confirmatory factor analysis thesis

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• v.7(Spec Issue); 2011 Dec

Language: English | French

An Overview of Confirmatory Factor Analysis and Item Response Analysis Applied to Instruments to Evaluate Primary Healthcare

Aperçu de l'analyse factorielle confirmatoire et de l'analyse de réponse par item appliquées aux instruments d'évaluation des soins primaires, darcy a. santor.

School of Psychology, University of Ottawa, Ottawa, ON

Jeannie L. Haggerty

Department of Family Medicine, McGill University, Montreal, QC

Jean-Frédéric Lévesque

Centre de recherche du Centre hospitalier de l'Université de Montréal, Montréal, QC

Frederick Burge

Department of Family Medicine, Dalhousie University, Halifax, NS

Marie-Dominique Beaulieu

Chaire Dr Sadok Besrour en médecine familiale, Centre de recherche du Centre hospitalier de l'Université de Montréal, Montréal, QC

Raynald Pineault

This paper presents an overview of the analytic approaches that we used to assess the performance and structure of measures that evaluate primary healthcare; six instruments were administered concurrently to the same set of patients. The purpose is (a) to provide clinicians, researchers and policy makers with an overview of the psychometric methods used in this series of papers to assess instrument performance and (b) to articulate briefly the rationale, the criteria used and the ways in which results can be interpreted. For illustration, we use the case of instrument subscales evaluating accessibility. We discuss (1) distribution of items, including treatment of missing values, (2) exploratory and confirmatory factor analysis to identify how items from different subscales relate to a single underlying construct or sub-dimension and (3) item response theory analysis to examine whether items can discriminate differences between individuals with high and low scores, and whether the response options work well. Any conclusion about the relative performance of instruments or items will depend on the type of analytic technique used. Our study design and analytic methods allow us to compare instrument subscales, discern common constructs and identify potentially problematic items.

Cet article présente un aperçu des approches analytiques que nous avons utilisées pour évaluer le rendement et la structure des mesures qui servent à évaluer les soins de santé primaires : six instruments ont été appliqués simultanément au même groupe de patients. L'objectif est (a) de fournir aux cliniciens, aux chercheurs et aux responsables de politiques, un aperçu des méthodes psychométriques utilisées dans cette série pour évaluer le rendement de l'instrument et (b) d'articuler brièvement l'analyse raisonnée, les critères employés et les façons dont peuvent être interprétés les résultats. À titre d'exemple, nous avons utilisé le cas des sous-échelles qui servent à évaluer l'accessibilité. Nous discutons (1) la distribution des items, y compris le traitement des valeurs manquantes, (2) les analyses factorielles exploratoires et confirmatoires afin de voir comment les items de différentes sous-échelles sont liés à un seul construit (ou sous-dimension) sous-jacent et (3) l'analyse de réponse par item pour voir si les items permettent de discriminer les différences entre les unités qui présentent des scores élevés et faibles, et pour voir si les choix de réponses fonctionnent bien. Toute conclusion sur le rendement relatif des instruments ou des items dépend du type de technique analytique employé. La conception et les méthodes analytiques de cette étude permettent de comparer les sous-échelles des instruments, de discerner les construit communs et de repérer les items potentiellement problématiques.

Psychometric scales and instruments have been used to assess virtually every component of healthcare, whether to identify gaps in service, to assess special needs of patients, or to evaluate the performance and efficiencies of programs, organizations or entire healthcare systems. In this special issue of Healthcare Policy , we examine the performance of several instruments that assess different attributes of primary healthcare from the patient's perspective. Any conclusion about the relative performance of instruments and items from those instruments will depend on the type of analytic technique used to assess performance.

The purpose of this paper is to describe the analytic approach and psychometric methods that we used to assess and compare the performance of the instrument subscales. We articulate the rationale of different approaches, the criteria we used and how results can be interpreted. For illustration, we use the case of instrument subscales evaluating accessibility, described in detail elsewhere in this special issue ( Haggerty, Lévesque et al. 2011 ).

Data Sources

All the instruments used in this study have previously been validated and meet standard criteria for validity and reliability. The goal of the study was to extend this process of validation and compare the performance of the six instruments for measuring core attributes of primary healthcare for the Canadian context. Our intention is not to recommend one instrument over another, but to provide insight into how different subscales measure various primary healthcare attributes, such as accessibility and care.

We administered the six instruments to 645 health service users in Nova Scotia and Quebec: the Primary Care Assessment Survey (PCAS, Safran et al. 1998 ); the adult version of the Primary Care Assessment Tool – Short (PCAT-S, Shi et al. 2001 ); the Components of Primary Care Index (CPCI, Flocke 1997 ); the first version of the EUROPEP ( EUROPEP-I, Grol et al. 2000 ); the Interpersonal Processes of Care – 18-item version (IPC-II, Stewart et al. 2007 ); and the Veterans Affairs National Outpatient Customer Satisfaction Survey (VANOCSS, Borowsky et al. 2002 ). The sample was balanced by overall rating of primary healthcare, high and low level of education, rural and urban location, and English and French language ( Haggerty, Burge et al. 2011 ).

Distribution of Responses

The first step was to examine the distribution of the responses, flagging as problematic items where a high proportion of respondents select the most negative (floor effect) or the most positive (ceiling effect) response options, or have missing values. Missing values, where respondents failed to respond or wrote in other answers, may indicate questions that are not clear or are difficult to understand. We expected low rates of true missing values because truly problematic items would be eliminated during initial validation by the instrument developers, but remained sensitive to items that are problematic in the Canadian context.

However, some instruments offer response options such as “not applicable” (EUROPEP-I) or “not sure” (PCAT-S), which count as missing values in analysis because they cannot be interpreted as part of the ordinal response scale. They represent a loss of information as they cannot be interpreted.

