SPSS For Research from Udemy

What's inside

Learning objectives

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)
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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)
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Show less

Syllabus

Find out what you will study in this course.

What's it all about - why you should take this course.

See the detailed structure of this course here.

The simplest (yet very important) operations with data in SPSS.

How to create a file and open an existing file in SPSS.

How to create variables and set variable properties.

Learn when you need to recode your variables and how to do it.

How to convert dichotomous and multinomial variables into dummy variables.

How to filter out cases in an SPSS data set.

How to split file using certain criteria in order to perform analyses on groups or strata of the population.

Know when it is necessary to weigh your cases and how to perform this operation.

How to create the most useful diagrams to visualize your data.

Learn how to build column charts in SPSS.

Learn how to build and interpret line charts.

How to use the Chart Builder in order to create simple and grouped scatterplot charts.

How to build and interpret boxplot charts (simple and grouped).

The basic statistical analysis procedures in SPSS.

How to use the Frequencies procedure to build frequency tables and to generate statistical indicators.

How to generate the essential statistics for continuous variables.

The Explore procedure helps you generate statistical indicators by groups or strata, create graphs and run normality tests.

Another quick and easy procedure to compute the statistics for a continuous variable.

How to build cross tables to visualize the relationship between categorical variables.

How to check for normality, identify the outliers and transform your variables.

How to compute and interpret the statistical tests for normality.

How to use charts in order to assess normality.

How to handle the non normal distributions (which are not uncommon).

How to use the boxplot diagram in order to check for outliers in your data.

How to detect the outliers with the help of the standardized scores.

What to do if you have extreme values in your data series.

How to transform your variables in an attempt to get normal distributions (unfortunately, often this is not possible).

How to perform the most important univariate analyses in SPSS.

When and why to use the one-sample t test.

How to perform the one-sample t test and interpret the results.

How to perform the binomial test in order to analyze the dichotomous variables.

How to use the binomial test when your data are weighted.

The chi square test for goodness-of-fit is very useful when you study the categorical variables with more than two groups.

How to perform the chi square test for goodness-of-fit when your data are weighted.

Association Tests

When and how to use the Pearson correlation coefficient.

How to check the assumptions of the Pearson correlation procedure.

How to compute and interpret the Pearson correlation coefficient.

When and why you should use the Spearman correlation.

How to compute the Spearman correlation coefficient and interpret it.

What is partial correlation? The four scenarios for analyzing the partial correlation coefficient.

How to compute and interpret the partial correlation coefficient in a real-world situation.

How to use the chi square test for association in order to analyze the relationship between categorical variables.

How to use the chi square test for association when your data are weighted.

What is loglinear analysis and when you can use it.

How to define the optimal parcimonious model in a loglinear analysis.

How to interpret the coefficients of the optimal loglinear model.

How to test the differences between the means of three or more groups.

What is the independent samples t test and when you should use it.

How to check the assumptions of the independent samples t test.

How to run the independent samples t test procedure and interpret the results.

What is the paired samples t test and when it is useful.

How to check the assumptions of the paired samples t test.

How to run the paired samples t test procedure and interpret the results.

The one-way ANOVA is useful when you want to compare the means of three or more groups.

How to check the assumptions for the one-way ANOVA.

How to interpret the F test (or Welch test, if the case) results.

How to perform pairwise comparisons for the groups in your population.

What is the two-way ANOVA and when you should use it.

How to check the assumptions for the two-way ANOVA.

How to interpret the interaction effect in a two-way ANOVA.

How to compute and interpret the simple main effects, if the interaction effect is statistically significant.

What is the three-way ANOVA and when it may be necessary to employ it.

How to check the assumptions for the three-way ANOVA.

How to interpret the third order interaction effect.

How to compute and interpret the simple second order interaction effects (if the third order interaction is significant).

How to compute and interpret the simple main effects (if one or more second order interaction effects are significant).

How to compute and interpret the simple comparisons between means.

How to compute and interpret the simple comparisons between means

(more examples).

What is the multivariate ANOVA and when you should use it.

How to check the assumptions for the multivariate ANOVA.

How to detect the multivariate outliers in a multivariate ANOVA.

How to interpret the results of a multivariate ANOVA.

What is the analysis of covariance and when it is useful.

How to check the main assumptions for the analysis of covariance.

