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Abstract

This study focused on adapting and evaluating the Life Orientation Test-Revised (LOT-R) for measuring dispositional optimism in university teachers in Mexico. The primary objective was to modify the LOT-R scale to a frequency response system and assess its psychometric properties in university teachers (232 participants). The findings revealed that the adapted frequency response system of the LOT-R has evidence of construct validity determined by Exploratory and Confirmatory Factor Analysis (X² = 1.34, p = .931; X²/df = 0.27, GFI = 0.99, AGFI = 0.99, RMR = 0.01, SRMR = 0.03, NFI = 0.99, RFI = 0.99), criterion validity by concurrent analysis with Satisfaction with Life scale (r = .41, p < .001, Fisher’s z = 0.44, 95% CI [0.32, 0.99]), convergent validity determined with Average Variance Extracted (AVE = 0.64), and reliability calculated by internal consistency (α = .88, ω = 0.89, GLB = 0.91). The study also highlighted the variability in the factorial structure of the LOT-R across different studies, contributing to the one-dimensional structure adaptations of the scale. The frequency response system demonstrated a better fit, and accuracy than the agreement system used in the LOT-R. The frequency system emphasizes direct measures of optimism toward a more realistic representation of lived experiences. Adapting the LOT-R using frequency response items in Mexican teachers significantly contributed to measuring optimism to promote a healthy higher education environment.

Keywords

optimism life orientation test-revised validation higher education educational innovation

Introduction

The study of optimism goes back to philosophical currents dating from the early 17th century. Since then, it has been addressed in contributions by prominent scientists, such as René Descartes, Gottfried Leibniz, Immanuel Kant, Georg Hegel, Friedrich Nietzsche, and Sigmund Freud, to name a few (Chang, 2001; Peterson & Seligman, 2004). However, it was not until the mid-1980s that its study began to gain momentum in a more systematic and scientifically way in the field of personality psychology and health psychology (Carver & Scheier, 2014; Ferrando et al., 2002).

Initially, optimism was conceived as a polar psychological dimension, with optimism on the positive side and pessimism on the negative side, interpreted as a type of bias in people’s perceptions and expectations (Dember et al., 1989). Over time, the scientific community has adopted the conceptualization proposed by Scheier and Carver (1985), who suggest that optimism is a tendency for people to believe they will experience favorable outcomes. This last proposal is founded on theories about motives, expectations, goals, and how they shape behavior, organized in a way that ensures behaviors fulfill the objectives of optimists (Chang, 2001).

Some works have classified optimism as a dispositional trait that tends to remain stable over time, varying only in its magnitude in significant life situations (Carver & Scheier, 2014; Cuadra & Florenzano, 2003; Scheier & Carver, 2018; Yu & Luo, 2018). Similarly, Carver and Scheier (2014) suggest that this construct is identifiable as cognitive because, at its center, is expectation, which, according to Sharot (2011), is a cognitive capacity that allows preparing actions to avoid or minimize undesirable situations and attain expected positive results. Finally, Park et al. (2013) simplify the concept by describing it in everyday language as thinking positively.

Findings have indicated that optimistic people tend to be more confident, persistent, and adapt more easily to critical life transitions (Carver et al., 2010; Scheier et al., 1994), distancing themselves from anxiety and depression and, under equal conditions, they live longer and stay healthier than pessimists (Sharot, 2011). They also focus on the problem, are more efficient with their coping strategies, connect better socially (Carver et al., 2010), and, in general, have a higher chances of success (Zhao & You, 2021).

Optimism has proven its worth, relevance, and impact in many aspects and spheres of life; education is no exception. Within educational institutions, there is evidence that optimism is positively associated with physical and mental health and well-being (Ferrando et al., 2002; Segerstrom, 2007; Song, 2022; Yu & Luo, 2018). The teaching profession is widely perceived as essential for societal development. Among teachers, it can be a career of mixed feelings (Vicente Coronado et al., 2019). On the one hand, the university teacher assumes the noble purpose of contributing to people’s development, feeling the affective return from their students and the community. On the other hand, for students, the teachers’ demands and assignments, as well as the need to comply with social expectations, force them to invest a significant amount of time, which leads to elevated levels of stress and emotional exhaustion (Sasmoko et al., 2017). Additionally, being a university teacher means performing in a profession where pedagogical, disciplinary, and technological knowledge are not enough; they must also have socio-emotional skills to build an optimal environment of trust in the classroom, which is essential for the generation of learning (Cansoy et al., 2017; de Pablos & González, 2012). Attributes such as empathy, self-regulation, assertiveness, resilience, and optimism are necessary for a memorable school experience (Fedorov et al., 2020; McInerney et al., 2018; Zhang, 2021).

Being an optimistic teacher means having positive expectations of one’s performance and that of the students. Optimism predisposes the teacher to act, pursue goals, and the willing to accompany the students, providing attention and presenting learning opportunities. Thus, influencing students and colleagues with the same positive feeling and creating a healthy academic work environment that posters positive functioning (Naveed Jabbar et al., 2019; Song, 2022; Zhang, 2021). Having optimistic instructors provides tangible benefits for higher education institutions, such as teachers’ higher perceived self-efficacy, improved leadership supported by a plan and vision, more creativity, and a better environment (Cuadra & Florenzano, 2003; Naveed Jabbar et al., 2019; Peterson & Seligman, 2004; Zhang, 2021).

