Complete Introduction in Statistics in R, SPSS, JASP

Explore why we engage in statistics and quantitative research, define the field, and examine paradigm, meta theory, deductive logic, and inference from a target population via surveys.

Explain the paradigm underlying research, focusing on neo positivism, the idea of a single reality perceived with bias, and probabilistic agreement in quantitative statistics.

Frame statistics as a quantitative approach to collecting and analyzing numbers, an applied form of mathematics with distinct skills from general mathematics.

Explore how statistics help you make informed decisions with data, conduct and interpret research transparently, and understand the role of statistics in society and politics.

Apply a deductive, theory-first approach to statistics by defining a statistical model and operationalizing concepts. Collect data to confirm hypotheses, assess measurement validity and reliability, and interpret results for reporting.

Analyze how statistics aims to infer the population from a sample. Understand the population, sample, and inference loop that drives cost-efficient election forecasts.

Statistics clarifies data to improve decision making with evidence-based inferences. It emphasizes deductive logic, theory first, and translating sample findings back to the population.

Open R Studio to see that R is a statistical programming language with a non gui interface, and build reusable code blocks for research.

Visit the provided URLs to install both R and RStudio, with R first and RStudio on top for an easier workflow.

Explore the RStudio interface, including the console, environment pane, files pane, and script panes, and learn to run code, store variables, and manage projects.

Create a new project in our studio for this statistics course and build a tidy folder structure with data, scripts, and output, then import data into the data folder.

Load data into R from CSV and Excel in RStudio using base functions like read.csv, and store it as a data frame with proper headers and delimiters.

Learn how to install and load R packages, using install.packages and library, and explore tidyverse as a comprehensive package for data manipulation in data science.

Learn to install and load packages in R, using the packages tab to search for and install libraries like tidyverse and ACM, and load them for structural equation modeling.

Learn to load and manipulate data in R using data frames, dollar sign and square brackets for selection, and c() to combine data, with tidyverse piping to save reproducible results.

Form row means to compute scale item means and recode data, including collapsing categories and reverse coding. Address missing data with na.rm in R.

Explore IBM SPSS Statistics graphical user interface, load data from CSV or Excel, and use the single-dataset option, syntax, and paste workflow to run analyses and view outputs.

Explore data manipulation in SPSS by using compute and recode: create mean scales from items and remap values with a new variable to avoid overwriting original data.

Define core statistical terms, identify variables and cases as dataset ingredients, differentiate latent and manifest variables, articulate hypotheses, and discuss validity criteria for sound statistical research.

Understand why statistics use abstract terminology and a common vocabulary to describe data, cases as data points, and how this shared language enables clearer communication across statistics courses.

Explore how variables and cases structure a data set as a simple table, with rows as cases and columns as variables, where each cell holds an attribute.

Explore manifest and latent variables, and learn how scales and item batteries capture multifaceted traits like personality, improving accuracy, reducing measurement error, and guiding data cleaning before analysis.

Explore hypotheses as credible, theory-first assumptions about relationships between variables, then test them via deductive logic against observations in statistics.

Explore statistical models by identifying hypotheses, distinguishing predictors as independent variables from outcome variables as dependent variables, and noting mediators, moderators, and indirect effects that shape relationships.

Explore the three validity requirements for quantitative research—objectivity, validity, and reliability—and learn to assess measurement validity, ensure reliable measurements, and maintain consistency across respondents.

Define the standard data sheet structure with rows, columns, and variables, and explain measurement through scales. Compare hypotheses, research questions, and core validity, reliability, and objectivity criteria in quantitative research.

Explore data collection by linking a population to a sample and identifying ideal sample size. Review data collection methods, levels of measurement, reliability, and factor analysis.

Explore how sampling connects the population to the sample and enables inference. Compare random, quota, and convenience sampling, uncovering biases and practical challenges using real-world examples.

