75 | 
78 |
79 | Content not found. Please use links in the navbar.
80 |
81 | 
17 |
18 |
19 | [](https://github.com/markolalovic/latent2likert/actions/workflows/R-CMD-check.yaml)
20 | [](https://app.codecov.io/gh/markolalovic/latent2likert)
21 | [](https://CRAN.R-project.org/package=latent2likert)
22 | [](https://cranlogs.r-pkg.org/badges/latent2likert)
23 |
24 |
25 | ## Overview
26 |
27 | The **latent2likert** package is designed to effectively simulate the discretization process inherent to Likert scales while minimizing distortion. It converts continuous latent variables into ordinal categories to generate Likert scale item responses. This is particularly useful for accurately modeling and analyzing survey data that use Likert scales, especially when applying statistical techniques that require metric data.
28 |
29 |
30 | ## Installation
31 |
32 | You can install the released version from CRAN:
33 |
34 | ```{r, eval = FALSE}
35 | install.packages("latent2likert")
36 | ```
37 |
38 | Or the latest development version from GitHub:
39 | ``` r
40 | devtools::install_github("markolalovic/latent2likert")
41 | ```
42 |
43 | ## Dependencies
44 |
45 | To keep the package lightweight, **latent2likert** only imports [mvtnorm](https://cran.r-project.org/package=mvtnorm), along with the standard R packages stats and graphics, which are typically included in R releases. An additional suggested dependency is the package [sn](https://cran.r-project.org/package=sn), which is required only for generating random responses from correlated Likert items based on a multivariate skew normal distribution. The package prompts the user to install this dependency during interactive sessions if needed.
46 |
47 | ## Features
48 |
49 | * `rlikert`: Generates random responses to Likert scale questions based on specified means and standard deviations of latent variables, with optional settings for skewness and correlations.
50 | * `estimate_params`: Estimates latent parameters from existing survey data.
51 |
52 |
53 | ## Structure
54 |
55 | 
57 | 
125 |
5 |
6 |
7 |
8 | [](https://github.com/markolalovic/latent2likert/actions/workflows/R-CMD-check.yaml)
9 | [](https://app.codecov.io/gh/markolalovic/latent2likert)
10 | [](https://CRAN.R-project.org/package=latent2likert)
12 | [](https://cranlogs.r-pkg.org/badges/latent2likert)
13 |
14 |
15 | ## Overview
16 |
17 | The **latent2likert** package is designed to effectively simulate the
18 | discretization process inherent to Likert scales while minimizing
19 | distortion. It converts continuous latent variables into ordinal
20 | categories to generate Likert scale item responses. This is particularly
21 | useful for accurately modeling and analyzing survey data that use Likert
22 | scales, especially when applying statistical techniques that require
23 | metric data.
24 |
25 | ## Installation
26 |
27 | You can install the released version from CRAN:
28 |
29 | ``` r
30 | install.packages("latent2likert")
31 | ```
32 |
33 | Or the latest development version from GitHub:
34 |
35 | ``` r
36 | devtools::install_github("markolalovic/latent2likert")
37 | ```
38 |
39 | ## Dependencies
40 |
41 | To keep the package lightweight, **latent2likert** only imports
42 | [mvtnorm](https://cran.r-project.org/package=mvtnorm), along with the
43 | standard R packages stats and graphics, which are typically included in
44 | R releases. An additional suggested dependency is the package
45 | [sn](https://cran.r-project.org/package=sn), which is required only for
46 | generating random responses from correlated Likert items based on a
47 | multivariate skew normal distribution. The package prompts the user to
48 | install this dependency during interactive sessions if needed.
49 |
50 | ## Features
51 |
52 | - `rlikert`: Generates random responses to Likert scale questions based
53 | on specified means and standard deviations of latent variables, with
54 | optional settings for skewness and correlations.
55 | - `estimate_params`: Estimates latent parameters from existing survey
56 | data.