In summary, an optimistic teacher’s persistence enables them to achieve their goals, accumulate resources over time, and serve as a stimulus for positive behavior in others. The above are sufficient reasons to diagnose and promote optimism in university teachers. Still, it is necessary to have instruments capable of obtaining valid and reliable data in this population.

Several instruments are used to measure optimism. One of the most widely recognized scales is the Life Orientation Test-Revised (LOT-R), which consists of 10 self-assessment items. It is based on theories of expectations and values, considering that people strive to overcome adversity to achieve their goals when their expectations are favorable or positive (Ferrando et al., 2002; Garcia Cadena et al., 2019; Scheier et al., 1994; Vera-Villarroel et al., 2009). Another is the 42-item Personal Optimism and Social Optimism Extended scale (POSO-E; Schweizer & Koch, 2001), which comprises three dimensions: (1) personal optimism, (2) self-efficacy optimism, and (3) social optimism. Its extension and lack of recognition are its significant limitations. A third option is the brief Interactive Optimism Scale-García (IOS-G; García et al., 2013), which was developed in Mexico and consists of four items. This recent scale requires further studies and validation in Mexico and worldwide. The brief nature of that scale raises concerns that it may not fully capture the construct and that the depth of the information provided might be too limited. It was similarly designed under the precept that optimism is an interactive rather than a dispositional trait, as has traditionally been established.

The LOT-R has been studied and validated in several countries with different populations. Recent studies, particularly those conducted with Latino populations, have demonstrated relative stability in their structure. Usually, the instrument measures two dimensions. However, the number of points on the scale has differed, and reliability, measured by internal consistency, has been questionable (see Table 1).

Table 1.

Description of the LOT-R Validation Studies.

Studies	Country	Sample	Dimensions	Points	Reliability
Scheier et al. (1994)	USA	Students	1	0–4	0.78
Pan et al. (2017)	USA	Hispanic Americans	2	1–4	0.57
Garcia Cadena et al. (2021)	USA	Students	2	1–5	0.61
Zenger et al. (2013)	Colombia	General	2	0–4	0.58
Sanin and Salanova Soria (2017)	Colombia	Workers	2	1–4	0.53–0.47
Valdelamar-Jiménez and Sánchez-Pedraza (2017)	Colombia	General	2	0–4	-
Hinz et al. (2022)	Germany and Colombia	General	2	0–4	0.48–0.74
Vera-Villarroel et al. (2009)	Chile	General	2	0–4	0.65
Kennes et al. (2021)	Netherlands	Adolescents	2	1–5	-
Nunes et al. (2023)	Brazil	Adults	1	0–4	0.81
Angelo et al. (2021)	Brazil	Young athletes	2	1–7	0.72–0.70
Gavín-Chocano et al. (2023)	Spain	University students	2	1–7	0.88–0.75
Landero and González (2009)	Spain, Mexico, and other countries	People diagnosed with fibromyalgia	2	-	0.81
García et al. (2013)	Mexico	Relatives of people sick	1	1–4	0.31

Note. Studies with two coefficients correspond to optimism and pessimism. Empty spaces are missing data in the revised publication.

In Mexico, research on the validity and reliability of the LOT-R remains limited. The first study identified with a Mexican population was conducted by Landero and González (2009), who validated this scale with 154 patients diagnosed with fibromyalgia. The sample primarily comprised residents from Spain (76 persons, representing 49.4%) and had only 34 (22.1%) responses from residents in Mexico. Their results indicate a structure composed of three factors (two first-order and one second-order) with six items. The validity evidence was acceptable (X²/df = 1.87, CFI = 0.97, AGFI = 0.93, RMR = 0.06, RMSEA = 0.07), as was the reliability (α = .81). The second study in Mexico was by García et al. (2013) with the participation of 350 relatives of patients diagnosed with cancer. The results of the five-item scale were not optimal (CMIN = 3.05, RMSEA = 0.82; α = .31) and its structure was found to be unidimensional. Garcia Cadena et al. (2023) conducted the most recent study with Mexican representation, which included populations from Spanish-speaking countries, with Mexico contributing 201 (11.7%) responses. The findings on the psychometric properties of the LOT-R for the total sample were considered poor, as well as for the Mexican subsample, which only obtained an acceptable fit index (GFI = 0.95) and poor reliability (ω = 0.57).

When analyzing the published studies and the items comprising the scale, the researchers of this study assumed that the inconsistencies in reliability could be due to the instructions and response options used in the instrument. The LOT-R has traditionally measured the degree of “agreement,” which refers to the presence or absence of a belief or attitude that cannot be observed; the response tends to fall into social desirability or refers to what is believed to have been observed or experienced when, in fact, the frequency was not measured. The scale could be subjective and imprecise, and participants could use different strategies to average, calculate, or estimate their responses. For example, the item “I hardly ever expect things to go my way” can have a double interpretation: Participants may consider whether they agree with the proposition of the time determined by the phrase “hardly ever” or whether they agree or disagree with “things to go my way.” The wording of the items comprising the LOT-R scale confuses the frequency and agreement formats, increasing the complexity of its understanding, which would be detrimental to measuring psychometric properties.

