Exploring Within-Person Variability in Qualitative Negative and Positive Emotional Granularity by Means of Latent Markov Factor Analysis

Figures & data

Figure 1. Hypothetical simplified state-specific measurement model for negative emotions. Here, individuals mentally represent “angry” and “sad” under one emotion category, which is represented by Factor 1 (F1). Due to high standardized loadings on F1, the items “angry” and “sad” can be considered to be weakly differentiated. The same logic applies to the items “scared” and “ashamed”, both of which have high loadings on Factor 2 (F2). Given the low correlation between F1 and F2, individuals differentiate between the categories “anger/sadness” versus “fear/shame.” For a continuous (slider) response format that ranges from 0 to 1, intercept values could theoretically range from 0.00 to 1.00. Hence, an intercept value of zero for “guilty” would indicate that individuals do not experience any guilt in this state. Grey, dashed arrows indicate low standardized cross-loadings. Item residuals are not depicted.

Table 1. State-specific measurement models for negative emotions.

	State 1 High negative intensity, low granularity					State 2 Low differentiation between anger and sadness			State 3 High differentiation between anger and sadness
Item	Intercept	F1 loading	F2 loading	F3 loading	Residual variance	Intercept	F1 loading	Residual variance	Intercept	F1 loading	Residual variance
angry	0.271	0.015	0.020	0.783	0.023	0.114	0.560	0.025	0.082	0.705	0.015
irritated	0.306	0.012	–0.019	0.847	0.020	0.135	0.541	0.031	0.105	0.824	0.012
annoyed	0.344	0.021	0.000	0.853	0.019	0.210	0.534	0.046	0.158	0.791	0.020
sad	0.318	0.910	–0.009	–0.042	0.018	0.163	0.734	0.022	0.097	0.166	0.032
downhearted	0.346	0.843	–0.014	0.040	0.021	0.193	0.774	0.022	0.133	0.270	0.041
unhappy	0.360	0.837	–0.031	0.044	0.020	0.232	0.778	0.022	0.195	0.209	0.057
terrified	0.190	0.383	0.175	0.075	0.031	0.000	–	–	0.000	–	–
scared	0.232	0.532	0.161	0.011	0.037	0.086	0.299	0.024	0.000	–	–
worried	0.375	0.602	0.132	0.070	0.036	0.244	0.575	0.041	0.126	0.283	0.035
ashamed	0.181	–0.014	0.835	–0.021	0.016	0.048	0.212	0.015	0.000	–	–
humiliated	0.179	0.006	0.446	0.294	0.027	0.000	–	–	0.000	–	–
disgraced	0.147	–0.135	0.749	0.069	0.018	0.000	–	–	0.000	–	–
guilty	0.207	0.118	0.794	–0.064	0.014	0.082	0.332	0.023	0.000	–	–
repentant	0.246	0.045	0.737	0.056	0.024	0.151	0.398	0.042	0.000	–	–
bad conscience	0.236	0.044	0.796	–0.019	0.020	0.165	0.306	0.045	0.000	–	–
	Factor correlations
F1			.62	.65
F2				.44

Note. Factor loadings were z standardized by using state-specific item standard deviations. No measurement model for State 4 (the “no negative emotions” state) depicted as all item values were zero in this state.

Table 2. Stress-specific transition probabilities of latent negative EG states for an interval of 4 hr.

State − 1	Initial state probabilities	Low stress				High stress
		State (negative EG)				State (negative EG)
		S1	S2	S3	S4	S1	S2	S3	S4
S1 High negative intensity, low granularity	.623	.583	.184	.138	.096	.719	.177	.089	.015
S2 Low differentiation between anger and sadness	.259	.289	.242	.283	.186	.467	.309	.188	.036
S3 High differentiation between anger and sadness	.095	.164	.224	.333	.279	.357	.279	.290	.073
S4 No negative emotions	.023	.142	.217	.305	.336	.268	.236	.347	.150

Note. The transition probabilities indicate the probability of transitioning from the previous latent state (State − 1, rows) to the current latent state (State, columns). For each previous latent state, the latent state that individuals were most likely to transition to is shaded in grey. Probabilities might not add up to 1 due to rounding errors. Stress was person-mean-centered. Low stress corresponds to a score of –0.21 (1 SD below the person mean), and high stress corresponds to +0.21 (1 SD above the person mean).

Figure 2. State-specific measurement models for negative emotions. The strengths of the loadings are absolute values of the standardized factor loadings. For interfactor correlations, see Table 1.

Table 3. Class-specific transition probabilities of latent negative EG states for an interval of 4 hr in mixture LMFA.

State − 1	Initial state probabilities	Class C1 Variability in negative granularity (size: 59%)				Class C2 High negative intensity, low granularity (size: 41%)
		State (negative EG)				State (negative EG)
		S1	S2	S3	S4	S1	S2	S3	S4
S1 High negative intensity, low granularity	.623	.246	.322	.288	.144	.875	.099	.022	.004
S2 Low differentiation between anger and sadness	.259	.210	.335	.305	.150	.788	.159	.042	.011
S3 High differentiation between anger and sadness	.095	.175	.267	.364	.194	.738	.189	.054	.019
S4 No negative emotions	.023	.140	.231	.340	.289	.631	.212	.082	.075
	Overall state membership probabilities	.199	.291	.323	.186	.857	.111	.026	.006

Note. The transition probabilities indicate the probability of transitioning from the previous latent state (State − 1, rows) to the current latent state (State, columns). For each previous latent state, the latent state that individuals were most likely to transition to is shaded in grey. Probabilities might not add up to 1 due to rounding errors.

Figure 3. Predicting latent class membership from a neuroticism, b negative mood repair, and c positive mood maintenance for negative emotions. Class C1_neg represents the “variability in negative granularity” class, Class C2_neg the “high negative intensity, low granularity” class.

Table 4. Multinomial logistic regression parameters of latent classes regarding transitions between negative EG states predicted by time-constant covariates.

Predictor	Class C2_neg (vs. Class C1_neg)			Wald test
	Intercept	Coef.	SE	χ²	df	p
Neuroticism	–2.559	0.687*	0.241	8.14	1	.004
Negative mood repair	1.721	–0.810*	0.299	7.32	1	.007
Positive mood maintenance	2.190	–0.961*	0.292	10.81	1	.001

Note. C1_neg represents the “variability in negative granularity” class” and C2_neg the “high negative intensity, low granularity” class.

* p < .01.

Data availability statement

Data, analysis scripts, and model outputs for this research can be accessed via https://osf.io/w4tye/.