Missing information, for whatever reason, is problematic for our study given that missing information on any item means that data for the entire participant is excluded from factor analysis (listwise missing), compromising statistical power and potentially introducing bias. In the case of accessibility subscales, 340 of the 645 respondents (53%) were excluded from factor analysis because of missing values, of which 267 (79%) were for selecting “not sure” or “not applicable” options. We examined the potential for bias by testing for differences between included and excluded respondents on all relevant demographic and healthcare use variables. We also imputed values for most of the missing values using maximum likelihood imputation ( Jöreskog and Sörbom 1996 ), which uses the subject's responses to other items and characteristics to impute a likely value. Then, we repeated all the factor analyses to ensure that our conclusions and interpretations remained unchanged, and reducing the possibility of bias. Nonetheless, the high proportion of missing values in some instances is an important limitation of our study, and needs to be considered in the selection of instruments.

Subscale Scores

Next, we examined the performance by subscale. Subscale scores were mostly calculated as the mean of item values if over 50% of the items were complete. This score was not affected by the number of items, and it reflects the response options. For example, a subscale score of 3.9 in Organizational Access on the PCAS corresponds approximately to the “4=good” option on the response scale of 1 (very poor) to 6 (excellent). But it is difficult to know how this compares to the score of 3.6 on the EUROPEP-I Organization of Care for a similar dimension of accessibility from 1 (poor) to 5 (excellent). To compare the subscales between different response scales, we normalized scores to a common 0-to-10 metric using the following formula:

New score = {(raw score − minimum possible) / (raw maximum − raw minimum)} * 10

So, the normalized mean for PCAS Organizational Access, 5.9, is seen to be considerably lower than 6.5 on the EUROPEP-I Organization of Care, and the PCAS variance is lower than the EUROPEP-I (1.8 vs. 2.4). Thus, if accessibility were measured in one population using the PCAS and in another using the EUROPEP-I, the scores of the EUROPEP-I would be expected to be higher than the PCAS, even if there were no difference in accessibility between the two populations.

Reliabilities

The reliability of each subscale was evaluated using Cronbach's coefficient a, which estimates how much each item functions as a parallel, though correlated, test of the underlying construct. Cronbach's a ranges from 0 (items completely uncorrelated, all variance is random) to 1 (each item yields identical information), with the convention of .70 indicating a minimally reliable subscale. The subscales included in our study all reported adequate to good internal consistency. The coefficient a is sensitive to sample variation as well as the number and quality of items. Given that our study sample was selected to overrepresent the extremes of poor and excellent experience with primary healthcare relative to a randomly selected sample, we expected our alpha estimates to meet or exceed the reported values.

We then calculated Pearson correlations among the subscale scores, controlling for educational level, geography and language (partial correlation coefficients) to account for slight deviations from our original balanced sampling design. We expect high correlations between subscales mapped to the same attribute (convergent validity) and lower correlations with subscales from other attributes (some degree of divergent validity), indicating that the items in the subscales are indeed specific to that attribute and that respondents appropriately distinguish between attributes. Pearson correlation coefficients indicate expected relationships observed in factor analysis. If we observe high correlations within an attribute, then we would expect all the items from those subscales to “load” on a common factor. So, for example, after the correlation analysis for accessibility, we observed that the PCAT-S First-Contact Utilization subscale correlated less strongly with the other accessibility subscales than it did with relational continuity. We had high expectations that its items would not only form a separate factor, but also that it would relate poorly to accessibility as a whole.

Exploratory and Confirmatory Factor Analysis

Subscales from different instruments that were designed to assess the same primary healthcare attribute should relate to a single underlying factor or construct. We used both exploratory and confirmatory factor analysis to examine this premise, as well as to determine whether items across subscales related to sub-dimensions within the attribute.

Exploratory analyses

Exploratory factor analysis is a descriptive technique that can detect an overarching structure that explains the relationships between items in a parsimonious way. We used the common factor analysis procedure computed in SAS v. 9.1 ( SAS 2003 ). This procedure identifies how much items can be represented by a smaller group of variables (i.e., common factors) that account for as much of the variability in the data as possible. The procedure assigns an eigenvalue to each factor that corresponds to the total variance in item responses that can be explained by the factor. Typically, factors with eigenvalues greater than 1.0 are retained.

This procedure also computes how strongly each individual item maps on to each factor. “Factor loadings” range from –1.0 to 1.0 and can be interpreted much like a correlation coefficient. These indicate (a) the extent to which all items relate to one or more distinctive factors, (b) how strongly each item is related to each factor (and whether the item should be retained or eliminated within a factor) and (c) how much variation in responses to items can be accounted for by each factor or subgroup. We considered items with factor loadings ≥| .4 | as strongly related to the underlying factor. It is important to note that common factor analysis assumes a normal distribution; items with highly skewed distributions will affect both the loadings and the extent to which factors can be easily interpreted ( Gorsuch 1983 ).

Results of an exploratory factor analysis for items in subscales from three different instruments assessing accessibility are presented in Table ​ Table1. 1 . Factor loadings are presented for each item showing how each item is related to three distinct factors. The first factor has a large eigenvalue (7.84) and accounts for approximately 41% of variance in the responses given to items, compared to just 6% for the second factor (eigenvalue=1.19) and less than 1% for the third. As a result, only two factors would be considered worth interpreting. This confirms our expectation based on the correlation analysis that the PCAT-S First-Contact Utilization subscale might not fit with other accessibility subscales. The two important underlying factors could be characterized as timeliness and accommodation ( Haggerty, Lévesque et al. 2011 ).

Factor loadings from an oblique exploratory principal component analysis for accessibility items drawn from four measures of accessibility

Note: Factor loadings smaller than 0.40 have not been presented.

Confirmatory analyses

Confirmatory factor analysis differs from exploratory factor analysis by allowing the investigator to impose a structure or model on the data and test how well that model “fits.” The “model” is a hypothesis about (a) the number of factors, (b) whether the factors are correlated or uncorrelated and (c) how items are associated with the factor. Models with different configurations are compared using structural equation modelling. Statistical software packages produce various “goodness-of-fit” statistics that capture how well the implied variance–covariance matrix of the proposed model corresponds to the observed variance–covariance matrix (i.e., how items from the instrument actually correlate). Confirmatory factor analysis attempts to account for the covariation among items (ignoring error variance), whereas common factor analysis accounts for the “common variance” shared among items, differentiating variance attributable to an underlying factor and error variance. Although similar in spirit, factor loadings are computed in fundamentally different ways from common factor analysis and should be interpreted differently.