The study of optimism seems far from over because theoretical and cultural inconsistencies remain among the populations studied today. This fact is addressed by Palacios-Delgado and Acevedo-Ibarra (2023), who mention that most of the cultural discrepancies are due to attempting to introduce scales originating from individualistic cultures into collectivist cultures, such as the Latin ones, without prior adaptation in the best of cases, and the worst without a rigorous assurance of compliance with psychometric properties.

In education, most research focuses on the students and overlooks teachers in a study sample, producing partial results from a population (Garcia Cadena et al., 2021; Vera-Villarroel et al., 2009). In addition, in Mexico, the study of optimism is still considered emerging and even more so when limited to specific populations such as university teachers. Consequently, there is a need to have instruments that report evidence of validity and reliability in different populations with the possibility and certainty to obtaining reliable and generalizable results and conclusions that help promote a sense of well-being within educational institutions. Finally, according to García et al. (2013), it is necessary to develop instruments to measure constructs in specific populations where the socioeconomic, political, and cultural contexts are different, which will contribute to a better understanding of the construct. Therefore, this study aimed to adapt the LOT-R to a response system that measures frequencies and evaluates the psychometric properties of the Mexican university teachers.

Method

Design

The investigation employed a quantitative research method with a non-experimental cross-sectional design, meaning that the data collected at one point in time (Creswell, 2009).

Sample

The sampling method was non-probabilistic sample and based on availability and willingness to participate. In this sense, the sample was collected from a population of 1,054 teachers from the School of Medicine and Health Sciences at a private Mexican university. The sample consisted of 232 Mexican university teachers, with an average age of 44.12 (SD = 2.29), of whom 128 identified as women (55.2%). Additionally, 98% of the participants resided in four major Mexican cities: Monterrey (57.4%), Mexico City (20.3%), Guadalajara (15.1%), and Chihuahua (5.2%), with remainder from other cities. Regarding academic background, the participants had a mean of 9.13 years of teaching experience (SD = 7.96). Among the university teachers, 74 held doctorate degrees, 151 held master’s degrees or medical specialties, and 7 had bachelor’s degrees.

Instruments

Life Orientation Test-Revised (LOT-R)

The LOT-R by Scheier et al. (1994) has been translated and applied in studies with Hispanic populations (Garcia Cadena et al., 2021; Gavín-Chocano et al., 2023; Nunes et al., 2023; Rondón Bernard & Angelucci Bastidas, 2016; Sanin & Salanova Soria, 2017; Valdelamar-Jiménez & Sánchez-Pedraza, 2017; Vera-Villarroel et al., 2009; Zenger et al., 2013). The original scale comprised 10 items, with 3 measuring optimism, 3 measuring pessimism, and 4 measuring distractions, using a Likert-type response format (0 = strongly disagree, 1 = disagree, 2 = neutral, 3 = agree, and 4 = strongly agree).

The version used in this study is an adaptation using only the items that measure optimism-pessimism (without the four filler items), changing the responses to indicate the frequency (rather than agreement), so the response options were “never,” “almost never,” “sometimes,” “almost always,” and “always,” scored from 0 to 4. This last change was appropriate, given that when applied to some items, they lose their negative orientation. Therefore, in some versions of the scale, it was considered that they mediated pessimism (Angelo et al., 2021; Garcia Cadena et al., 2021; Pan et al., 2017). In addition, we based our version on the Spanish version by Landero and González (2009) and adjusted the items to align with the original version by Scheier et al. (1994) as shown in Table 2.

Table 2.

Comparison of the Modifications from the LOT-R (Scheier et al., 1994) to the Spanish Version by Landero and González (2009) to the Version Proposed in this Paper.

No.	Original version Scheier et al. (1994)	Spanish version Landero and Gonzalez (2009)	Our change	Our version [translation into English]
1	In uncertain times, I usually expect the best	En tiempos difíciles, suelo esperar lo mejor	None	En tiempos difíciles, suelo esperar lo mejor [In hard times, I usually expect the best]
2	I’m always optimistic about my future	Siempre soy optimista en cuanto al futuro	“Siempre” [Always] is removed	Soy optimista en cuanto al futuro [I’m optimistic about the future]
3	Overall, I expect more good things to happen to me than bad	En general, espero que me ocurran más cosas buenas que malas	None	En general, espero que me ocurran más cosas buenas que malas [Overall, I expect more good things to happen to me than bad]
4	I hardly ever expect things to go my way	Rara vez espero que las cosas salgan a mi manera	“Rara vez” [hardly ever] is removed	Espero que las cosas salgan a mi manera [I expect things to go my way]
5	I rarely count on good things happening to me	Casi nunca cuento con que me sucedan cosas buenas	“Casi nunca” [rarely] is removed	Cuento con que me sucedan cosas buenas [I count on good things happening to me]
6	If something can go wrong for me, it will	Si algo malo me tiene que pasar, estoy seguro de que me pasará	Adapted to Spanish structure	Si algo malo me tiene que pasar, estoy seguro de que me pasará [If something bad must happen to me, I am sure it will occur]
7	I don’t get upset too easily	-	Not included	-
8	It’s important for me to keep busy	-	Not included	-
9	I enjoy my friends a lot	-	Not included	-
10	It’s easy for me to relax	-	Not included	-

The Spanish translation of the LOT-R, developed by Landero and González (2009), was faithful to the original proposal by Scheier et al. (1994). However, we identified that item 2 has a slightly different meaning. The original proposal asks about the person’s future, whereas the translated version makes inquiries about the future in general, which could exclude the participant. Consequently, three measurement models were evaluated: the original, the Spanish translation, and the translated version with seven items, including two versions of item 2, the one that deals with the participant’s future and the other that asks about the future in general; all models with frequency labels.

