Package: LikertMakeR 2.3.0

Purpose | Motivation | Rating scale properties | Rating scales have bounds and discrete measurement intervals | A single 1-5 rating scale is NOT a Likert scale - it may be an Likert-scale item. | Most rating scales are skewed | LikertMakeR functions | Using LikertMakeR | Download and Install LikertMakeR | from CRAN | development version from GitHub. | Generate synthetic rating-scale data | lfast() | lfast() example | a four-item, five-point Likert scale | an 11-point likelihood-of-purchase scale | Correlating rating scales | lcor() | lcor() example | Generate a correlation matrix from Cronbach's Alpha | makeCorrAlpha() | makeCorrAlpha() examples | Four variables, alpha = 0.85 | test output with Helper functions | makeCorrAlpha() with diagnostics output | diagnostics output | Generate a correlation matrix from factor loadings | makeCorrLoadings | makeCorrLoadings() usage | makeCorrLoadings() arguments | Note | makeCorrLoadings() examples | Typical application from published EFA results | define parameters | Apply the function | Test makeCorrLoadings() output | Assuming orthogonal factors | Test orthogonal output | Generate a dataframe of rating scales from a correlation matrix and predefined moments | makeScales() | makeScales() examples | makeScales() example #1. four correlated items | Structure of new dataframe | Means should be correct to two decimal places | Correlations should be correct to two decimal places | makeScales() example #2. four Likert scales | Generate a dataframe from Cronbach's Alpha and predefined moments | Step 1: Generate a correlation matrix | Step 2: Generate dataframe | Summary plots of new dataframe | Generate a dataframe of rating-scale items from a summated rating scale | makeItemsScale() | makeItemsScale() Example: | create items with makeItemsScale() | makeItemsScale() with same summated values and higher alpha | Create a dataframe for a t-test | Independent-samples t-test | makePaired() paired-sample t-test | makePaired() examples | check properties of new data | run a paired-sample t-test with the new data | Create a dataframe for Repeated-Measures ANOVA | makeRepeated() | makeRepeated() usage | makeRepeated() arguments | makeRepeated() examples | Generate rating-scale data from multiple regression results | makeScalesRegression() | makeScalesRegression() usage | makeScalesRegression() arguments | makeScalesRegression() examples | Example 1: With provided IV correlation matrix | Example 2: With optimisation (no IV correlation matrix) | Create a multidimensional dataframe of correlated scale items | correlateScales() | correlateScales() examples | three attitudes and a behavioural intention | create dataframes of Likert-scale items | check properties of item dataframes | correlateScales parameters | apply the correlateScales() function | plot the new correlated scale items | Check the properties of our derived dataframe | Helper functions | alpha() | alpha() examples | eigenvalues() | eigenvalues() examples | eigenvalues() function with optional scree plot | reliability() | reliability() examples | Alternative methods & packages | sampling from a truncated normal distribution | sampling with a predetermined probability distribution | marginal model specification | Factor Models: Classical Test Theory (CTT) | References

Reliability estimation with LikertMakeR::reliability() | When should you use reliability()? | Function usage | Arguments | data | include | ci | ci_level | n_boot | na_method | min_count | digits | verbose | Reliability coefficients returned | Pearson-based coefficients (always available) | Ordinal (polychoric-based) coefficients | Ordinal diagnostics and safeguards | Hierarchical reliability: $\omega_h$ (Coefficient H) | Why no confidence intervals for $\omega_h$? | Examples | Create a synthetic dataset | Basic reliability estimates | Including additional coefficients | When should I use each option? | Notes on computation | Choosing a Reliability Coefficient: A Practical Decision Guide | Step 1: What kind of data do you have? | Continuous or approximately continuous items | Ordinal (Likert-type) items | Step 2: Choosing between $\alpha$ and $\omega$ | Cronbach’s alpha ($\alpha$) | McDonald’s omega ($\omega$) | Where does Guttman’s $\lambda_6$ fit? | Step 3: When should I use ordinal reliability? | Step 4: $\alpha$ vs $\omega$ vs ordinal $\omega$ — a practical summary | Step 5: Confidence intervals | Recommended reading | Understanding Cronbach’s alpha and its limitations | Omega and factor-based reliability | Comparative studies | Ordinal reliability for Likert-type data | Polychoric correlations in practice | Teaching tip | Citations