Our premise was that different measures of an attribute can still be viewed as indicators (i.e., items) assessing the same underlying construct despite being drawn from different instruments employing different phrasing and response scales. Testing this hypothesis allows researchers and policy makers to view similarly the results obtained from different measures of, say, accessibility.

Figure ​ Figure1 1 presents the results of a confirmatory factor analysis for accessibility subscales. Figure ​ Figure1A 1 A depicts a standard unidimensional model, where every item is linked to the same, single underlying construct, namely accessibility. Constructs (designated with ellipses) are linked to (designated with arrows with loading coefficients) individual items (designated with rectangular boxes). The model shows that most items load strongly (i.e., factor loading greater than .90) on the latent construct called Access, but that some items do not (e.g., loadings of .71 and .78) or they have high residual error, shown to the right of the item.

Results of a confirmatory factor analysis for accessibility subscales

The performance of the model is evaluated by examining the ensemble of “goodness-of-fit” statistics, such as the comparative fit index (CFI), the normed fit index (NFI), consistent Akaike's information criterion (CAIC) or the root mean squared error of approximation (RMSEA). They all assess in different ways the discrepancy between the pattern of variances and covariances implied by the model and the actual pattern of variances and covariances observed in the data (see Kline 1998 for an in-depth review of basic issues in structural equation modelling). If the implied pattern is close to what is observed in the data, then the model is said to fit – it accurately accounts for the manner in which items are interrelated.

Fit statistics for the model in Figure ​ Figure1A 1 A were all good. Unlike the usual interpretation of significance, lower chi-squared (χ 2 ) values suggest better fit. The χ 2 value was 649 with 152 degrees of freedom and was significant, which might indicate the model does not fit well (though χ 2 is sensitive to large samples such as ours). However, other fit statistics, such as NFI, CAIC and GFI (results not shown), which take into consideration both the sample size and model complexity, were all well above the conventional criterion of .90 of “good fit.” The RMS EA of .104 is higher than the .05 criterion indicating good fit, but is still reasonable. Altogether, these results suggest that although items were drawn from distinct subscales, response to questions can be accounted for by a single underlying construct, namely, accessibility.

However, we might hypothesize that because items were drawn from subscales with different numbers of response options and formats, the pattern of responses would be even better explained by a model that explicitly locates individual items with the subscale from their parent instrument (first-order factor) and then links these first-order factors within the general construct of accessibility (a second-order factor). Figure ​ Figure1B 1 B depicts this second-order, multidimensional model. Fit statistics for this model were also extremely good. Again, NFI, CAIC and GFI were all .98. The χ 2 value was 514 (with 148 degrees of freedom).

Comparing models

One of the strengths of a confirmatory factor analysis is the ability to compare “nested” models, where one model is a simpler version of a more complex model. Because these models differ only in the number of paths that are being estimated, χ 2 values for one model can be subtracted from the other and the significance of the difference evaluated. The χ 2 difference between the simple model in Figure ​ Figure1A 1 A and the more complex model in Figure ​ Figure1B 1 B (649 – 514 = 135, 4 df ) is statistically significant, so we can infer that the complex model is more valid. Some of the variability in how individuals respond to questions is not just determined by the underlying construct of accessibility, but also by the specific measure from which the question is drawn.

We also test a model that groups items within sub-dimensions of accessibility. Figure ​ Figure1C 1 C depicts a second-order, multidimensional model in which items are grouped within two first-order factors, namely, the timeliness of service and the extent to which patients' access barriers are accommodated, which are themselves part of a broader, second-order factor: accessibility. This model says there are two components of the more general construct of accessibility, and that these transcend specific instrument subscales.

It is important to note that not all models can be compared directly. The model in Figure ​ Figure1C 1 C does not include the items from the PCAT First-Contact Utilization subscale; it differs from the model in Figure ​ Figure1A 1 A by more than the number of paths. To test the validity of this model, we compared its own restricted version rather than the model depicted in Figure ​ Figure1A 1 A and found that grouping items within sub-factors of timeliness and accommodation is superior to the one-dimensional model (χ 2 426 – 364 = 52, 3 degrees of freedom).

Item Response Models

Item response analysis evaluates how well questions and individual response options perform at different levels of the underlying construct being evaluated. They provide a fine-grained level of analysis that can be used to evaluate how well individual items and options discriminate among individuals at both high and low levels of the construct and can identify items and options that should ideally be revised or, if necessary, discarded.

Defining a shared or common underlying continuum against which items can be compared is crucial, because any result will be contingent on the appropriateness of this common underlying dimension. Each item's performance was modelled two ways, as a function of (1) items drawn from the single original subscale and (2) all items from any subscale that appear to measure a common construct, for example, timeliness within accessibility. Items are likely to perform better when modelled as a function of just the instrument from which they were drawn. However, because our goal is to compare the relative performance of all items (which may come from different instruments) that are believed to assess a similar construct (i.e., accessibility), we report on items modelled on the shared or common underlying dimension (e.g., timeliness within accessibility).

We used a non-parametric item response model to examine item performance on the common underlying factor ( Ramsay 2000 ). This is an exploratory approach ( Santor and Ramsay 1998 ), and these techniques have been used previously to examine the psychometric properties of self-report items and to evaluate item bias ( Santor et al. 1994 ; Santor et al. 1995 ; Santor and Coyne 2001 ). A detailed description of the algorithm used to estimate response curves has been published elsewhere ( Ramsay 1991 ).

