Challenge and threat appraisals in high school science: investigating the roles of psychological and physiological factors

Figures & data

Table 1. Descriptive and reliability statistics.

	Mean	SD	Skewness	Range (possible range)	Kurtosis	Omega
PROFILE INDICATORS
Science self-efficacy	5.98	0.96	−1.51	1–7 (1–7)	4.08	.85
Science anxiety	4.28	1.56	−0.27	1–7 (1–7)	−0.64	.86
EDA during science task (µS)	0.08	0.07	2.01	.01–.39 (0–.40)	4.48	.97
OUTCOMES
Science test achievement (%)	63.22	14.44	−0.01	29–93 (0–100)	−0.72	.74
Science flow	4.70	1.09	−0.17	1–7 (1–7)	0.17	.93

Notes. For analyses, achievement was standardised within grade (year group) to have a mean of zero and standard deviation of 1.00 (raw scores are provided in Table 1 to assist interpretation).

EDA: electrodermal activity; SD: standard deviation.

Given we derived latent factor scores to take into the LPA, coefficient omega was employed for estimating reliability to account for any non-equivalence in loadings for a given factor (Trizano-Hermosilla & Alvarado, 2016); coefficient alpha (a frequently used reliability index) does not account for this.

Table 2. Fit statistics, entropy, and profile size for various profile solutions.

	2 Classes	3 Classes	4 Classes	5 Classes	6 Classes	7 Classes
Log-Likelihood (LL)	−587	−553	−525	−506	−494	−487
Best LL Replicated	Yes	Yes	No	No	No	Yes
SSA BIC	1199	1143	1102	1076	1066	1064
Entropy	.659	.772	.837	.759	.781	.795
pLMR	<.001	<.01	.100	<.05	.050	.261
pBLRT	<.001	<.001	<.001	<.001	<.05	<.001
Min % cases	35	14	6	5	5	2

Notes. When the best log-likelihood not replicated, solutions may not be trustworthy.

SSA BIC: sample size adjusted Bayesian information criterion; pLMR: p value of the Lo–Mendell–Rubin Likelihood Ratio Test; pBLRT: p value of the Bootstrapped Likelihood Ratio Test.

In all cases where the best log-likelihood was not replicated, this was the case for 10,000 starts with 100 iterations, 5000 starts with 50 iterations, and 3200 starts with 20 iterations.

Figure 1. The final three derived profiles (standardised scores; mean = 0). Note. Table 3 shows significant differences in the profile indicator variables (science anxiety, science self-efficacy, EDA during science task) across the profiles.

Table 3. Latent profile analysis solution and significant mean differences in latent profile indicators.

	Profile indicator variables
	Anxiety	Self-efficacy	EDA	Relative profile size (%)	Proposed profile name
Profile 1	−0.04^a	0.16^a	−0.46^†a	52	‘Composed challenge-and-threat’ (Modest EDA, Average Anxiety; Average Self-efficacy)
Profile 2	0.42^†b	−0.58^†b	0.76^†b	34	‘Aroused high-threat’ (Elevated EDA; Elevated Anxiety; Low Self-efficacy)
Profile 3	−1.06^†c	1.06^†c	−0.41^†a	14	‘Composed high-challenge’ (Modest EDA; Low Anxiety; Elevated Self-efficacy)

Note. ^† = value significantly different from zero at p < .05; Different alphabetical superscripts ^(a,b,c) in a column indicate two profile groups significantly different on that indicator variable, p < .05 (e.g. there are different alphabetical superscripts for anxiety across the 3 profiles, indicating each profile’s anxiety value is significantly different from the other profiles). Profile indicator variables were standardised (M = 0, SD = 1) to aid interpretation.

Figure 2. (a) Differences across profiles in science test achievement. Note: Different superscripts indicate two profile groups significantly different on achievement, p < .05. (b) Differences across profiles in science flow. Note. Different superscripts indicate two profile groups significantly different on flow, p < .05.

Trizano-Hermosilla, I., & Alvarado, J. M. (2016). Best alternatives to Cronbach’s alpha reliability in realistic conditions: Congeneric and asymmetrical measurements. Frontiers in Psychology, 7, 769. https://doi.org/10.3389/fpsyg.2016.00769

 PubMed Web of Science ®Google Scholar