The Ellen Clarke Bertrand Library

Data: Analyzing and Visualizing Data

Quantitative Data: References

Quantitative Data: Tools and Resources

Open statistical software (free to download and use):

jamovi

jamovi is a free and open alternative to costly statistical products such as SPSS and SAS. 

  • Provides a complete suite of analyses for quantitative data analyses, including t-tests, ANOVAs, correlation and regression, non-parametric tests, contingency tables, reliability and factor analysis.

  • Additional analyses contributed by the experts can be found in the jamovi library

  • A fully functional spreadsheet, immediately familiar to anyone. Enter, copy/paste data, filter rows, compute new values, perform transforms across many columns at once – jamovi provides a streamlined spreadsheet experience, optimised for statistical data.

  • Built on top of the R programming language, and the underlying R syntax for each analysis is made available through the syntax mode.

Get started using jamovi with these resources:

  • jamovi user's guide

  • jamovi textbook: Navarro DJ and Foxcroft DR (2019). learning statistics with jamovi: a tutorial for psychology students and other beginners. (Version 0.70). DOI: 10.24384/hgc3-7p15

  • jamovi video tutorials from datalab.cc

  • Introduction to jamovi (LinkedIn Learning). An online course on LinkedIn Learning with transcripts, exercise files, and self-assessment quizzes.Learn how to install jamovi and third-party modules, navigate jamovi, import and wrangle data, create visualizations based on ggplot2, analyze data using t-tests, analysis of variance (ANOVA), linear and binomial logistic regression, log-linear regression, and conduct exploratory factor analysis. Plus, see how to share your work with unified files and collaborate with the Open Science Framework (OSF). Length: 4 hours, 41 minutes. (Free access with Bucknell login.)


Proprietary statistical software (purchase or subscription required):

SPSS

SAS

STATA


Software available at Bucknell

A range of software programs is available to Bucknell students, faculty, staff, and retirees. Depending on the license, some software may be available on a Bucknell owned computer, in a classroom or lab, virtually through a web or remote interface, and/or via download to a personally owned computer. Please see this L&IT Help Page for a list and more information.

For assistance and questions about the available software, please submit a Tech Ticket here.

Bucknell Remote Labs

Bucknell University Remote Labs (VMware Horizon) is a virtual computer lab that allows you to use software without installing it on your computer. You can connect to BU Remote Labs from your personal Linux, Mac, or Windows computer. Please see this L&IT Help Page for more information.

For assistance and questions about Bucknell Remote Labs, please submit a Tech Ticket here.

Quantitative Data: Keep Learning

Basic Statistics (Coursera)

Understanding statistics is essential to research in the social and behavioral sciences. The course starts with descriptive statistics. You will learn about cases and variables, computing measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance), as well as assessing relationships between variables, including via correlation and regression. Next, we will discuss probability: calculating probabilities, probability distributions, and sampling distributions. And finally, in the third part of the course, you will learn about inferential statistics, which help us decide whether the patterns we see in our sample data are strong enough to draw conclusions about the underlying population. We will also discuss confidence intervals and significance tests. 

Length: 8 weeks (self-paced).

 

Inferential Statistics (Coursera)

Inferential statistics are concerned with making inferences from the relations found in the sample to those in the population. We will start with significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Next, we will consider a range of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test, we will consider how it works, for what data and design it is appropriate and how results should be interpreted. We will look at z-tests for 1 and 2 proportions, McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test).

Length: 8 weeks (self-paced).

 

SPSS for Academic Research (LinkedIn Learning)

Learn to use SPSS to run common statistical tests such as one-sample t-test, paired-sample t-test, analysis of variance (ANOVA), and repeated-measures ANOVA. Review the tenants of hypothesis testing, including the central theorem, P values and confidence intervals, and specific assumptions and use cases for each test. 

Length: 2 hours, 40 minutes.

 

Academic Research Foundations: Quantitative (LinkedIn Learning)

The course explores the foundations, types, and main characteristics of quantitative research with a focus on social science research, and explains key steps in the research process, including: generating research questions, conducting literature review, research ethics, collecting data, and presenting and interpreting your results. 

Length: 1 hour, 40 minutes. (Free access with Bucknell login.)

 

Statistics Foundations 1 (LinkedIn Learning)

The course covers statistics basics, including: calculating averages, medians, modes, and standard deviations; calculating z-scores; using probability and distribution curves to inform decisions; and detecting detect false positives and misleading data. Each concept is covered in simple language, with detailed examples that show how statistics are used in real-world scenarios from the worlds of business, sports, education, entertainment, and more.

Length: 2 hours. (Free access with Bucknell login.)

 

Statistics Foundations 2 (LinkedIn Learning)

Building on Statistics Foundations 1 course, this course provides a practical, example-based overview of the intermediate skills associated with statistics: samples and sampling, confidence intervals, and hypothesis testing. The topics include: sampling, random samples, sample sizes, sampling error and trustworthiness; the central unit theorem; t-distribution; confidence intervals (including explaining unexpected outcomes); and hypothesis testing. 

Length: 2 hours. (Free access with Bucknell login.)

 

Statistics Foundations 3 (LinkedIn Learning)

Part 3 in the Statistics Foundations series, this course moves learners into the practical study and application of experimental design, analysis of variance (ANOVA), population comparison, and regression analysis. Covers concepts such as working with small sample sizes, t-distribution, degrees of freedom, chi-square testing, ANOVA testing, and regression testing. 

Length: 1 hour, 40 minutes. (Free access with Bucknell login.)

 

SPSS Statistics Essential Training (LinkedIn Learning)

SPSS is a statistics and data analysis program for businesses, governments, research institutes, and academic organizations. Learn how to use SPSS to analyze and visualize your data, including: importing or entering data, creating new variables, calculating descriptive statistics and inferential statistics, modeling associations with correlations, contingency tables, and multiple-regression analysis; creating charts, scatterplots, and box plots to share your results. Plus, learn how to extend the power of SPSS with Python and R. 

Length: 5 hours. (Free access with Bucknell login.)

 

R Statistics Essential Training (LinkedIn Learning)

R is the language of big data—a statistical programming language that helps describe, mine, and test relationships between large amounts of data. Learn how to use R to model statistical relationships using graphs, calculations, tests, and other analysis tools. Learn how to enter and modify data; create charts, scatter plots, and histograms; examine outliers; calculate correlations; and compute regressions, bivariate associations, and statistics for three or more variables. Challenge exercises with step-by-step solutions allow you to test your skills as you progress.

Length: 6 hours. (Free access with Bucknell login.)

 

SAS Essential Training: 1 Descriptive Analysis for Healthcare Research (LinkedIn Learning)

After decades on the scene, SAS remains one of the industry leaders in the world of big data. Learn how to conduct a descriptive analysis of a health survey dataset and present the results in plots and tables. Topics include: preparing hypotheses and research aims; importing a dataset from SAS *.xpt format using the XPORT command; editing datasets to add new categorical and continuous variables; conducting chi-square tests, t-tests, and analyses of variance (ANOVAs); generating bar, pie, and scatter plots, as well as tables with descriptive analysis results.

Length: 4 hours. (Free access with Bucknell login.)

 

SAS Essential Training: 2 Regression Analysis for Healthcare Research (LinkedIn Learning)

Learn how to use SAS to conduct a hypothesis-driven linear and logistic regression analysis of a health survey data, and how to interpret and present your regression model results.

Length: 3.5 hours. (Free access with Bucknell login.)

 

Data Services Specialist

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Agnes Jasinska
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