Some more assumption checking for ANCOVA. :)

How to interpret the ANCOVA results.

What is the repeated measures ANOVA.

How to check the assumptions for the repeated measures ANOVA.

How to interpret the main output of the repeated measures ANOVA.

What is the within-within subjects ANOVA and when it is useful.

Assumption checking for the within-within subjects ANOVA.

How to interpret the interaction effect in a within-within subjects ANOVA.

How to compute and interpret the simple main effects (when the interaction effect is significant).

A bit more about the simple main effects in a within-within subjects ANOVA.

How to continue the analysis if the interaction effect is not significant.

What is the mixed ANOVA and when you can use it.

How to check the assumptions for a mixed ANOVA.

How to interpret the interaction effect in a mixed ANOVA.

How to compute and interpret the simple main effects (if the interaction is not statistically significant).

A bit more about the simple main effects in a mixed ANOVA.

How to go on with the analysis if the interaction effect is not significant.

What is the non-parametric Mann-Whitney test (for independent samples).

How to interpret the results of the Mann-Whitney test.

How to perform the Wilcoxon test (for paired samples) and how to interpret its results.

How to perform the sign test (for paired samples) and interpret the results.

How to perform the Kruskal-Wallis test for comparing the median of three or more groups.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers a wide range of statistical procedures, from basic to advanced, making it suitable for various research needs

Presents statistical procedures in a simple, straightforward manner, avoiding technical jargon and mathematical formulas

Includes hands-on demonstrations of how to perform each procedure in SPSS, enhancing practical application

Explains how to interpret the output of each statistical procedure, enabling learners to draw meaningful conclusions

Requires learners to have access to SPSS software, which may require a separate purchase or subscription

Assumes a basic understanding of statistical concepts, which may require learners to acquire prerequisite knowledge

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 SPSS For Research with these activities:

Review Basic Statistics Concepts

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Solidify your understanding of fundamental statistical concepts. This will provide a strong foundation for understanding the more advanced procedures covered in the course.

Browse courses on Hypothesis Testing

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Review definitions of key statistical terms.
Work through practice problems on hypothesis testing.
Summarize the different types of statistical errors.

Review: Statistics Without Tears

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Gain a conceptual understanding of statistics. This will help you interpret SPSS outputs more effectively and avoid relying solely on button-clicking.

View Statistics without Tears: An Introduction for... on Amazon

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Read the chapters covering descriptive statistics and inferential statistics.
Summarize the key concepts from each chapter.
Relate the concepts to the types of analyses covered in the SPSS course.

Practice Recoding Variables in SPSS

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Master the skill of recoding variables. This is a fundamental data manipulation technique that is essential for preparing data for analysis in SPSS.

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Download a sample dataset with categorical variables.
Recode existing variables into new categories.
Create dummy variables from categorical variables.
Verify the recoding by running frequency distributions.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Create a Data Visualization Portfolio

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Develop your data visualization skills. This will allow you to effectively communicate your findings and insights from SPSS analyses.

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Choose a dataset relevant to your field of interest.
Use SPSS to create various charts and graphs.
Write a brief description of each visualization.
Compile the visualizations into a portfolio.

Review: Discovering Statistics Using IBM SPSS Statistics

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Deepen your understanding of statistical concepts and SPSS procedures. This book provides a more in-depth treatment of the topics covered in the course.

View Discovering Statistics Using JASP on Amazon

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Read the chapters related to the statistical techniques covered in the course.
Work through the examples provided in the book using SPSS.
Compare the book's explanations with the course materials.

Analyze a Public Dataset Using SPSS

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Apply your SPSS skills to a real-world dataset. This will solidify your understanding of the entire data analysis process, from data cleaning to interpretation.

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Select a public dataset from a source like Kaggle or a government website.
Clean and prepare the data for analysis in SPSS.
Perform appropriate statistical analyses based on your research questions.
Interpret the results and draw conclusions.
Write a report summarizing your findings.

Answer Questions in Online Forums

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Reinforce your learning by helping others. Explaining concepts to others is a great way to solidify your own understanding.

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Find online forums or communities related to SPSS and statistics.
Browse the forums for questions related to the course material.
Provide clear and helpful answers to the questions.

