A scoping review on the psychometric properties of the teacher efficacy for inclusive practices (TEIP) scale

Figures & data

Table 1. Reviewed articles on the TEIP scale with 18 items (cf., Sharma et al., 2012).

Study	N	Country	Teachers	Response rate	Psychometric aim	Structural validity	Reliability
Alnahdi (2019a)	312	Saudi Arabia	In-service	-	Construct validity	Rasch analysis	Person separation index
Alnahdi (2019b)	432	Saudi Arabia	In- and pre-service	-	Psychometric characteristics	PCA, CFA, Rasch analysis	Cronbach's alpha
Alnahdi and Yada (2020)	250	Japan	In-service (grade 1–12)	-	Construct validity	Rasch analysis	Person separation index
Cardona-Molto et al. (2020)	475	Spain	Pre-service (early childhood, elementary)	71%	Factor structure and reliability	EFA, CFA	Cronbach's alpha
Malinen et al. (2012)	437	China	In-service (primary, middle)	>90%	Factor structure	CFA	Cronbach's alpha
Malinen et al. (2013)	552	China	Pre-service (early childhood, general, special education)	-	Factor structure	CFA	Cronbach's alpha
Martins and Chacon (2020)	308	Brazil	In-service (early childhood, elementary)	-	Psychometric properties	EFA	Cronbach's alpha
Miesera et al. (2019)	887	Germany	Pre-service (elementary, lower secondary, upper secondary, vocational, special education)	-	Factor structure	CFA	-
Emam and Al-Mahdy (2019)	287	Sultanate of Oman	In-service (basic school)	96%	Factor structure	CFA	Cronbach's alpha
Narkun and Smogorzewska (2019)	291	Poland	In-service (primary)	≈50%	Psychometric properties and factor structure	PCA	Cronbach's alpha
Park et al. (2016)	134	USA	Pre-service (early childhood)	-	Factor structure	CFA	Cronbach's alpha
Tanrıverdi and Özokçu (2018)	567	Turkey	Pre-service (early childhood, primary, special education)	-	Psychometric properties	CFA	Cronbach's alpha
Vogiatzi et al. (2022)	465	Greece	In-service (secondary)	-	Psychometric properties	EFA, CFA	Cronbach's alpha
Yada et al. (2021)	105	Finland	Pre-service	32%	Factor structure	CFA	Cronbach's alpha
Yada et al. (2018)	1231	Finland, Japan	In-service (primary, secondary)	≈60% Finland, 49% Japan	Construct validity	MGCFA	-

Notes: EFA = Explorative factor analysis, PCA = Principal component analysis, CFA = Confirmatory factor analysis, MGCFA = Multigroup confirmatory factor analysis.

Table 2. Studies on the structural validation of the TEIP self-rated by teachers.