We supplemented our non-parametric models with parametric item response modelling to estimate the discriminatory capacity of each item within its own original subscale using Multilog ( Du Toit 2003 ). Discriminability (the “a” parameter) indicates an item's sensitivity to detect differences among individuals ranked on the construct being measured (e.g., accessibility). It can be viewed as a slope, with a value of 1 considered the lower limit for acceptable discriminability, i.e., each unit increase in the item predicts a unit increase in the underlying construct. Items with lower discriminability in the parent construct invariably performed poorly on the common underlying dimension with non-parametric item response modelling.

Examining item performance

To illustrate how item response models can be used to evaluate item and response option performance, Figures ​ Figures2A, 2 A, ​ A,2B 2 B and ​ and2C 2 C show item response graphs for three items drawn from the PCAT-S and EUROPEP-I subscales assessing construct of timeliness within accessibility. Figure ​ Figure2A 2 A presents a relatively well-performing item from the PCAT-S; Figures ​ Figures2B 2 B and ​ and2C 2 C illustrate some difficulties in the other two items.

Response graphs for three items drawn from the PCAT-S and EUROPEP-I subscales assessing construct of timeliness within accessibility

In the Figure ​ Figure2 2 graphs, the total expected score for timeliness is presented at the top of the plot; below the horizontal axis on the bottom, it is represented as standard normal scores. Expressing scores as standard normal scores is useful because it is informative about the proportion of a population above or below integer values of standard deviations from the mean score. So in the graphs we can see that −2 SD corresponds to a total timeliness score of 18.3, the mean is 30.8, and +2 SD is 27.1. Extreme values on curves need to be interpreted with caution because, by definition, sample sizes are small in the tails of the overall distribution of scores.

The overall performance of the item is captured in the steepness of the slope of the characteristic curve (the topmost dashed lines in Figure ​ Figure2A–C), 2 A–C), which expresses item discriminability, the relationship between the cumulated item score and the total score in the construct (e.g., total timeliness). Given that we calculated items from different instruments as a function of a common continuum, slopes can be compared directly to assess performance across different subscales.

Several important features of item performance are illustrated in Figure ​ Figure2A 2 A for an item from the PCAT-S. First, each of the option characteristic curves (a solid line probability curve for each response option) increases rapidly with small increases in timeliness. For example, the probability of option 1 being endorsed increases rapidly from 0.0 to 0.6 over a narrow region of timeliness, –3.0 to –1.5. Second, each option tends to be endorsed most frequently in a specific range of timeliness. For example, option 2 is more likely to be endorsed than any other option within the timeliness range of –1.0 to 0.0. Third, the regions over which each option is most likely to be endorsed are ordered, left to right, in the same way as the option scores (weights, 1 to 4). That is, the region in which option 2 is most likely to be endorsed falls between the regions in which option 1 and option 3 are most likely to be endorsed. Finally, together, the options for an item span the full continuum of accessibility, from –3 to +3. Most positive options are endorsed only at high levels of timeliness (e.g., option 5), whereas most negative options are endorsed only at low levels of timeliness (e.g., option 1).

In contrast, Figure ​ Figure2B 2 B shows an item from the PCAT-S with four response options, but only options 1 and 4 are endorsed frequently. Options 2 and 3 do not provide any meaningful additional information, and the response scale functions essentially as a binary option. However, the responses cover specific and distinct areas of timeliness, making the item very discriminating, as illustrated by the steep slope of the item characteristic curve.

Figure ​ Figure2C 2 C illustrates a problematic item. The response option curves do not peak rapidly nor in specific areas of timeliness, and the response options do not seem to be ordinal. The item characteristic curve is almost flat, showing little capacity for discrimination. It does not perform well to measure timeliness, which may not be surprising given that it asks about helpfulness of staff.

Each of the techniques described above offers a different method of examining item and subscale performance; applied together, they offer a comprehensive assessment of how the selected instruments measure performance of core primary healthcare attributes. The attribute-specific results are presented in individual papers elsewhere in this special issue.

The strength of this study was our analysis across instruments, which allowed us to identify sub-dimensions within an attribute. Sometimes a sub-dimension is unique to one subscale; sometimes, more than one is represented. This approach will help program evaluators select the measures appropriate for their needs. Another consideration will be the missing values, and evaluators may choose not to offer “not sure” or “not applicable” options to minimize information loss. As with any study, results are sample-dependent, and items that do not function well in the present sample may still function well in a different sample of individuals or a different healthcare setting. However, the results of our study show that most of these measures can be used with confidence in the Canadian context. Ideally, any difficulties identified should be viewed as opportunities for improvement, potentially by rewriting, rewording or clarifying questions.

Acknowledgements

This research was funded by the Canadian Institutes of Health Research (CIHR).

Contributor Information

Darcy A. Santor, School of Psychology, University of Ottawa, Ottawa, ON.

Jeannie L. Haggerty, Department of Family Medicine, McGill University, Montreal, QC.

Jean-Frédéric Lévesque, Centre de recherche du Centre hospitalier de l'Université de Montréal, Montréal, QC.

Frederick Burge, Department of Family Medicine, Dalhousie University, Halifax, NS.

Marie-Dominique Beaulieu, Chaire Dr Sadok Besrour en médecine familiale, Centre de recherche du Centre hospitalier de l'Université de Montréal, Montréal, QC.

David Gass, Department of Family Medicine, Dalhousie University, Halifax, NS.

Raynald Pineault, Centre de recherche du Centre hospitalier de l'Université de Montréal, Montréal, QC.

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Introduction to Confirmatory Factor Analysis and Structural Equation Modeling

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• Matthew W. Gallagher &
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Confirmatory factor analysis (CFA) is a powerful and flexible statistical technique that has become an increasingly popular tool in all areas of psychology including educational research. CFA focuses on modeling the relationship between manifest (i.e., observed) indicators and underlying latent variables (factors).

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Gallagher, M.W., Brown, T.A. (2013). Introduction to Confirmatory Factor Analysis and Structural Equation Modeling. In: Teo, T. (eds) Handbook of Quantitative Methods for Educational Research. SensePublishers, Rotterdam. https://doi.org/10.1007/978-94-6209-404-8_14

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ORIGINAL RESEARCH article

Exploratory and confirmatory factor analysis of the 9-item utrecht work engagement scale in a multi-occupational female sample: a cross-sectional study.