Satisfaction With Life Scale

López-Ortega et al. (2016) adapted the study by Diener et al. (1985) for the Mexican population. In this study, 13,220 adults aged 50 years or older (both women and men) from the 2012 Mexican Health and Aging Study participated. This scale has a unifactorial structure comprising five items (e.g., “In most ways, my life is close to my ideal”). López-Ortega et al. (2016) found the results’ reliability to be acceptable with a Cronbach’s alpha coefficient of .74, while the reported validity also obtained favorable results (X² = 12.05, p = < .001; KMO = .80), explaining 54.3% of the variance, and factor loads were between .61 and .83.

Statistical Analysis

The statistical programs used were Factor 12.04.02, JASP 0.17.3.0, and IBM SPSS 27 in its Statistics and AMOS versions. Internal consistency was analyzed using Cronbach’s alpha coefficient, McDonald’s omega coefficient, and the Greatest Lower Bound. Convergent validity was evaluated using the Average Variance Extracted. Criterion validity was determined by Pearson’s product-moment correlation coefficient, where the criterion was the Life Satisfaction construct, which was expected to correlate positively.

To determine construct validity, the total sample was divided into two random subsamples. The first subsample (n = 113) was used to explore the structure, following the addition of a new item, using Exploratory Factor Analysis (EFA) with the unweighted least squares extraction method. The number of factors was determined by Horn’s Parallel Analysis and Hull’s Method. The second subsample (n = 119) was used to test the EFA-derived model using Confirmatory Factor Analysis (CFA) with scale-free least squares method. Table 3 outlines the criteria for each test, which warranted the adjustment to the proposed measurement model based on their satisfaction.

Table 3.

Criteria for Statistical Tests.

Property	Statistics	Cut point	Reference
Collinearity	Kaiser-Meyer-Olkin MSA for scale	≥0.80	George and Mallery (2019), Lorenzo-Seva and Ferrando (2021)
	Kaiser-Meyer-Olkin MSA for item	≥0.50
	Bartlett’s sphericity test	p ≤ .050
	Communality (h²)	≥0.35	De Vellis (2012)
	Explained Variance (EVA)	≥50%	Cea (2004)
Absolute fit indices	Likelihood ratio test	p ≥ .050	Sahoo (2019)
	Goodness-of-fit index (GFI)	≥0.90
	Adjusted goodness-of-fit index (AGFI)	≥0.90
	Standardized root mean square residual (SRMR)	≤0.05
Incremental fit indices	Normed fit index (NFI)	≥0.90
Incremental fit indices	Relative fit index (RFI)	≥0.90
Parsimony-fit	Normed chi-square (X² /df)	1–3
Parsimony-fit	Parsimony-adjusted CFI, NFI y RFI	≥0.60	Morrison et al. (2017)
Internal consistency	Cronbach’s alpha (α)	≥.70	De Vellis (2012)
	Greatest lower bound (GLB)	≥0.70	Chakraborty (2017)
	McDonald’s omega (ω)	≥0.70	McDonald (1999)
Convergent validity	Average variance extracted (AVE)	≥0.50	Moral de la Rubia (2019)
Criterion validity	Pearson’s product-moment correlation	≥.70	Kerlinger and Lee (2002)

Note. MSA = measures of sampling adequacy; p = probability-value.

The LOT-R was initially proposed as a unidimensional model for measuring dispositional optimism, a concept widely used worldwide (Huffman et al., 2019). However, empirical studies on its psychometric properties have not yet reached a consensus on its structure, especially when cross-cultural adaptations are made.

In this study, the LOT-R has undergone substantial modifications. The main change was in the response system, which went from an agreement-based scale to a frequency-based scale. This modification is intended to remove the possibility that participants may interpret each item differently, as words can have different meaning. It included the removal of the negative orientation of two items. For example, “I rarely count on good things happening to me” (with agreement response) was changed to “I count on good things happening to me” (with frequency response). In addition, an item was included that comes from the study by Landero and González (2009), which did not provide a linear translation of an item from the original proposed by Scheier et al. (1994), but which added breadth to the measurement of dispositional optimism.

Considering the magnitude of these changes and the lack of consensus on the factorial structure, it was deemed methodologically appropriate to perform an EFA for the empirical exploration of the underlying structure under its new conditions (Ferrando & Anguiano-Carrasco, 2010; Knekta et al., 2019). Subsequently, a sequential CFA was performed to verify the dimensionality obtained from the EFA (Costello & Osborne, 2005; De Vellis, 2017; Morrison et al., 2017; Raykov & Marcoulides, 2011).