Explore errors in statistical research, including unsystematic random error and broader systematic error, and illustrate survivorship bias with the World War II bomber example to emphasize representative sampling.

Explore data collection methods in social science, from asking people through surveys and interviews to observations and video data, plus secondary data from existing reports.

Assess how sample size influences the precision of estimates and data collection costs. Apply criteria based on model complexity and expected effect size, using the rule of thumb for cases.

Explore the levels of measurement—nominal, ordinal, and interval/ratio—and learn how data quality and test selection hinge on the level, with examples like Likert scales and age.

Understand that a survey is a collection of items used to measure a concept, prioritizing clear, valid, closed questions and the reuse of established instruments.

Explore Cronbach's alpha as a measure of internal consistency in scale development, examining inter-item correlations. Aim for alpha above 0.7 and assess how removing items affects reliability, using theory-guided checks.

Explore reliability analysis in R to assess internal consistency of a scale using alpha, examine item correlations, and consider reverse coded items when interpreting results.

Perform reliability analysis in SPSS to test the internal consistency of a scale using alpha and explore how deleting an item alters the alpha value.

Explore exploratory factor analysis (efa) to uncover the factor structure in scale development, estimate and rotate factors, and interpret factor loadings for uni-dimensional measurements.

Explore exploratory factor analysis in R to reveal structure in data, using extraction and rotation to show how items load on two factors.

Conducts exploratory factor analysis in SPSS to identify a three-factor structure for learning-outcome measures, using extraction and rotation, evaluating loadings and cross-loadings, and emphasizing theory-driven decisions.

Explore sampling procedures for unbiased, representative samples, data collection methods (surveys, interviews, observations), and levels of measurement to guide reliability analysis and exploratory factor analysis.

Describe a data sample using descriptive statistics, focusing on location and variation, explore the normal distribution, visualize data, and apply these techniques in R, SPSS, and JASP.

Explore descriptive statistics focusing on location and variation, using mode, median, and mean, plus outliers, range, interquartile range, and variance to describe a sample.

Explore the normal distribution, its bell-shaped curve, and why statistical procedures assume normality for a single variable to simplify analyses and reflect real-world data.

Explore the normal distribution and how data distribution underpins many statistical procedures, highlighting why assumptions of normality simplify calculations and reflect real-world phenomena, with an empirical bell-shaped example in R.

Master descriptive statistics in R by creating a sample data vector, setting a seed, and computing mean, median, mode, range, quartiles, variance, and standard deviation.

Create a script named normality using Ah Norm to generate a normal distribution, specify end, and visualize it with a histogram; larger samples resemble the normal curve more.

Create quick visualizations in R with base functions to generate histograms, box plots, and bar charts, then group by C one and summarise the mean of X1.

Learn to compute descriptive statistics in SPSS using frequencies, descriptive statistics, and explore across groups, including mean, median, mode, standard deviation, and standard error of the mean.

Describe sample data using descriptive statistics, focusing on mean for location and variation measures like range and standard deviation, and assess normality and visualize distributions for quick insight.

Describe two leadership scales with descriptive statistics, reverse code the full scale, compute new variables, explore normality, and assess reliability.

Explore inferential statistics by linking samples to populations through estimation and confidence levels, and learn the logic of hypothesis testing, including p-values and related terms.

Learn how to estimate population means from non-biased samples using the standard error of the mean and confidence intervals; see how sample size and variability affect precision, supported by simulations.

Frame a hypothesis as the null versus the alternative, assess data with a p value and a 5% alpha, and conclude whether evidence supports a difference.

Understand inferential statistics by exploring estimation from samples to populations, confidence, hypotheses, p-values, and the distinction between statistical and practical significance.

Explore testing for differences between two groups using the two-sample t-test, then extend to analysis of variance (ANOVA), with applied steps in R, SPSS, and JASP.

Explore the t-test family, including one-sample and two-sample tests with metric outcomes and grouping, and learn about normality, equal variances, p values, and alternatives like Mann–Whitney U test.

