Results

Latent Profile Analysis

_____________________________________________________________________________________________

1. tidyLPA R package is described in the page.

2. Four models(1,2,3,6) are specified using mclust R package.

3. Person membership will be shown in the datasheet.

4. Feature requests and bug reports can be made on the GitHub.

_____________________________________________________________________________________________

Model fit
		Values
Model		1.00000
Classes		2.00000
LogLik		-972.86681
AIC		1965.73362
AWE		2068.40351
BIC		1993.00750
CAIC		2003.00750
CLC		1947.61149
KIC		1978.73362
SABIC		1961.40241
ICL		-1996.03503
Entropy		0.93893
prob_min		0.98298
prob_max		0.99219
n_min		0.47788
n_max		0.52212
BLRT_val		53.99260
BLRT_p		0.00990

[3]

Class comparison
Class	Model	LogLik	AIC	AWE	BIC	CAIC	CLC	KIC	SABIC	ICL	Entropy
1	1	-1000	2012	2072	2028	2034	2002	2021	2009	-2028	1.000
2	1	-973	1966	2068	1993	2003	1948	1979	1961	-1996	0.939

[3]

Estimates
	Category	Parameter	Estimate	SE	p	Class	Model	Classes
1	Means	E_1	5.08	0.553	< .001	1	1	2
2	Means	E_2	1.35	0.192	< .001	1	1	2
3	Means	E_3	5.79	0.773	< .001	1	1	2
4	Variances	E_1	15.28	1.784	< .001	1	1	2
5	Variances	E_2	3.86	0.528	< .001	1	1	2
6	Variances	E_3	27.86	2.709	< .001	1	1	2
7	Means	E_1	4.24	0.518	< .001	2	1	2
8	Means	E_2	9.68	0.343	< .001	2	1	2
9	Means	E_3	3.09	0.763	< .001	2	1	2
10	Variances	E_1	15.28	1.784	< .001	2	1	2
11	Variances	E_2	3.86	0.528	< .001	2	1	2
12	Variances	E_3	27.86	2.709	< .001	2	1	2

[3]

Correlation plot for a mixture model

Latent profile plot

[3]

Density plot

[3]

Elbow plot

[4]

[4]

Latent Profile Analysis

_____________________________________________________________________________________________

1. tidyLPA R package is described in the page.

2. Four models(1,2,3,6) are specified using mclust R package.

3. Person membership will be shown in the datasheet.

4. Feature requests and bug reports can be made on the GitHub.

_____________________________________________________________________________________________

Model fit
		Values
Model		1.00000
Classes		2.00000
LogLik		-972.86681
AIC		1965.73362
AWE		2068.40351
BIC		1993.00750
CAIC		2003.00750
CLC		1947.61149
KIC		1978.73362
SABIC		1961.40241
ICL		-1996.03503
Entropy		0.93893
prob_min		0.98298
prob_max		0.99219
n_min		0.47788
n_max		0.52212
BLRT_val		53.99260
BLRT_p		0.00990

[3]

Class comparison
Class	Model	LogLik	AIC	AWE	BIC	CAIC	CLC	KIC	SABIC	ICL	Entropy
1	1	-1000	2012	2072	2028	2034	2002	2021	2009	-2028	1.000
2	1	-973	1966	2068	1993	2003	1948	1979	1961	-1996	0.939

[3]

Estimates
	Category	Parameter	Estimate	SE	p	Class	Model	Classes
1	Means	E_1	5.08	0.505	< .001	1	1	2
2	Means	E_2	1.35	0.147	< .001	1	1	2
3	Means	E_3	5.79	0.754	< .001	1	1	2
4	Variances	E_1	15.28	1.713	< .001	1	1	2
5	Variances	E_2	3.86	0.408	< .001	1	1	2
6	Variances	E_3	27.86	3.071	< .001	1	1	2
7	Means	E_1	4.24	0.554	< .001	2	1	2
8	Means	E_2	9.68	0.330	< .001	2	1	2
9	Means	E_3	3.09	0.743	< .001	2	1	2
10	Variances	E_1	15.28	1.713	< .001	2	1	2
11	Variances	E_2	3.86	0.408	< .001	2	1	2
12	Variances	E_3	27.86	3.071	< .001	2	1	2