• 1 Department of Health and Caring Sciences, Faculty of Health and Occupational Studies, University of Gävle, Gävle, Sweden
• 2 Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden

Objective: The aim of the present study was to use exploratory and confirmatory factor analysis (CFA) to investigate the factorial structure of the 9-item Utrecht work engagement scale (UWES-9) in a multi-occupational female sample.

Methods: A total of 702 women, originally recruited as a general population of 7–15-year-old girls in 1995 for a longitudinal study, completed the UWES-9. Exploratory factor analysis (EFA) was performed on half the sample, and CFA on the other half.

Results: Exploratory factor analysis showed that a one-factor structure best fit the data. CFA with three different models (one-factor, two-factor, and three-factor) was then conducted. Goodness-of-fit statistics showed poor fit for all three models, with RMSEA never going lower than 0.166.

Conclusion: Despite indication from exploratory factor analysis (EFA) that a one-factor structure seemed to fit the data, we were unable to find good model fit for a one-, two-, or three-factor model using CFA. As previous studies have also failed to reach conclusive results on the optimal factor structure for the UWES-9, further research is needed in order to disentangle the possible effects of gender, nationality and occupation on work engagement.

Introduction

Work engagement has been described as the conceptual opposite of burnout ( González-Romá et al., 2006 ), and as such belongs in the area of positive psychology, or “the study of the conditions and processes that contribute to the flourishing or optimal functioning of people, groups, and institutions”( Gable and Haidt, 2005 ). In occupational health, the study of work engagement focuses on factors that contribute to job satisfaction as well as long-term mental and physical health ( Torp et al., 2013 ).

Work engagement has been described as “a positive work-related state of mind characterized by vigor, dedication and absorption.” ( Schaufeli et al., 2002 ). These three concepts are in their turn described as “characterized by high levels of energy and mental resilience while working, the willingness to invest effort in one’s work, and persistence even in the face of difficulties” (Vigor), “characterized by a sense of significance, enthusiasm, inspiration, pride and challenge” (Dedication) and “characterized by being fully engrossed in one’s work, so that time passes quickly and one has difficulties in detaching oneself from work” (Absorption) ( Schaufeli et al., 2002 ).

The idea that these three concepts – Vigor, Dedication and Absorption – together form the foundation of work engagement forms the basis of the Utrecht work engagement scale (UWES) ( Schaufeli et al., 2002 ). Originally a 17-item questionnaire (UWES-17), the original authors have shortened it to a 9-item version (UWES-9) in order to reduce the burden on the respondents and minimize attrition ( Schaufeli et al., 2006 ). The items are in the form of statements (for example “At my work, I feel bursting with energy” (Vigor); “I find the work that I do full of meaning and purpose” (Dedication); “When I am working, I forget everything else around me” (Absorption) which the respondent reads and reacts to by indicating one of 7 points on a scale ranging from 0 (“Never”) to 6 (“All the time”). The 9-item version, which has been psychometrically tested in various countries and samples ( Ho Kim et al., 2017 ; Petrović et al., 2017 ), will be the focus of the present study.

In a number of studies, conducted in different countries and with samples of various make-ups, UWES-9 scores have been found to be associated with work performance, job satisfaction, and mental and physical health ( Bakker and Matthijs Bal, 2010 ; Christian et al., 2011 ). The scores have also been found to predict general life satisfaction and the frequency of sickness absence ( Leijten et al., 2015 ).

Despite its wide-spread use, both the UWES-17 and the UWES-9 have been the subject of some criticism. Mills et al. (2012) have argued that the methodology when developing the original scale contained flaws in relation to the establishment of its factorial structure. Criticism has also been voiced regarding the factor structure of the instrument, one of the main points being that the three subscales Vigor, Dedication and Absorption are very closely correlated with each other, casting doubt on the three-factor structure’s superiority to a one-factor structure using only the total score on the scale ( Kulikowski, 2017 ). For example, Shirom has argued that the three dimensions of Vigor, Dedication, and Absorption were not theoretically deduced and that they overlap each other conceptually ( Shirom, 2003 ). In support of this, several studies have failed to confirm the three-factor structure in their samples. Previous studies have also tested other factor structures – for example, Kulikowski (2019) tested a two-factor structure, with Dedication and Vigor merged into a single factor and Absorption constituting a second factor ( Kulikowski, 2019 ). A 2017 review by Kulikowski investigated the factorial structure of the UWES-17 and UWES-9 as reported in 21 different studies, conducted in 24 countries using samples from a variety of occupations and countries. The author found that of the 11 studies investigating the UWES-9, three confirmed the one-factor structure, three the three-factor structure, four studies found these two factor structures to be equivalent, and one study failed to support either alternative ( Kulikowski, 2017 ). Thus, Kulikowski (2017) concluded that no definitive recommendations could be made based on the review. He also pointed out the importance, in light of these inconclusive results, that further research be conducted on the factorial structure of the UWES-9 in different samples ( Kulikowski, 2017 ).

Only one previous study has tested the factorial validity of the UWES-9 in a Swedish sample ( Hallberg and Schaufeli, 2006 ). In their sample of 186 information communication technology consultants (of whom 37% were women), both the one-factor and three-factor structures were supported by data, leading the authors to draw the conclusion that both options were equally strong. If the scope is broadened to take in all the Scandinavian countries, a Norwegian study using a large multi-occupational sample ( n = 1266, 67% women) found support for the three-factor structure, but also found that the three latent factors were strongly correlated, leading the authors to suggest that a one-factor structure might also be suitable (Nerstad, Richardsen and Martinussen, 2010). In addition to this, a Finnish study found, in a sample of 9404 workers in several different occupational sectors, that both the one-factor and three-factor structures may reasonably be used ( Seppälä et al., 2009 ). Similarly to the Norwegian study, the results showed that the three subscales of Vigor, Dedication, and Absorption were highly correlated.