Learn to run a one-sample t test in R with clean, reproducible scripts, form a hypothesis, interpret t and p values, and check normality with a histogram and test.

Perform a one-sample t-test in SPSS to compare a sample mean to a test value, interpret the t and p values, and verify normality with the Shapiro-Wilk test.

Run a two independent samples t test in R with two groups, and assess normality with Shapiro and variance homogeneity with Levene.

Learn to apply the Welch t-test in R by adapting the independent t-test script for unequal variances, while preserving normality and skipping the Levine test.

Learn to run the Mann-Whitney U-test in R using the Wilcox test as a nonparametric alternative to t tests when normality is violated, and interpret the non-significant p-value.

Learn how to run an independent samples t-test in SPSS to compare two groups, check variances with Levene’s test, consider Welch correction, and switch to Mann-Whitney U if normality fails.

Run a paired t test in R to compare dependent pretest and post measurements, specify paired = true, and interpret t and p values while checking normality for nonparametric alternatives.

Learn to run a two paired samples t-test in SPSS on two variables across cases, interpret the t value and p significance, and compare with nonparametric alternatives for nonnormal data.

Perform an independent samples t-test to compare l0 SGP by gender on job performance, verify homogeneity of variances and normality, and use the Mann-Whitney test when normality fails.

Use ANOVA to test differences across multiple groups for a metric outcome with nominal grouping factor. Check assumptions; interpret F and P values; apply post hoc tests to control alpha.

Demonstrates performing anova in R, treating group variables as factors, obtaining a tidy anova summary, conducting pairwise tests with bonferroni correction, and checking normality and homogeneity assumptions.

Explore the alternative to analysis of variance in R, using cross call test for ordinal or non-normal outcomes and flexible data input via formula notation.

Conduct a one-way ANOVA in SPSS to compare Y2 across C1 groups and interpret the F value and Bonferroni post-hoc results. Check Levene's test and Shapiro-Wilk; use nonparametric alternatives.

Analyze objective job performance across three age groups with a one-way ANOVA, include Bonferroni post hoc and homogeneity of variance checks, and use a non-parametric test if assumptions fail.

Explore testing for differences with t tests and nonparametric alternatives like the Mann-Whitney U test, and cover one-sample t test, anova, alpha inflation, post-hoc tests, and Levene's test.

Explore testing relationships through correlation and regression, cover theoretical concepts, then apply these methods directly in software packages.

Explore Pearson correlation for relationships between metric variables, interpret direction, strength, and p-value, view scatterplots, and use Spearman's rank correlation for non-normal or ordinal data.

Explore how to run and interpret Pearson and Spearman correlations in R, using the psych package, with emphasis on normality testing via the Shapiro test and interpreting p-values.

Learn to perform and interpret bivariate correlations in SPSS, comparing Pearson and Spearman results, assessing R values and p-values, and checking normality to decide when to use nonparametric methods.

Test the correlation between workplace climate and learning approach. The exercise finds a statistically significant, weak positive relationship (around 0.3), consistent across Spearman and Pearson analyses.

Explore regression basics with ordinary least squares, including simple and multiple regression, model fit metrics like R-squared and p-values, and interpreting coefficients, direction, and significance.

Learn to run simple and multiple linear regression in R, interpret R squared and F statistics, obtain standardized coefficients, and check residual normality and multicollinearity with the variance inflation factor.

Explore simple and multiple regression in SPSS, interpreting model fit with R-squared and F, examining coefficients and diagnostics, including Durbin-Watson, VIF, and residual plots.

Use multiple regression to explain career development differences, with pcdi as the dependent variable and predictors including LA city and LA sd, assessing model fit, coefficients, VIFs, and residual normality.

Explore testing for relationships using correlations (including Pearson's r and Spearman's rho) and regressions to assess direction, effect size, and p-values. Verify assumptions to ensure test validity in your dataset.