[3]

Correlation plot for a mixture model

Latent profile plot

[3]

Density plot

[3]

Elbow plot

[4]

[4]

Latent Class Analysis

Logistic regression coefficients

LCA Plot

[5]

Item by class

[4]

Profile Plot

[4]

Elbow Plot

[4]

_____________________________________________________________________________________________

1. Latent Class Analysis based on poLCA(Linzer & Lewis, 2022) R package.

2. Variables must contain integer values, and must be coded with consecutive values from 1 to the maximum number.

3. Membership table will be shown in the datasheet.

4. Feature requests and bug reports can be made on my GitHub.

_____________________________________________________________________________________________

Model Fit

Model fit
Class	Log-likelihood	Resid.df	AIC	AIC3	BIC	SABIC	CAIC	Entropy	G²	G² p	χ²	χ² p
2	-427	1002	896	917	953	887	974	0.645	103	1.000	222	1.000
Note. G²=Likelihood ratio statistic; χ²=Pearson Chi-square goodness of fit statistic; Entropy=entropy R^2 statistic (Vermunt & Magidson, 2013, p. 71)

[5]

Model comparison
Class	AIC	AIC3	BIC	SABIC	CAIC	Log-likelihood	χ²	G²
1	911	921	939	907	949	-446	232	141
2	896	917	953	887	974	-427	222	103

[5]

Item response probabilities

Probability of T_selfconcept - Transform 1
	Pr(1)	Pr(2)
class 1:	0.741	0.259
class 2:	0.749	0.251

Probability of T_task - Transform 1
	Pr(1)	Pr(2)
class 1:	0.941	0.0590
class 2:	0.880	0.1198

Probability of T_trauma - Transform 1
	Pr(1)	Pr(2)
class 1:	1.000	4.17e-12
class 2:	0.505	0.495

Probability of T_threat - Transform 1
	Pr(1)	Pr(2)
class 1:	1.000	1.35e-66
class 2:	0.818	0.182

Probability of T_conflict - Transform 1
	Pr(1)	Pr(2)
class 1:	0.906	0.0938
class 2:	1.000	2.34e-39

Probability of T_transgression - Transform 1
	Pr(1)	Pr(2)
class 1:	0.879	0.121
class 2:	1.000	2.24e-41

Probability of ERP_anxiety (2) - Transform 1
	Pr(1)	Pr(2)
class 1:	0.861	0.139
class 2:	0.358	0.642

Probability of ERP_closeness - Transform 1
	Pr(1)	Pr(2)
class 1:	0.975	0.0252
class 2:	0.945	0.0552

Probability of ERP_socialanx - Transform 1
	Pr(1)	Pr(2)
class 1:	0.759	0.241
class 2:	1.000	1.39e-11

Probability of ERP_ hopeless - Transform 1
	Pr(1)	Pr(2)
class 1:	0.470	0.530
class 2:	0.650	0.350

[5]

Probability

Size of each latent class
		Probability
1		0.661
2		0.339

[5]