Interestingly, it has been suggested that as a rule, levels of work engagement tend to be higher in countries in Northwestern Europe, and lower in Southern Europe, on the Balkans and in Turkey ( Schaufeli, 2018 ). However, Sweden is identified as an exception to this rule, with relatively low levels of work engagement compared to, for example, Norway, where levels were found to be higher ( Schaufeli, 2018 ).

The 9-item UWES is a widely used instrument to measure work engagement. Despite this, the optimal factorial structure of the UWES-9 remains unknown. A recent review of factorial structure for the UWES-9 and UWES-17 failed to reach conclusive results, and indicated that more research was needed to determine the appropriate default factorial structure ( Kulikowski, 2017 ). Many previous studies have used relatively small samples, and many have reached inconclusive results, including the only previously published Swedish study. In order to adequately assess and potentially target work engagement in future interventions using Swedish populations, it is important to examine and ascertain whether Swedish people hold the same representation of work engagement. Thus, the aim of the present study was to use exploratory and confirmatory factor analysis (CFA) to investigate the factorial structure of the 9-item UWES in a multi-occupational Swedish sample.

Materials and Methods

Participants.

The women in the all-female sample used for the current study were originally recruited in 1995, when they were aged between 7 and 15 years, through stratified randomization from a number of school classes in Sweden. They were sampled to represent a general population of girls, and were participants in a longitudinal study aiming to identify risk and protective factors for the development of eating disorders. More details about the recruitment and follow-up can be found elsewhere ( Westerberg-Jacobson et al., 2010 ). The data used in the current study was collected in 2015, as part of the 20-year follow-up data collection. The participants remaining in the study were asked to complete a number of questionnaires, including the UWES-9, and those who indicated that they were currently working full-time or part-time (not on long-term sick-leave, parental leave, unemployed, or studying full-time) were included in the current study. Thus, the final sample consisted of 702 women, aged between 26 and 37, who completed a Swedish translation of the 9-item UWES ( Schaufeli et al., 2006 ). Aside from the UWES-9, data was collected on level of education (primary school, secondary education or university education), although not on specific occupation.

Ethics Statement

The project was approved by the Regional Ethics Board in Uppsala, Sweden (2014/401). At the time of the original recruitment, in 1995, the participants and their parents gave written informed consent to take part in the study. At the time of the data collection for the present study, the participants again gave their written informed consent and were reminded that their participation was voluntary, could be withdrawn any time without giving a reason, and that all information would be treated confidentially. All participants who completed the data collection were offered a cinema ticket or a department store gift voucher as thanks.

Statistical Analysis

All analyses were performed using Stata 14 ( StataCorp, 2015 ) and SPSS ( IBM Corp, 2016 ) statistical software packages. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy and Bartlett’s Test of Sphericity were used to assess the suitability of the data for factor analysis ( Dziuban and Shirkey, 1974 ). Exploratory factor analysis (EFA) was first performed unrotated, using maximum likelihood extraction and eigenvalues > 1. Additionally, we performed EFA with promax rotation and enforcing three-factor solution in order to test the theoretical structure of the UWES-9. In this analysis, we also used maximum likelihood extraction. Additionally, Parallel Analysis (using principal axis factoring) and Velicer’s Minimum Average Partial test were conducted ( O’Connor, 2000 ).

CFA was then performed using maximum likelihood estimation.

In order to investigate the models’ goodness of fit, a number of statistics were used: Overall χ 2 ( Hooper et al., 2008 ), root mean square error of approximation (RMSEA) ( Steiger, 1990 ; Hooper et al., 2008 ), Akaike’s information criterion (AIC), Bayesian information criterion (BIC), comparative fit index (CFI), Tucker-lewis index (TLI) ( Bentler, 1990 ), and the standardized root mean square residual (SRMSR) ( Hooper et al., 2008 ).

Demographic information about the participants can be seen in Table 1 . Data on highest attained educational level was collected, and showed that the majority of the sample had attended at least 3 years of higher education.

Table 1. Demographic information about the participants.

The inter-item correlation was relatively high for all items of the UWES-9, ranging between 0.524 and 0.849. The three subscales Vigor (V), Dedication (D), and Absorption (A) also showed high correlation with each other (0.79–0.84). In addition to this, Cronbach’s alpha was calculated and found to be 0.947, indicating very good internal consistency.

The items were checked for skewness and kurtosis and these are shown in Table 2 , together with the wording of the items, their respective subscales, mean scores and standard deviations. Based on the Shapiro-Wilks test and a visual inspection of their histograms, normal Q-Q plots and box-plots, we concluded that the UWES item distributions had a skewness range between −0.560 and −1.262 (SE = 0.094) and a kurtosis range between −0.046 and 1.645 (SE = 0.187) ( Table 2 ). The values for skewness and kurtosis were deemed to be within the range for maximum likelihood estimation. We also tested the multivariate normality using Doornik-Hansen test, the Mardia skewness test and Mardia kurtosis test. For all of these, the p -value was <0.0001, indicating non-normality.

Table 2. Items with their subscales, mean scores, standard deviations, skewness, and kurtosis.

In the next step, the sample was randomly divided in two, so that mutually independent samples were obtained for the EFA and CFA, respectively. As the number of participants with missing values was very low (19 individuals, corresponding to 3% of the entire sample), only observations without any missing items were used, resulting in 683 observations in total, 341 for the EFA and 342 for the CFA.

Exploratory Factor Analysis

The results of the EFA suggested that one factor explained over 70% of the variance. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy was 0.922, indicating that the sample was adequate, and Bartlett’s Test of Sphericity gave a p -value of <0.001. A Scree plot of the eigenvalues was constructed (not shown) and shown to be strongly in favor of the one-factor structure. The χ2 for this model was 332,43 (df 27).

Velicer’s MAP test was also performed, both in the original ( Velicer, 1976 ) and revised version ( O’Connor, 2000 ). This also strongly pointed toward a one-factor solution.

