANOVA - Analysis of Variance

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).

SPSS Data file for this video: one way ANOVA_example 1.

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).

SPSS Data file for this video: one way ANOVA_example 1.

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

SPSS Data file for this video: one way ANOVA_example 2.

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

In this video we take a look at the relationship between the independent samples t test and the one-way between subjects ANOVA when there are two groups. You might be surprised what you find!

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).

SPSS Data file for this video: one within ANOVA_example 1.

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.

SPSS Data file for this video: one within ANOVA_example 1.

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

SPSS Data file for this video: one within ANOVA_example 2.

Correlation and Regression

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.

SPSS Data file for this video: Correlation_example 1.

This lecture covers a second example on correlation.

SPSS Data file for this video: Correlation_example 2.

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

SPSS Data file for this video: Regression_example 1.

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

SPSS Data file for this video: Regression_example 2.

Chi-Square

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

SPSS Data file for this video: Chi Square GFI_example 1.

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

SPSS Data file for this video: Chi Square GFI_example 2.

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

SPSS Data file for this video: Chi Square Test of Independence_example 1.

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

SPSS Data file for this video: Chi Square Test of Independence_example 2.

Bonus Material - Data Management in SPSS

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

SPSS Data file for this video: Compute Procedure_Manual Add.

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.

SPSS Data file for this video: Compute Procedure_Sum Function.

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.

SPSS Data file for this video: Sort Example.

Conclusion

Course Conclusion

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Delves into inferential statistics, which is essential for business professionals

Taught by Quantitative Specialists, who are recognized experts in the field

Covers a wide range of statistical tests, including three different t tests, two ANOVAs, post hoc tests, chi-square tests, correlation, and regression

Provides hands-on examples and in-depth explanations of each test

Includes two in-depth examples of each test for practical implementation

Focuses on students with basic statistical knowledge and helps them build a strong foundation

Reviews summary

Competent intro to spss

learners say this introductory course is an engaging entryway to statistics and SPSS. Although no hands-on exercises meant students couldn't practice entering in their own syntax, learners recommend this course to further understanding and applications of inferential statistics in SPSS.

Course is good for beginners

"good intro to stats and to running important and marketable analyses in SPSS."

Engaging intros to SPSS

"good intro to stats and to running important and marketable analyses in SPSS."

"only criticism is that we should be pasting syntax throughout, but perhaps that is beyond the scope of this course."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Statistics / Data Analysis in SPSS: Inferential Statistics with these activities:

Review basic statistics

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This course assumes that you have a basic understanding of statistics. If you need to brush up on your skills, you can find many resources online or in textbooks.

Browse courses on Statistics

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Review the materials from your previous statistics course.
Find online tutorials or videos on basic statistics.
Take a practice quiz to test your understanding.

Read Lewis-Beck, M. & Bryman, A. (2016). Data Analysis For Social Science.

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The topics covered in this course will be enhanced by reading from the text specified in this activity.

View The SAGE Encyclopedia of Social Science... on Amazon

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Read Statistics for Research (4th Edition) by George A. Morgan

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This book covers many of the topics that will be covered in this course. It is a great resource for additional reading and review.

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Read the chapters that are assigned in the course.
Complete the practice questions at the end of each chapter.

Six other activities

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SPSS tutorials

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This course uses SPSS for data analysis. There are many tutorials available online that can help you learn how to use SPSS.

Browse courses on SPSS

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Search for SPSS tutorials online.
Watch the tutorials and follow along with the instructions.
Try out the techniques you learn on your own data sets.

Practice questions on significance testing

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Significance testing is a key concept in this course and completing practice exercises will help reinforce your knowledge.

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Work through the practice questions provided in the course materials.
Find additional practice questions online or in textbooks.
Attend the Q&A sessions to get help from the instructor and other students.

Discussion forums

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This course has a discussion forum where you can ask questions, share insights, and collaborate with your classmates.

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Log in to the discussion forum.
Read the existing threads.
Ask questions or start new discussions.
Respond to other people's posts.

Attend a data analysis workshop

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There are many workshops available that can help you learn new data analysis techniques and improve your skills.

Browse courses on Data Analysis

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Search for data analysis workshops in your area.
Register for a workshop.
Attend the workshop and participate in the activities.
Follow up with the instructors or other participants after the workshop.

Project: Analyze a data set

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This activity will allow you to apply the concepts you learn in this course to a real-world data set.

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Choose a data set that is relevant to your interests.
Clean and prepare the data.
Analyze the data using the techniques you learn in this course.
Create a report or presentation to summarize your findings.
Share your project with the class.