Results

Initially, the means and distributions of the scores obtained for each item, along with their correlations, were analyzed. The items met the assumption of univariate normality, with scores ranging from ±3 (George & Mallery, 2019) and statistically significant correlations were observed in most of these with the Pearson test (see Table 4).

Table 4.

Descriptives and Correlations Between Items (n = 113).

Item	M	SD	S	K	1	2	3	4	5	6	7
1. In tough times, I usually expect the best	3.07	0.78	−0.70	0.42	-
2. I’m optimistic about the future	3.26	0.59	−0.15	−0.50	0.66*	-
3. Overall, I expect more good things to happen to me than bad	3.38	0.56	−0.15	−0.84	0.56*	0.67*	-
4. I hardly ever expect things to go my way	2.81	0.71	0.14	−0.67	0.12	0.10	0.15	-
5. I count on good things happening to me	3.18	0.59	−0.05	−0.27	0.44*	0.51*	0.64*	0.39*	-
6. If something bad has to happen to me, I am sure it will happen	1.53	1.08	0.37	−0.48	0.03	−0.01	−0.10	0.14	−0.04	-
7. I’m optimistic about my future	3.35	0.59	−0.28	−0.65	0.43*	0.63*	0.68*	0.18	0.57*	−0.14	-

Note. M = mean; SD = standard deviation; S = skewness; K = Kurtosis (Zero centered).

p < .001.

Construct Validity

Exploratory Factorial Analysis (EFA)

The dispositional optimism measurement model encompassed three configurations. Table 5 presents the first model (M1) for the six items by Scheier et al. (1994), the second one (M2) for the translation items used by Landero and González (2009), and the third model (M3) for the seven items proposed in this study. The Kaiser-Meyer-Olkin (KMO), Measure of Sampling Adequacy (MSA), and Bartlett sphericity test verified the feasibility of factor extraction. Horn’s (1965) parallel analysis determined the number of factors, verified by the Hull Method. The extraction of factors was performed using the unweighted least squares extraction method, and the rotation of the factorial matrix was executed by the Normalized Direct Oblimin method (Lorenzo-Seva & Ferrando, 2021).

Table 5.

Initial Results of the EFA Between the Original Model, the Adaptation to the Spanish, and the Proposed Adaptation.

Items and factor analysis indicators	M1 (Scheier et al., 1994)			M2 (Landero & González, 2009)			M3 (proposed scale)
Items and factor analysis indicators	λ	h ²	MSA	λ	h ²	MSA	λ	h ²	MSA
1. In tough times, I usually expect the best	0.59	0.35	0.83	0.70	0.49	0.81	0.66	0.44	0.82
2. I’m optimistic about the future	-	-	-	0.80	0.65	0.76	0.81	0.65	0.80
3. Overall, I expect more good things to happen to me than bad	0.87	0.75	0.72	0.84	0.70	0.75	0.86	0.74	0.83
4. I hardly ever expect things to go my way	0.28	0.07	0.57*	0.24	0.06	0.53*	0.24	0.06	0.57*
5. I count on good things happening to me	0.77	0.59	0.77	0.71	0.51	0.74	0.72	0.52	0.82
6. If something bad has to happen to me, I am sure it will happen	−0.07	0.00	0.49*	−0.03	0.00	0.39*	−0.05	0.00	0.44*
7. I’m optimistic about my future	0.75	0.56	0.80	-	-	-	0.76	0.58	0.84
Bartlett’s statistic (df; p)	201 (15; <.001)			218.9 (15; <.001)			305.3 (21; <.001)
Kaiser-Meyer-Olkin MSA	0.75			0.74			0.81
Dimensions by parallel analysis	1			1			1
Dimensions by Hull method	1			1			1
Explained variance	46%			47%			49%

Item proposed to be removed based on Measure of Sampling Adequacy (MSA).

The Kaiser-Meyer-Olkin (KMO) and Measure of Sampling Adequacy (MSA) for items 4 and 6 was calculated by interval using the repetitive bias-corrected and accelerated method with a confidence interval of less than or equal to 0.50, indicating that these two items should be eliminated (Goretzko et al., 2021). Next, we performed the same analyses without the deleted items (4 and 6). In addition, some goodness-of-fit indices were included (see Table 6), namely, the non-normed fit index (NNFI), comparative fit index (CFI), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), and the square root of mean square approximation error (RMSEA).

Table 6.

Results of the EFA: The Original Model (M1), Adaptation to Spanish (M2), and the Proposed Adaptation of This Study (M3), Excluding the Two Items That Did Not Pass the Test.