Career center

Learners who complete SPSS For Research will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst is responsible for collecting, cleaning, and interpreting data using statistical methods and software. This role often requires expertise in using tools like SPSS to extract meaningful insights. This course equips a data analyst with a strong foundation in statistical operations using SPSS, covering topics from basic data manipulation to advanced techniques like logistic regression and principal component analysis. A learner interested in becoming a data analyst might especially benefit from the course's emphasis on practical application, where statistical procedures are explained in a way that is accessible even to those without a strong mathematical background and with hands-on guides for each technique.

See salaries and explore the career path for Data Analyst

Academic Researcher

An Academic Researcher designs and conducts research studies within a university or similar setting, often requiring in-depth statistical analysis. This role uses statistical software to analyze data effectively. This course assists an academic researcher in mastering essential statistical procedures through hands-on learning with SPSS, covering everything from basic descriptive statistics to complex methods like multivariate analysis and data reduction techniques. It's especially useful for academic researchers because it focuses on using SPSS to address research questions, interpret results, and validate findings.

See salaries and explore the career path for Academic Researcher

Market Research Analyst

A Market Research Analyst uses statistical software to analyze consumer behavior and preferences. This role involves collecting data, interpreting trends, and providing insights to guide business decisions. This course on SPSS helps the market research analyst perform essential tasks such as data cleaning, hypothesis testing, and regression analysis. This course is particularly beneficial to those interested in this career path because it provides practical experience in using SPSS for various statistical techniques, including tests for association and comparison of means using ANOVA techniques. These operations are crucial for extracting actionable information from complex data sets, directly aligning with the daily activities of a successful market research analyst.

See salaries and explore the career path for Market Research Analyst

Research Associate

A Research Associate supports research projects by gathering, analyzing, and interpreting data. This role often uses statistical software to process and understand study results. This course on SPSS helps a research associate understand the entire statistical analysis process, from data preparation to advanced analytical techniques. A learner pursuing a career as a research associate should take this course because it is designed for those outside of mathematics and statistics, focusing on simplifying complex concepts in the field. Such a structure and direction are extremely helpful in understanding, running, and interpreting statistical tests central to academic research and reporting.

See salaries and explore the career path for Research Associate

Social Science Researcher

A Social Science Researcher designs and conducts studies to understand social phenomena. This role requires a strong foundation in statistical methods for analyzing data. This course provides a social science researcher statistical knowledge using SPSS by covering basic to advanced statistical techniques, including hypothesis testing and multivariate analysis. This course is especially good for a social science researcher because it is tailored to people who are not professional mathematicians and presents statistical procedures in an easily digestible manner, avoiding technical jargon and mathematical formulas as much as possible.

See salaries and explore the career path for Social Science Researcher

Survey Researcher

A Survey Researcher is involved in designing, conducting, and analyzing surveys. This role requires a solid understanding of data collection and analysis methods. This course on SPSS helps a survey researcher by enhancing analytic abilities through various statistical analysis procedures, from basic descriptive statistics to more complex approaches such as regression analysis and tests for mean comparison. Aspiring survey researchers would find this course very helpful because it emphasizes hands-on learning using SPSS which will provide the necessary skills for interpreting the data collected.

See salaries and explore the career path for Survey Researcher

Statistician

A Statistician designs and conducts statistical studies and analyzes data to solve real-world problems. This role requires in-depth knowledge of statistical methods and proficiency in software like SPSS. This course helps a statistician to further their understanding of data analysis techniques and statistical outputs, with its focus on practical application and interpretation of results. It is useful for aspiring statisticians because it provides hands-on experience with a broad range of statistical procedures, from basic descriptive statistics to more complex methods like multidimensional scaling and cluster analysis, all within the SPSS platform.

See salaries and explore the career path for Statistician

Healthcare Analyst

A Healthcare Analyst examines healthcare data to improve patient outcomes and operational efficiency. This role requires expertise in data management, statistical analysis, and reporting. This course greatly benefits a healthcare analyst in their career as it offers a comprehensive approach to statistical analysis using SPSS, covering data cleaning, descriptive statistics, hypothesis testing, and various advanced analytical techniques. Healthcare analysts would find the course especially helpful due to its emphasis on practical application, which can help professionals make data driven decisions and recommendations.