Study	N	Teachers	Stability
Alnahdi (2019a)	312	in-service	The initial Rasch analysis on 18 items did not support the unidimensionality of the TEIP scale (unidimensionality test = 14.4%). However, when removing 27 participants regarded as outliers, omitting 5 items, and rescoring 4 items, the unidimensionality of the TEIP scale was improved to 4.9% on the lower limit of 95% CI.
Alnahdi (2019b)	432	in-service and pre-service	In the initial Rasch analysis, 2 of the 18 items and 21 participants were reported to misfit the unidimensionality of the TEIP. Nextly, analyses with PCA revealed that 4 items did not function as expected according to a three-factor solution of the TEIP. There was support for two components, and 45% of the variance was explained with the first components. CFA on the three-factor structure of the TEIP was reported to have an acceptable fit (CFI = .910, TLI = .895, RMSEA = .075, SRMR = .0466). A model with omitted items and removed outliers based on finding from the Rasch analysis did not fit the data better. Thus, the authors report a slightly better fit when errors from 8 items were covaried.
Alnahdi and Yada (2020)	250	in-service	The initial Rasch analysis on 18 items did not support the unidimensionality of the TEIP scale. When omitting 4 items and 18 participants and rescoring 1 item, the unidimensionality test of the TEIP scale was 4.46% on the lower limit of 95% CI. Rasch analyses on the three subscales did not support multidimensionality within the TEIP scale.
Cardona-Molto et al. (2020)	475	pre-service	In the EFA, on 18 items, the explained overall variance was 61.52% (KMO = 0.932, Bartlett's test p < .001). The first factor explained 44.43% of the variance, the second 9.07%, and the third 7.02%. With promax oblique rotation the items in three factors had loadings between .476 and .865. Further, the CFA on the three-factor structure of the TEIP and 18 items was reported to not have an acceptable fit to the data (CFI = .845, TLI = .842, RMSEA = .111). The CFA model was reported to improve when 3 items were omitted (CFI = .864, TLI = .861, RMSEA = .090). Correlations between the subscales were reported to be high (r = .74–.90).
Malinen et al. (2012)	437	in-service	CFA on the three-factor structure of the TEIP was reported to have an acceptable fit (CFI = .92, TLI = .91, RMSEA = .06, SRMR = .06). Two items loaded on all factors and were removed from the model.
Malinen et al. (2013)	552	pre-service	CFA on the three-factors structure of the TEIP was reported to have a good fit (CFI = .96, TLI = .95, RMSEA = .05, SRMR = .04). Correlations between the subscales were between .53 and .60. One item loaded on all factors and was left out.
Martins and Chacon (2020)	308	in-service	An initial EFA explained 62.27% of the overall variance for three dimensions (KMO = 0.90, Bartlett's test p < .001). The first factor explained 46.96% of the variance, the second 8.82%, and the third 6.50%. Two items were observed to load on two factors. Also, only two items had loadings on the third factor, and the researchers argued for an additional model consisting of 16 items divided into two factors. The total explained variance was 56.95%, and the first factor explained 47.07% and the second 9.88%.
Study	N	Teachers	Stability
Miesera et al. (2019)	887	pre-service	CFA on the three-factors structure of the TEIP including 16 items proposed by Park et al. (2016) were reported to have an acceptable fit (CFI = .913, Gamma-hat = .956, RMSEA = .066, SRMR = .069).
Emam and Al-Mahdy (2019)	287	in-service	Initial CFA on the three-factor structure of the TEIP and 18 items had not an acceptable fit to the data (CFI = .864, NFI = .844, TLI = .841, RMSEA = .138). Three items had low loadings (< .31) and 2 items had cross-loadings. Further, a CFA on three-factor structure with 15 items and 4 covaried items errors was reported to have good fit (CFI = .961, NFI = .041, TLI = .952, RMSEA = .079).
Narkun and Smogorzewska (2019)	291	in-service	The PCA explained 65.05% of the overall variance (KMO = 0.923, Bartlett's test p < .0001). The first component explained 46.61% of the variance, the second 10.57% and the third 7.86%. One item loaded on component that was not expected in relation to the TEIP scale. Orthogonal rotation solution with varimax rotation resulted in loadings with values between .429 and .873 for items in the three components. Correlations between the total scale and the subscales were report to high (r = .855–.860). The correlations between the subscales (r = .573–.631) were also high.
Park et al. (2016)	134	pre-service	CFA on three-factor structure was reported to have an acceptable fit (CFI = .994, TLI = .993, WRMR = .886, RMSEA = .085, LL = .070, UL = .100). Factor loadings between .01-.47 for the factor Instructions, .17-.41 for the factor Collaboration, and .01-.46 for the factor Managing behavior. Additionally, unidimensionality of the TEIP was also confirmed (CFI = .997, TLI = .996, WRMR = .651, RMSEA = .064, LL = .045, UL = .082).
Tanrıverdi and Özokçu (2018)	567	pre-service	CFA on the three-factor structure of the TEIP was reported to have an acceptable fit of the data (CFI = .96, NFI = .95, SRMR = .054, RMSEA = .101). Factor loadings were between .44 and .81. Two items in the factor Collaboration were assumed to measure the same behavior. Also, two items in Managing behavior were reported to measure the same behavior.
Vogiatzi et al. (2022)	465	in-service	EFA explained 55.77% of the overall variance (KMO = .882, Bartlett's test p < .005). The first factor explained 37.449% of the variance, the second 9.741% and the third 8.578%. Loadings between .38-.85 for the Instructions, .53-.76 for the Collaboration, and .51-.78 for the Managing behavior. Different factor structures were explored with CFA, and the three-factor structure was reported to fit the data best (CFI = .90, TLI = .96, RMSEA = .013, SRMR = .003). Correlations between factors were reported to be between .55 to .65.
Yada et al. (2021)	105	pre-service	CFA on three-factor structure was reported to sufficiently fit the data after covariation of items (SRMR = .061, RMSEA = .060). The factors were highly correlated with each other (r = .73-.83), and factor loadings were reported to be between .47-.84 for the factor Instructions, .69-.84 for the factor Collaboration, and .57-.81 for the factor Managing behavior.
Yada et al. (2018)	872 359	in-service	An initial CFA model on the three-factor structure of the TEIP was reported to have an adequate fit (RMSEA = .052, SRMR = .061). When scalar invariance was explored, an insufficient fit was reported and explained by lack of invariance in some item intercepts (RMSEA = .067, SRMR = .093).

Table 3. Studies on the internal consistency, evaluated with Cronbach's alpha, for the TEIP.

Study	N	Country	Teachers	Efficacy in inclusive instructions	Efficacy in collaboration	Efficacy in managing behavior	Total scale
Alnahdi (2019b)	432	Saudi Arabia	pre-service and in-service	.82	.82	.83	.92
Cardona-Molto et al. (2020) ^a	475	Spain	pre-service	.84	.85	.89	.92
Malinen et al. (2012)	437	China	in-service	.75	.87	.88	.91
Malinen et al. (2013) ^b	552	China	pre-service		.90
Martins and Chacon (2020) ^c	308	Brazil	in-service		.92
Emam and Al-Mahdy (2019)	287	Sultanate of Oman	in-service	.83	.93	.93	.93
Narkun and Smogorzewska (2019)	291	Poland	in-service	.83	.88	.92	.93
Park et al. (2016)	134	USA	pre-service	.93	.95	.94	.98
Tanrıverdi and Özokçu (2018)	567	Turkey	pre-service	.77	.79	.68	.89
Vogiatzi et al. (2022) ^d	465	Greece	in-service				.89
Yada et al. (2021)	105	Finland	pre-service				.93

Note: ^a Reliability based on 15 items due to the omission of 3 items in the structural validation of the TEIP. ^b Cronbach’s alpha was not specified for each scale but was reported to be between .75-.85 for the subscales. ^c Cronbach’s alpha is reported on 16 items, divided into 2 subscales (alphas were .88 and .89). ^d Cronbach’s alpha was reported to be between .88 and .89 for all items.

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