Finally, in the Parallel Analysis, the raw data eigenvalue from the actual data was greater than eigenvalues of the 95th percentile of the distribution of random data for four factors, in disagreement with the MAP test and the EFA ( O’Connor, 2000 ).

Table 3 shows the factor loadings. As the table shows, all loadings were relatively high, ranging from 0.65 to 0.93.

Table 3. Factor loadings.

In addition to this, we also conducted EFA using promax rotation and enforcing a three-factor structure, in order to compare the fit of the theoretical dimensionality of the UWES-9 with the one-factor solution we found in our sample. The χ2 for this model was 45,72 (df 12) ( p < 0.001). The items did not load on their expected factors “Dedication” had 4 items (3, 4, 5, 6), “Vigor” had 2 items (1, 2), and “Absorption” had 3 items (7, 8, 9).

Confirmatory Factor Analysis

As the EFA suggested a one-factor solution, as described above, the model was first specified with just one latent factor (Work Engagement). Standardized coefficients were used and the estimation model was maximum likelihood, since the items showed acceptable skewness and kurtosis ( Table 2 ). Observations with missing values were excluded.

In order to also test the theoretical foundation of the UWES-9, we performed CFA with the original three subscales Vigor, Dedication and Absorption. Additionally, inspired by a previous study by Kulikowski (2019) , who also tested a two-factor model, we also performed CFA using this structure.

Figures 1 – 3 show all the attempted models.

Figure 1. One-factor structure with maximum likelihood estimation.

Figure 2. Two-factor structure with maximum likelihood estimation.

Figure 3. Three-factor structure with maximum likelihood estimation.

Table 4 shows the coefficients of the hypothesized relationships, together with their z -values, standard errors, 95% confidence intervals and p -values, for all tested models.

Table 4. All models’ standardized coefficients and associated data.

After estimating the models, goodness-of-fit statistics were obtained, as described in the section “Materials and Methods,” above. As can be seen in Table 5 , none of the models showed very good fit, with RMSEA ranging between 0.181 and 0.167. Also, CFI and TLI, which should preferably be above 0.95 ( Hooper et al., 2008 ) remained below this value for all tested models.

Table 5. Goodness-of-fit statistics for all models.

The aim of the present study was to use exploratory and CFA to investigate the factorial structure of the UWES in a multi-occupational sample of Swedish women. The EFA seemed to mainly favor a one-factor solution, which was shown to explain over 70% of the variance.

Confirmatory factor analysis was then performed using three different models: one-factor, two-factor, and three-factor. Goodness-of-fit statistics were obtained for all models and showed that none of them showed overall good fit, with RMSEA never going below 0.167 and CFI and TLI remaining relatively low ( Table 5 ).

As previously mentioned, a recent review of the factorial structure of the UWES showed inconclusive results, with some included studies showing best fit for a one-factor structure, some showing best fit for a three-factor structure, and some showing an equally good (or poor) fit for both ( Kulikowski, 2017 ). This indicates a need for further research into the underlying factors impacting the factor structures in various samples.

One of the studies included in the Kulikowski review found that neither the one-factor nor the three-factor structure of the UWES-9 was a good fit for their data ( Wefald et al., 2012 ). This used a sample similar to ours, both in terms of size (382 vs. 342) and level of education (in both samples, around 60% had a university degree or higher). The RMSEA was 0.18 and 0.16 for the one-factor and three-factor structures, in the Wefald study, almost identical to 0.181 and 0.167 for our study.

A previous study by Kulikowski (2019) has also attempted a two-factor structure, merging Dedication and Vigor into a single factor, letting Absorption constitute the second factor ( Kulikowski, 2019 ). We attempted the same model in the present study, but in agreement with Kulikowski’s results, failed to obtain satisfactory goodness of fit.

The only previous Swedish study using the UWES used a sample consisting of 186 information technology (IT) consultants (37% women) and found that both the one-factor and three-factor structure showed similar fit, with RMSEA of 0.13 and CFI of 0.97 for both ( Hallberg and Schaufeli, 2006 ). Although this sample was Swedish, it was different from that of the present study in other significant ways, such as gender (a majority were male) and occupation (all the participants were IT consultants, whilst ours was a multi-occupational sample), which may explain the differences in the results.

If our results are compared with those of other studies also using multi-occupational samples, several of them have, in agreement the Swedish study by Hallberg and Schaufeli (2006) , found that both the one-factor and three-factor structures may be used. For example, this was the case for Schaufeli et al. (2006) with a very large multinational sample of 14521 individuals.

These differing results support the recommendation made by Kulikowski (2017) , namely that each study using the UWES-9 should undertake their own factor analysis based on their own sample, and make a decision on which structure to use based on their own results ( Kulikowski, 2017 ). In addition to this, and in agreement with the current study, several previous studies have found that none of the factor structures tested have shown an acceptable fit ( Hallberg and Schaufeli, 2006 ; Wefald et al., 2012 ). Subsequently, researchers looking to use a measure of work engagement may wish to use another instrument in parallel with the UWES.

The present study has strengths, as well as weaknesses. The relatively large sample size of approximately 700 women made it possible to randomly divide the group into half so that both an exploratory and a CFA could be undertaken. The fact that the sample consisted exclusively of women may be seen both as a strength and as a weakness. On the one hand, it ensures that the results are not skewed by an uneven gender balance, but on the other hand our results should not be assumed to be generalizable to males. An Iranian study investigating determinants of work engagement in hospital staff found no significant effect of gender ( Mahboubi et al., 2014 ). However, a Dutch study exploring work engagement and burnout in veterinarians found that women rated their work engagement lower than men, indicating that gender differences may vary with different occupational groups, nationalities, or other, hitherto unknown factors ( Mastenbroek et al., 2014 ).