Contribute to open-source projects

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There are many open-source projects related to data analysis and statistics. Contributing to these projects can help you learn new skills and improve your understanding of the material.

Browse courses on Open Source

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Find an open-source project that you are interested in.
Read the project documentation.
Start contributing to the project.
Collaborate with other contributors.

Career center

Learners who complete Statistics / Data Analysis in SPSS: Inferential Statistics will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians apply statistical methods to collect, analyze, interpret, and present data. They work in a variety of fields, including healthcare, finance, and market research. This course provides a thorough introduction to inferential statistics, making it highly relevant to this role.

See salaries and explore the career path for Statistician

Data Scientist

Data Scientists work with large amounts of data to solve complex business problems using statistical methods. These professionals typically hold an advanced degree. Inferential statistics plays a key role in data science, and this course can provide a solid foundation for aspiring Data Scientists.

See salaries and explore the career path for Data Scientist

Biostatistician

Biostatisticians apply statistical methods to medical and health-related data. They work in hospitals, universities, and government agencies. This course covers inferential statistics, a critical tool for biostatisticians to analyze clinical data and draw conclusions about the effectiveness of medical treatments.

See salaries and explore the career path for Biostatistician

Quantitative Analyst

A Quantitative Analyst uses statistical and mathematical models to analyze financial data and make investment decisions. This course provides a solid introduction to inferential statistics, an essential tool for Quantitative Analysts.

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Epidemiologist

Epidemiologists investigate the causes and patterns of disease in populations. They use statistical methods to analyze data and identify risk factors for disease. Inferential statistics is an important tool for epidemiologists, and this course can provide a solid foundation for this role.

See salaries and explore the career path for Epidemiologist

Survey Researcher

Survey Researchers design, conduct, and analyze surveys to collect data on a variety of topics. They use statistical methods to ensure that their surveys are accurate and reliable. This course provides a foundation in inferential statistics, essential for designing and analyzing effective surveys.

See salaries and explore the career path for Survey Researcher

Market Research Analyst

Market Research Analysts collect and analyze data on consumer behavior and trends. They use this information to help businesses make better decisions about product development, marketing, and pricing. Inferential statistics is an important tool for market research analysts, and this course can provide a solid foundation for this role.

See salaries and explore the career path for Market Research Analyst

Data Analyst

A Data Analyst's job description is to collect, clean, and analyze large amounts of data. This role is responsible for extracting meaningful insights from company data and communicating these findings to stakeholders. The course *'Statistics / Data Analysis in SPSS: Inferential Statistics'* is especially relevant to this role, as inferential statistics is a critical tool for data analysts to draw conclusions from data and make predictions.

See salaries and explore the career path for Data Analyst

Econometrician

Econometricians use statistical methods to analyze economic data. They develop models to explain economic phenomena and forecast economic trends. Inferential statistics is a fundamental part of econometrics, and this course may be helpful for those seeking a career in this field.

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Business Analyst

A Business Analyst's job description revolves around using data analysis to identify business needs and develop solutions. Inferential statistics is a valuable tool for business analysts looking to analyze business data, understand customer needs, and improve business operations.

See salaries and explore the career path for Business Analyst

Market Researcher

Individuals in the Market Researcher role gather and analyze research data to understand market trends and consumer behavior. This course may be especially helpful to Market Researchers that wish to use inferential statistics to design surveys, analyze research data, and draw conclusions from their findings.

See salaries and explore the career path for Market Researcher

Operations Research Analyst

Operations Research Analysts use statistical and mathematical models to solve complex problems in business and industry. They help organizations improve efficiency, reduce costs, and make better decisions. Inferential statistics is an important part of operations research, and this course may be helpful for those seeking a career in this field.

See salaries and explore the career path for Operations Research Analyst

Data Engineer

Data Engineers design and build the infrastructure that stores and processes data. They ensure that data is accessible, reliable, and secure. Inferential statistics may be helpful for Data Engineers who wish to design and build data systems that can handle large amounts of data and produce accurate results.

See salaries and explore the career path for Data Engineer

Financial Analyst

A Financial Analyst's job responsibilities include evaluating investments and making recommendations to clients. They use statistical and economic data to create financial models and make predictions about the performance of investments. This course introduces inferential statistics, an essential tool for analyzing financial data and making sound investment decisions.

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Actuary

Actuaries are responsible for analyzing and assessing risks, typically in the insurance industry. They use statistical and mathematical models to determine the likelihood and potential impact of future events. A course in inferential statistics will help build a foundation for this role.

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