Items and factor analysis indicators	M1 (Scheier et al., 1994)			M2 (Landero & González, 2009)			M3 (proposed scale)
Items and factor analysis indicators	λ	h ²	MSA	λ	h ²	MSA	λ	h ²	MSA
1. In tough times, I usually expect the best	0.60	0.36	0.85	0.71	0.50	0.80	0.67	0.45	0.82
2. I’m optimistic about the future	-	-	-	0.83	0.68	0.77	0.82	0.68	0.80
3. Overall, I expect more good things to happen to me than bad	0.90	0.80	0.73	0.85	0.71	0.75	0.87	0.75	0.83
5. I count on good things happening to me	0.73	0.53	0.83	0.67	0.45	0.80	0.69	0.48	0.87
7. I’m optimistic about my future	0.90	0.56	0.79	-	-	-	0.76	0.58	0.84
X² minimum fit function (df; p)	0.15 (2; .927)			1.78 (2; .410)			3.05 (5; .695)
Kaiser-Meyer-Olkin MSA	0.79			0.78			0.83
Dimensions by parallel analysis	1			1			1
Dimensions by Hull method	-			-			1
Explained variance	66%			67%			67%
X ² /df	0.30			0.89			0.61
NNFI [BC Bootstrap 95% IC]	0.99 [0.99–0.99]			0.91 [0.61–0.99]			0.96 [0.88–0.99]
CFI [BC Bootstrap 95% IC]	0.99 [0.95–0.99]			0.97 [0.95–0.99]			0.98 [0.94–0.99]
GFI [BC Bootstrap 95% IC]	0.99 [0.99–0.99]			0.99 [0.99–0.99]			0.99 [0.99–0.99]
AGFI [BC Bootstrap 95% IC]	0.99 [0.99–0.99]			0.99 [0.96–0.99]			0.99 [0.98–0.99]
RMSEA [BC Bootstrap 95% IC]	0.00 [0.00–0.19]			0.19 [0.03–0.33]			0.11 [0.01–0.19]

Note. Hull method is affected to small numbers of items in dimensions. λ = factor loading; h² = communality; MSA = measure of sampling adequacy.

Based on the above, the explorative adjustment of the three unifactorial models was adequate for the data. Notably, M3 performed slightly better in terms of internal consistency and explained variance, while M1 performed better in terms of goodness of fit.

Confirmatory Factorial Analysis (CFA)

The structures derived from the EFA corresponding to M1 and M3 were contrasted in the CFA. M2 was excluded because it had the lowest values in the adjustment indices and a score far from desirable in RMSEA. M1 and M3 were identified with their respective items and independent errors, and their parameters were estimated using the scale-free least squares method and interval by repetitive sampling method of bias-corrected percentiles.

Only items with factor scores equal to or greater than 0.50 were maintained to determine the CFA adjustment indices, as Hair et al. (2019) recommended. The evidence for the validity of both models was excellent, although the Scheier et al. (1994) model (M1) yielded slightly better results than our proposed model M3 (see Table 7).

Table 7.

Factor Loads and CFA Adjustment Indices of the Models to Measure Optimism.

Item					M1 (Scheier et al., 1994)			M3 (proposed scale)
1. In tough times, I usually expect the best					0.75			0.78
2. I’m optimistic about the future					-			0.89
3. Overall, I expect more good things to happen to me than bad					0.77			0.79
5. I count on good things happening to me					0.77			0.74
7. I’m optimistic about my future					0.83			0.80
	Fit indices
Models	X ²	df	p	X ² /df	GFI	AGFI	RMR	SRMR	NFI	RFI
M1 (Scheier et al., 1994)	0.17	2	.929	0.09	1	1	0.01	0.01	0.99	0.99
M3 (proposed scale)	1.34	5	.931	0.27	0.99	0.99	0.01	0.03	0.99	0.99

Criterion Validity

The Satisfaction with Life scale designed by Diener et al. (1985) served as a criterion for concurrent validity. Both Scheier’s model, M1 (1994, r = .41, p < .001, Fisher’s z = 0.44, CI 95% [0.32, 0.99]), and our proposed model, M3, (r = .41, p < .001, Fisher’s z = 0.44, 95% CI [0.32, 0.99]) correlated with the Satisfaction with Life scale.

Convergent Validity and Reliability

The Average Variance Extracted (AVE) was used to calculate convergent validity. M1 obtained a score AVE of 0.61 and M3 was equal to 0.64; thus, both scores were acceptable (Moral de la Rubia, 2019). The reliability of the models was determined by the internal consistency coefficients calculated with McDonald’s omega, Cronbach’s alpha, and the Greatest Lower Bound. Table 8 shows the results for each method. The scores can be interpreted as good (De Vellis, 2012; McDonald, 1999; Nunnally & Bernstein, 1994).

Table 8.

Evidence of Reliability by Model.

Estimate	McDonald’s ω	Cronbach’s α	Greatest lower bound
M1 (Scheier et al., 1994)
Point estimate	0.84	.84	0.85
Lower 95% CI	0.81	.80	0.82
Upper 95% CI	0.87	.87	0.89
M3 (proposed model)
Point estimate	0.89	.88	0.91
Lower 95% CI	0.86	.86	0.89
Upper 95% CI	0.91	.91	0.93

Discussion

The main objective of this research was to adapt the LOT-R to a response system that counts frequencies and evaluates its psychometric properties among Mexican university faculty members. To meet this objective, the researchers assessed construct validity through exploratory and confirmatory factor analyses. They employed three internal consistency coefficients (Cronbach’s alpha, McDonald’s omega, and the Greatest Lower Bound) to evaluate reliability. The evidence suggests that adapting the instrument to allow participants to report frequency can yield valid and reliable data about university teachers’ optimism. Likewise, it was demonstrated that the faithfully translated instrument items used in M1 (Scheier et al., 1994) were a more suitable option than the slightly adapted model M2 by Landero and González (2009).