Predicted cell counts from latent class analysis
	T_selfconcept...Transform.1	T_task...Transform.1	T_trauma...Transform.1	T_threat...Transform.1	T_conflict...Transform.1	T_transgression...Transform.1	ERP_anxiety..2....Transform.1	ERP_closeness...Transform.1	ERP_socialanx...Transform.1	ERP_.hopeless...Transform.1	observed	expected
1	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	15.00	14.6940
2	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	2.00	12.00	15.2390
3	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	4.00	3.9410
4	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	2.00	2.00	6.00	4.4510
5	1.00	1.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	0.4550
6	1.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	5.00	6.1150
7	1.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	1.00	2.00	3.00	4.4710
8	1.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	2.00	2.00	1.00	0.7180
9	1.00	1.00	1.00	1.00	1.00	1.00	2.00	2.00	1.00	1.00	1.00	0.2920
10	1.00	1.00	1.00	1.00	1.00	1.00	2.00	2.00	2.00	1.00	1.00	0.0160
11	1.00	1.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	1.00	1.00	1.7000
12	1.00	1.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	2.00	4.00	1.9200
13	1.00	1.00	1.00	1.00	1.00	2.00	2.00	1.00	1.00	2.00	1.00	0.3100
14	1.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	1.00	2.00	4.00	1.4490
15	1.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	2.00	2.00	1.00	0.4610
16	1.00	1.00	1.00	1.00	2.00	1.00	2.00	1.00	1.00	2.00	1.00	0.2340
17	1.00	1.00	1.00	1.00	2.00	2.00	1.00	1.00	1.00	2.00	1.00	0.1990
18	1.00	1.00	1.00	2.00	1.00	1.00	2.00	1.00	1.00	1.00	2.00	0.9190
19	1.00	1.00	1.00	2.00	1.00	1.00	2.00	1.00	1.00	2.00	1.00	0.4940
20	1.00	1.00	2.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	2.00	2.2500
21	1.00	1.00	2.00	1.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	0.1310
22	1.00	1.00	2.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	4.00	4.0400
23	1.00	1.00	2.00	1.00	1.00	1.00	2.00	1.00	1.00	2.00	3.00	2.1720
24	1.00	1.00	2.00	2.00	1.00	1.00	2.00	1.00	1.00	1.00	1.00	0.9020
25	1.00	1.00	2.00	2.00	1.00	1.00	2.00	1.00	1.00	2.00	1.00	0.4850
26	1.00	2.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.0900
27	1.00	2.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	1.0460
28	1.00	2.00	1.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	2.00	0.6860
29	1.00	2.00	1.00	1.00	1.00	1.00	2.00	1.00	1.00	2.00	2.00	0.4430
30	1.00	2.00	1.00	2.00	1.00	1.00	2.00	1.00	1.00	2.00	1.00	0.0670
31	2.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	8.00	5.1130
32	2.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	2.00	4.00	5.3190
33	2.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	4.00	1.3800
34	2.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	1.00	2.00	2.00	1.5330
35	2.00	1.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	1.00	2.00	0.5960
36	2.00	1.00	2.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	3.00	0.7550
37	2.00	1.00	2.00	1.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	0.4060
38	2.00	1.00	2.00	1.00	1.00	1.00	2.00	1.00	1.00	1.00	2.00	1.3560
39	2.00	1.00	2.00	2.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	0.0910
40	2.00	2.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	2.00	1.00	0.3640
41	2.00	2.00	1.00	1.00	1.00	1.00	2.00	1.00	2.00	2.00	1.00	0.0160

[5]

[4]

Latent Class Analysis

Logistic regression coefficients

_____________________________________________________________________________________________

1. Latent Class Analysis based on poLCA(Linzer & Lewis, 2022) R package.

2. Variables must contain integer values, and must be coded with consecutive values from 1 to the maximum number.

3. Membership table will be shown in the datasheet.

4. Feature requests and bug reports can be made on my GitHub.

_____________________________________________________________________________________________

Model Fit

Model fit
Class	Log-likelihood	Resid.df	AIC	AIC3	BIC	SABIC	CAIC	Entropy	G²	G² p	χ²	χ² p
.	.	.	.	.	.	.	.	.	.	.	.	.

[5]

[4]

References

[1] The jamovi project (2023). jamovi. (Version 2.4) [Computer Software]. Retrieved from https://www.jamovi.org.

[2] R Core Team (2022). R: A Language and environment for statistical computing. (Version 4.1) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from CRAN snapshot 2023-04-07).

[3] Rosenberg, J., Beymer, P., Anderson, D., Van Lissa, C., & Schmidt, J. (2021). tidyLPA: Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software. [R package]. Retrieved from https://CRAN.R-project.org/package=tidyLPA.

[4] Seol, H. (2023). snowRMM: Rasch Mixture, LCA, and Test Equating Analysis. (Version 5.6.8) [jamovi module]. URL https://github.com/hyunsooseol/snowRMM.

[5] Linzer, D., & Lewis, J. (2022). poLCA: An R Package for Polytomous Variable Latent Class Analysis. [R package]. Retrieved from https://CRAN.R-project.org/package=poLCA.