Capturing the process of academic commitment: a taxonomy and test of post-secondary student academic commitment trajectories

Figures & data

Table 1. Fit indices of the latent classes models of AC.

Model	BLRT χ^2 (df)	BIC	ABIC	Entropy	% of total counts	Posterior probabilities
One latent class	-	23359.98	23290.30	1.00	1.00	1.00
Two latent classes	1364.21*** (5)	22021.73	21936.22	.96	[.26; .74]	[.99; 1.00]
Three latent classes Four latent classes Five latent classes Six latent classes	828.66*** (5) 357.58*** (5) 221.72*** (5) 210.85*** (5)	21219.04 20887.42 20691.67 20506.78	21117.69 20770.24 20558.66 20357.93	.96 .95 .96 .96	[.09; .48] [.09; .41] [.01; .42] [.001; .36]	[.98; 1.00] [.93; 1.00] [.97; 1.00] [.93; 1.00]

Notes. N_respondents = 180; N_observations = 2778. * p<.05; ** p<.01; *** p<.001

Figure 1. Five distinct AC trajectories during the first term of university. The dashed line indicates the start of the first term of the university.

Table 2. Growth parameters of the latent classes models of AC.

	Latent classes
	Class 1: Learning to Love (N = 14; 7.78%)	Class 2: High Match (N = 20; 11.11%)	Class 3: Low Match (N = 69; 38.33%)	Class 4: Honeymoon Hangover (N = 2; 1.11%)	Class 5: Moderate Match (N = 75; 41.67%)
Intercept	42.65*** (2.42)	92.43*** (1.53)	68.09*** (1.02)	44.61* (22.09)	79.46*** (.85)
Slope	.93* (.49)	.62*** (.22)	.10 (.26)	.99*** (.27)	.20 (.16)
Quadratic term	-.15 (.16)	-.08 (.05)	-.10 (.06)	−1.26*** (.35)	-.02 (.03)
Cubic term	.011 (.009)	.003 (.002)	.007* (.003)	.076*** (.017)	.001 (.002)

Notes. N_respondents = 180; N_observations = 2778. Standard errors are presented between parentheses. The cubic term is reported with three digits after the comma because these third order terms often tend to be small. * p<.05; ** p<.01; *** p<.001

Data Availability Statement

The data that support the findings of this study are openly available in the Open Science Framework at DOI 10.17605/OSF.IO/NS8RC.

Acknowledgments

We would like to thank two anonymous reviewers and the Editor (Ly Tran) who handled our manuscript for their suggestions to further improve our manuscript. We would also like to thank Jesse Fenneman for his assistance in creating the R script that was used to generate the figures in this manuscript.