In general, the factorial structure of the LOT-R was shown not to be stable, as some studies reported two-dimensional models (Garcia Cadena et al., 2021b; Hinz et al., 2022; Kennes et al., 2021; Pan et al., 2017; Valdelamar-Jiménez & Sánchez-Pedraza, 2017; Vera-Villarroel et al., 2009), while others reported a one-dimensional structure (García et al., 2013; Scheier et al., 1994; Suryadi et al., 2021). The present adaptation study adds to the understanding of unidimensional structures.

According to Pedrosa et al. (2015), another limitation of the LOT-R scale is its questionable reliability, as evidenced by the reported coefficients are reduced or low. Studies have reported Cronbach’s alpha coefficients below .50 (García et al., 2013; Sanin & Salanova Soria, 2017), within the range of .51 to .60 (Hinz et al., 2022; Pan et al., 2017; Rondón Bernard & Angelucci Bastidas, 2016; Zenger et al., 2013), one between .61 and .70 (Garcia Cadena et al., 2021), and others between .71 and .80 (Angelo et al., 2021; Hinz et al., 2022; Scheier et al., 1994; Suryadi et al., 2021). Only one study reported a score higher than 0.80 (Landero & González, 2009). The present adaptation of the LOT-R obtained Cronbach’s alpha equal to .88.

On the other hand, few studies used McDonald’s omega composite reliability coefficient. Those reporting it indicated values equal to or less than 0.66 (Garcia Cadena et al., 2021b; Kennes et al., 2021). However, the coefficient in the present adaptation of the LOT-R was .89, a good value that complements Cronbach’s alpha. Notably, both coefficients were valid because the factorial loads were homogeneous, fulfilling the assumption of Tau-equivalent items required by Cronbach’s alpha coefficient (Goodboy & Martin, 2020).

Several Latin American studies have validated the LOT-R measurement model with Confirmatory Factor Analysis. Venezuela (Rondón Bernard & Angelucci Bastidas, 2016), Colombia (Hinz et al., 2022; Zenger et al., 2013), and Brazil (Angelo et al., 2021) maintain a two-dimensional structure; however, validations involving diverse Mexican populations found evidence of a two-dimensional model (Landero & González, 2009) and another of unidimensionality (García et al., 2013). Most of the studies reviewed meet the absolute and incremental fit indices, except for García et al. (2013). In this case, the researchers modified the scale’s response format to range from 1 (Yes) to 4 (No), a change that appears to have impacted its psychometric properties. More recently validated scales in other countries, such as the United States (Garcia Cadena et al., 2021; Pan et al., 2017) and the Netherlands (Kennes et al., 2021), have also supported two-dimensional models with appropriate fit indices.

To shorten the discussion, Appendix 1 compares this adaptation and previous studies.

Conclusions

The frequency response system, employed in the LOT-R to measure optimism, demonstrated a better fit and higher accuracy than the agreement-based system. Frequency-based statements are more suitable for assessing optimism as they encourage direct and precise responses, minimizing the need for respondents to overthink their answers. Similarly, the frequency-based system shifts focus from measuring beliefs or opinions to describing actual life experiences, resulting in greater fidelity to the realities experienced by the population rather than to their perceptions of those experiences.

The primary limitation of this study is the small sample size. Other limitations include the use of incidental or convenience sampling and the involvement of only faculty from a higher-level private university. Consequently, the conclusions drawn in this study should be considered in that context in which they are presented. However, this study can help (a) those who want to use the data results for comparison, (b) as a basis for more sophisticated or robust studies examining the use and dissemination of the LOT-R scale, and (c) those interested in studying the university faculty. For future studies, some recommendations are (1) use a sample with a larger number of participants, (2) incorporate the use of techniques such as Rasch analysis to have greater precision regarding the behavior of the participants’ responses for each item, and (3) collect responses from professors from public institutions and other disciplines.

The main contribution of this study was adapting the LOT-R scale to use frequency response items in an application to a population of Mexican university teachers. It represents one of the few contributions measuring teachers’ optimism in Mexico. Using scales that measure the virtues of teachers is an educational innovation, as this type of construct is not typically explored in this population. Knowing the teachers’ strengths and areas for improvement is an advantage for institutions, as it provides information to develop their talent and training better. Valuing and promoting teacher optimism can create a healthy school environment that is conducive to learning, with proactive individuals who fulfill their activities and expecting positive results.

Footnotes

Appendix

Appendix 1.

Comparison of Evidence of Validity and Reliability of Scales to Measure Optimism.

