Table 3.

Latent Profile Analysis: Assessing model fit for older and younger infants

Younger Infants	1 Class	2 Classes	3 Classes	4 Classes	5 Classes
AIC	32710.97	26710.99	26148.24	25805.95	25513.03
BIC	32931.50	26954.49	26506.61	26279.17	26101.12
Sample Size Adjusted	32779.08	26786.19	26258.93	25952.11	25694.67
BIC
Entropy	na	.834	.887	.853	.851
Lo, Mendell, Rubin	na	2 v 1	3 v 2	4 v 3	5 v 4
Test		Value	Value	Value	Value
		p = 0.00	p = 0.04	p = 0.31	p = 0.12
N for each class	C1 = 731	C1 = 325 C2 = 406 P1 = −.57 p = 0.00	C1 = 282 C2 = 92 C3 = 357 P1 = −.60 p = 0.00 P2 = .07 p = 0.79	C1 = 253 C2 = 238 C3 = 81 C4 = 159 P1 = .30 p = 0.11 P2 = .31 p = 0.46 P3 = .92 p = 0.00	C1 = 171 C2 = 232 C3 = 76 C4 = 127 C5 = 125 P1 = .30 p = 0.43 P2 = .44 p = 0.51 P3 = .86 p = 0.00 P4 = .93 p = 0.03
Average Posterior Probability		.948	.953	.923	.916
Older Infants	1 Class	2 Classes	3 Classes	4 Classes	5 Classes
AIC1	27367.02	21731.75	21263.29	21003.17	20757.72
BIC2	27571.15	21962.52	21600.56	21446.94	21308.00
Adjusted BIC	27425.11	21797.42	21359.27	21129.46	20914.32
Entropy	na	.832	.845	.829	.849
Lo, Mendell, Rubin	na	2 v 1	3 v 2	4 v 3	5 v 4
Test		Value	Value	Value	Value
		p = 0.00	p = 0.07	p = 0.24	p = 0.15
N for each class	C13 = 625	C1 = 232 C2 = 393	C1 = 98 C2 = 243 C3 = 284	C1 = 208 C2 = 183 C3 = 92 C4 = 142	C1 = 78 C2 = 173 C3 = 79 C4 = 186 C5 = 109
Covariate Paths/Effects	na	P1 = .04 p = 0.81	P1 = −.12 p = 0.72 P2 = −.16 p = 0.60	P1 = −.14 p = 0.54 P2 = .07 p = 0.85 P3 = −.03 p = 0.89	P1 = −.08 p = 0.82 P2 = .19 p = 0.49 P3 = .48 p = 0.31 P4 = −.03 p = 0.93
Average Posterior Probability		.954	.929	.916	.912

Notes.

¹Akaike Information Criteria (AIC);

²Bayesian Information Criteria (BIC);

³C1–C5 values represent sizes of classes/profiles;

⁴P1–P4 path coefficients for infant age (covariate) as a predictor of profile membership (i.e., latent profile variable).