See salaries and explore the career path for Healthcare Analyst

Evaluation Specialist

An Evaluation Specialist assesses the effectiveness of programs and policies using quantitative methods. This role requires strong statistical analysis skills. This course may help an evaluation specialist, as it is designed to provide an in-depth knowledge of statistical analysis with SPSS, covering everything from basic data operations to advanced methods like logistic regression and multidimensional scaling. The course will help evaluation specialists perform quantitative analysis and understand the results when assessing program effectiveness through the use of its various procedures.

See salaries and explore the career path for Evaluation Specialist

Business Analyst

A Business Analyst uses data to evaluate business processes and identify areas for improvement. This role often requires the use of statistical analysis software for data-driven decision-making. This course develops skills needed by a business analyst for data analysis, including hypothesis testing, regression analysis, and data visualization. This course is beneficial to those interested in this career because it provides hands-on instruction in SPSS, a popular tool in business analytics, and emphasizes the practical application of statistical methods without relying heavily on mathematical formulas and jargon, which is perfect for a business-oriented analyst.

See salaries and explore the career path for Business Analyst

Psychometrician

A Psychometrician develops and validates psychological assessments, requiring knowledge of statistical methods. This role also requires the ability to work with data and understand statistical output. This course will benefit a psychometrician through its comprehensive training in SPSS, helping to master skills in data manipulation, reliability testing, and data reduction, which are all key to developing and validating tests. This course is particularly beneficial to someone interested in becoming a psychometrician due to its focus on practical application of statistical techniques in SPSS.

See salaries and explore the career path for Psychometrician

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical methods to analyze financial data and develop models. The work of a quantitative analyst involves not only mathematical aptitude, but also statistical software skills. This course on SPSS may be useful to a quantitative analyst by enhancing skills for data cleaning, transformation, and statistical testing, all within a familiar software environment. This is helpful for professionals in the field who need to work with large datasets and apply analytic techniques, as the course offers practical instruction on a broad range of methods for data analysis that, while not solely finance-focused, are nonetheless valuable in quantitative analysis.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical methods to optimize business processes. This role involves data analysis, modeling, and interpretation of results. This course may be useful to an operations research analyst as it focuses on using SPSS to perform a wide range of statistical procedures, from basic data handling to advanced techniques such as multidimensional scaling and cluster analysis. This course can help you in your journey to become an analyst by providing practical experience in statistical analysis in a software environment that is used across many industries for optimization, logistics, and decision-making.

See salaries and explore the career path for Operations Research Analyst

Policy Analyst

A Policy Analyst researches and evaluates public policies using data-driven methods. This role often involves statistical analysis to understand the impact of various policies. This course may be useful to a policy analyst by offering a deep dive into various statistical techniques using SPSS, such as regression analysis, variance analysis, and hypothesis testing. While a policy analyst could benefit from various courses, the accessible structure of this course, which avoids technical jargon and uses a hands-on approach, make it particularly conducive to policy evaluation.

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Data Scientist

A Data Scientist uses a combination of statistical techniques, machine learning, and programming to extract insights and knowledge from large data sets. This role often requires proficiency in statistical software. This course might assist a data scientist as it introduces many of the fundamental statistical analysis techniques including regression, ANOVA, and data reduction methods. The strength of this course for a data scientist is its approach of hands-on use of SPSS, demonstrating how to apply essential statistical methods in a practical way, which are sometimes overlooked in programming heavy data science programs.

See salaries and explore the career path for Data Scientist

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in SPSS For Research.

Discovering Statistics Using JASP

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Comprehensive guide to using SPSS for statistical analysis. It covers a wide range of statistical techniques, from basic descriptive statistics to advanced multivariate methods. It provides clear explanations of the underlying statistical concepts and step-by-step instructions on how to perform the analyses in SPSS. This book is commonly used as a textbook at academic institutions and useful reference tool for researchers and students alike. It adds more depth and breadth to the existing course.

Discovering Statistics Using JASP

Paperback

$$$$

Discovering Statistics Using JASP

Kindle Edition

$$$$

Statistics without Tears

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Provides a gentle introduction to statistical concepts, making it ideal for those without a strong mathematical background. It explains complex ideas in a clear and accessible way, focusing on understanding the logic behind statistical tests rather than the mathematical formulas. It is particularly useful for building a solid foundation before diving into SPSS-specific procedures. This book is more valuable as additional reading than as a current reference.

Statistics without Tears: An Introduction for Non...

Paperback

SPSS For Research

What's inside

Learning objectives

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