	This study	1	2	3	4	5	6	7	8	9	10
Country	Mexico	USA			Colombia		Chile	Venezuela	Brazil	Mexico and others	Mexico
Sample	University Teachers	Students	Hispanic American	Students	General	Workers	General	Diabetics	Young athletes	Sick people	Relatives of sick people
n	232	2,055	422	100	1,500	791	309	300	953	154	350
Scale	0–4	0–4	1–4	1–5	0–4	1–4	1 a 4	0–4	1–7	1–5	1–4
Labels	Frequency	Agreements									Affirmation
Exploratory factorial analysis
X² (p)	0.15 (.927)			-	-	-	249.48 (.010)	139.42 (<.001)	-	14.98	-
KMO	0.79			-	-	-	0.72	0.65	-	0.77	-
λ	0.73–0.90	0.58–0.79		-	-	-	−0.48.6–0.79	0.30–0.60	-	0.42–0.84	-
Extraction	Unweighted Least Squares	Principal components	-	-	-	-	Principal components	-	-	-	-
Rotation	Oblimin Direct Standardized	Varimax	-	-	-	-	Varimax	-	-	-	-
Criterion factors	Parallel Analysis	Eigenvalues	-	-	-	-	-	-	-	Kaiser	-
Dimensions	1	1	-	-	-	-	2	2	-	2	1
Explained variance	66%	48.10%	-	-	-	-	55.55%	-	-	83.1%	-
Confirmatory factorial analysis
Absolute fix index
X²	0.17	16.51	13.95	-	30.87	13.54	-	20.9	4.54	14.98	-
df	2	6	8	-	8	19	-	13	8	8	-
p	.919	.01	.083	-		.094	-	.080	-		-
X² /df	0.09	-	-	2.18	3.86	-	-	-	-	1.87	3.05
GFI	1	-	-	0.97	0.99	-	-	0.98	-	0.97	-
AGFI	1	-	-	0.93	-	-	-	0.96	-	0.93	-
RMR	0.01	0.01	-	0.97	-	-	-	0.04	-	0.06	-
SRMR	0.01	-	0.04	0.08	-	-	-	0.04	0.02	0.07	-
RMSEA	-	-	0.06	0.11	0.04	0.03	-	0.05	0	0.07	0.08
Incremental fit indices
NFI	0.99	-	-	0.75	0.98	-	-	-	-	0.94	-
TLI	-	-	-	0.72	0.5	0.96	-	-	1	0.95	-
CFI	-	-	0.94	0.83	0.98	0.98	-	0.96	1	0.97	0.82
RFI	0.99	-	-		-	-	-	-	-	-	-
Items	5	6	6	5	6	6	-	7	6	6	5
λ	0.75–0.83		0.25–0.68		0.34–0.76		-	0.30–0.63	0.60–0.78	0.42–0.84	0.26–0.50
Estimation	Scale-free least squares	-	Maximum Likelihood Robust	Maximum Likelihood	Maximum Likelihood	Maximum Likelihood	-	Maximum Likelihood	Robust Weight Least Square	Maximum Likelihood	Weight Least Square
Dimensions	1	1	2	2	2	2	-	2	2	2	-
Reliability
α Cronbach	.88	.78	.57	.61	.58	.53–.47	.65	<.60	.72–.70	.81	.31
ω McDonald	0.89			0.61	-		-	-	-		-
GLB	0.91				-		-	-	-		-

Note. 1 = Scheier et al. (1994); 2 = Pan et al. (2017); 3 = García Cadena et al. (2019); 4 = Zenger et al. (2013); 5 = Sanin and Salanova Soria (2017); 6 = Vera-Villarroel et al. (2009); 7 = Rondón Bernard & Angelucci Bastidas (2016); 8 = Angelo et al. (2021); 9 = Landero and González (2009); 10 = García et al. (2013).

Acknowledgements

The authors acknowledge the financial and technical support of the Writing Lab, Institute for the Future of Education, Tecnologico de Monterrey (Mexico), in the production of this work. The authors thank all the participants who made this study possible.

ORCID iDs

Jesus Alfonso Beltran-Sanchez

Jose Moral de la Rubia

Angeles Dominguez

Mónica Teresa González Ramírez

Ethical Considerations

In compliance with the rules established by the American Psychological Association (), institutional approval was obtained, allowing access to the field. The study was approved as a low-risk project by the Institutional Committee on Research Ethics at the institution where it was conducted, receiving an exemption letter with the tracking code EMCS-22-003-1.

Consent to Participate

The participants were informed of the study's purpose, estimated duration, and the various stages of the investigation. Moreover, they were given the option to refuse participation and were provided with an address to submit questions and concerns about the project. Additionally, the research guaranteed the anonymity of participants' answers and the exclusive academic use of the information obtained.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data Availability Statement

The data sets analyzed for this study will be available from the corresponding author upon reasonable request.

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Adaptation and Validation of Life Orientation Test: Revised as a Measure for Dispositional Optimism in a Mexican Sample of University Teachers

Abstract

Keywords

Introduction

Method

Design

Sample

Instruments

Life Orientation Test-Revised (LOT-R)

Satisfaction With Life Scale

Statistical Analysis

Results

Construct Validity

Exploratory Factorial Analysis (EFA)

Confirmatory Factorial Analysis (CFA)

Criterion Validity

Convergent Validity and Reliability

Discussion

Conclusions

Footnotes

Appendix

Acknowledgements

ORCID iDs

Ethical Considerations

Consent to Participate

Funding

Declaration of Conflicting Interests

Data Availability Statement

References