57 |
58 | ## Structure
59 |
60 |
63 |
160 | 
YEAR: 2024 54 | COPYRIGHT HOLDER: Marko Lalovic 55 |56 | 57 |
Copyright (c) 2024 Marko Lalovic
56 |Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
57 |The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
58 |THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
59 |
Marko Lalovic. Author, maintainer. 57 |
58 |Source: DESCRIPTION
Lalovic M (2024). 66 | latent2likert: Converting Latent Variables into Likert Scale Responses. 67 | R package version 1.2.2, https://latent2likert.lalovic.io/. 68 |
69 |@Manual{,
70 | title = {latent2likert: Converting Latent Variables into Likert Scale Responses},
71 | author = {Marko Lalovic},
72 | year = {2024},
73 | note = {R package version 1.2.2},
74 | url = {https://latent2likert.lalovic.io/},
75 | }
76 | rlikert() function.estimate_params() function for estimating parameters from existing survey data.responsesR to latent2likert to better reflect its purpose of converting latent variables into Likert scale responses.CRAN release: 2024-06-24
84 |R/estimation.R
53 | estimate_mean_and_sd.RdEstimates the mean and standard deviation of a latent variable given the 58 | discrete probabilities of its observed Likert scale responses.
59 |named vector of probabilities for each response category.
number of response categories for the Likert scale item.
marginal skewness of the latent variable, defaults to 0.
tolerance for convergence, defaults to 1e-6.
maximum number of iterations, defaults to 100.
A numeric vector with two elements: the estimated mean and 93 | standard deviation.
94 |This function uses an iterative algorithm to solve the system of non-linear 98 | equations that describe the relationship between the continuous latent 99 | variable and the observed discrete probability distribution of Likert scale 100 | responses. The algorithm ensures stability by reparameterizing the system 101 | and applying constraints to prevent stepping into invalid regions.
102 |prob <- c("1" = 0.313, "2" = 0.579, "3" = 0.105, "4" = 0.003)
107 | # returns estimates that are close to the actual mean and sd: c(-1, 0.5)
108 | estimate_mean_and_sd(prob, 5)
109 | #> [1] -1.0025695 0.5004155
110 |
111 | discretize_density()
66 | estimate_mean_and_sd()
71 | estimate_params()
76 | part_bfi
81 | plot_likert_transform()
86 | response_prop()
91 | rlikert()
96 | simulate_likert()
101 | Plots the densities of latent variables and the corresponding 58 | transformed discrete probability distributions.
59 |number of Likert scale items (questions).
number of response categories for each Likert item. 74 | Integer or vector of integers.
means of the latent variables. 79 | Numeric or vector of numerics. Defaults to 0.
standard deviations of the latent variables. 84 | Numeric or vector of numerics. Defaults to 1.
marginal skewness of the latent variables. 89 | Numeric or vector of numerics. Defaults to 0.
plot_likert_transform(n_items = 3, n_levels = c(3, 4, 5))
102 | 
103 | plot_likert_transform(n_items = 3, n_levels = 5, mean = c(0, 1, 2))
104 | 
105 | plot_likert_transform(n_items = 3, n_levels = 5, sd = c(0.8, 1, 1.2))
106 | 
107 | plot_likert_transform(n_items = 3, n_levels = 5, skew = c(-0.5, 0, 0.5))
108 | 
109 | Returns a table of proportions for each possible response category.
56 |numeric vector or matrix of responses.
number of response categories.
data <- c(1, 2, 2, 3, 3, 3)
83 | response_prop(data, n_levels = 3)
84 | #> 1 2 3
85 | #> 0.1666667 0.3333333 0.5000000
86 |
87 | data_matrix <- matrix(c(1, 2, 2, 3, 3, 3), ncol = 2)
88 | response_prop(data_matrix, n_levels = 3)
89 | #> Response
90 | #> Item 1 2 3
91 | #> [1,] 0.3333333 0.6666667 0
92 | #> [2,] 0.0000000 0.0000000 1
93 | 
73 | 
139 | 
212 |