In addition to this, in terms of generalizability, it should be acknowledged that the sample used in the present study should be considered to represent the white-collar population, based on the higher-than-average level of education. More than 60% of the participants reported having at least 3 years of university education, whilst the national average for women between the ages of 25 and 34 is 35%, according to Statistics Sweden ( Statistics Sweden, 2017 ). In addition to this, only Swedish-speaking girls participated. However, 21.6% had immigrated or had parents who had immigrated to Sweden, which is in line with the population in general ( Statistics Sweden, 2018 ).

The present study used a large, multi-occupational female sample to explore the factorial structure of the UWES-9. Despite indication from EFA that a one-factor structure best fit the data, we were unable to find good model fit for a one-, two-, or three-factor model using CFA. As previous studies have also failed to reach conclusive results on the optimal factor structure for the UWES-9, further research is needed in order to disentangle the possible effects of gender, nationality and occupation on work engagement. Until such data exists, researchers would be wise to conduct their own factor analysis in order to determine whether the total score, the three dimensions representing Vigor, Dedication and Absorption, or even a two-factor structure is applicable for their sample.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

This project was approved by the Regional Ethics Board (2014/401). At the time of the data collection for the present study, the participants were again asked to give their consent and reminded that their participation was voluntary, could be withdrawn any time without giving a reason, and that all information would be treated confidentially. All participants who completed the data collection were offered a cinema ticket or a department store gift voucher as thanks.

Author Contributions

MW contributed to the conception and design of the work, performed the analyses, and drafted the manuscript. JW and ML contributed to the conception and design of the work, took part in the data collection and analyses, and revised the work critically. All authors approved the final version to be published, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

This work was supported by Capio Research Foundation, the Signe and Olof Wallenius Foundation, and the Thuring Foundation.

Conflict of Interest

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.

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Keywords : confirmatory factor analysis, exploratory factor analysis, Utrecht work engagement scale, work engagement, occupational psychology

Citation: Willmer M, Westerberg Jacobson J and Lindberg M (2019) Exploratory and Confirmatory Factor Analysis of the 9-Item Utrecht Work Engagement Scale in a Multi-Occupational Female Sample: A Cross-Sectional Study. Front. Psychol. 10:2771. doi: 10.3389/fpsyg.2019.02771

Received: 10 April 2019; Accepted: 25 November 2019; Published: 06 December 2019.

Reviewed by:

Copyright © 2019 Willmer, Westerberg Jacobson and Lindberg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Mikaela Willmer, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Confirmatory factor analysis using AMOS: a demonstration

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• Published: 22 July 2019

Structural equation modeling and confirmatory factor analysis of social media use and education

• Shugufta Abrahim 1 ,
• Bilal Ahmed Mir 1 ,
• Hayato Suhara 1 ,
• Fatin Amirah Mohamed 1 &
• Masahiro Sato 1  

International Journal of Educational Technology in Higher Education volume  16 , Article number:  32 ( 2019 ) Cite this article

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The objective of this study is to design a structural equation model and test confirmatory factor analysis system in order to better explain how students could utilize social networking system (Facebook) for educational purposes. Thus, this paper seeks to examine the attitude, perception and behaviour of Japanese students’ towards social-networking sites, and how students from non-English speaking backgrounds (especially Japanese students) at the University of Toyama perceive the use of Facebook for learning English as a foreign language. Our Structural Equation Modelling system based Facebook model outline the relations among different types of independent, dependent variables and constructs. We tested our model using adequate fitting indices like Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Non-Normed Fit Index/Tucker Lewis index (NNFI/TLI) and Incremental Fit Index (IFI). The results of the proposed model confirmed the hypothesized latent structures and theoretical validity of probed factors. Conclusions drawn from this study might be useful to better understand the use of social networking tools in educational context.

Introduction

The digitization of identity and global networked system, with the ubiquitous computing applications, particularly high quality electronic data creation and maintenance, reveals that common networking concepts are widely found across a range of domains (Hansen, Shneiderman, & Smith, 2010 ; Pentland, 2016 ). Online Social Network (OSN) is a contemporary type of internet phenomenon where everyone and everything is connected. In today’s society, social media is a term that everyone knows and all households with youngsters having access to the internet are familiar with multiple social media platforms exist currently. Recent years have witnessed a phenomenal proliferation and widespread use of social media platforms among a large population (Amichai-Hamburger & Vinitzky, 2010 ; Chae, 2018 ; Choi & Sung, 2018 ; Rodriguez & Keane, 2018 ). It is estimated that more than 3 billion people around the world (around 40% of the world’s population) use online social media.

( http://www.bbc.com/future/story/20180104-is-social-media-bad-for-you-the-evidence-and-the-unknowns )

( https://wearesocial.com/blog/2018/01/global-digital-report-2018 ).

Social media platforms offer wealth of unique opportunities to many individuals from all walks of the life in many different ways. These sites are used not only to make friends, send or receive instant messages, but also offer social and participatory virtual platforms that facilitate the users to build and expand social and professional networks with different stakeholders within and beyond geographic territories using a variety of modalities (Greenhow, Robelia, & Hughes, 2009 ; Thompson, 2013 ; Valkenburg, Koutamanis, & Vossen, 2017 ).

The overall explosion of social media platforms where multitude of users from around the globe congregate has fundamentally caused a shift in the society on how people communicate and share knowledge with each other, how information flow and diffuses, how people organize social events and make relationships, how businesses compete and operate, how celebrities promote themselves and attract their followers, and how politicians exert considerable influence on voters and contest elections (Fogel & Nehmad, 2009 ; Hansen et al., 2010 ; Junco, 2015 ; Shen, Brdiczka, & Liu, 2015 ). The infiltration and power of new age social networking technology into society is undoubtedly reshaping almost every aspect of life in an unprecedented way, and education sector is no exception (Amichai-Hamburger & Vinitzky, 2010 ; Correa, Willard Hinsley, & Gil se Zuniga, 2010 ). The unique capabilities and a variety of advantageous features of social networking channels have brought to student community, besides online social entertainment, the utilization of social media platforms for academic and professional activities in a more appealing and modern format (Junco, 2015 ). The use of social media provide important personal data information regarding social and emotional background, cognitive functioning and psychosocial development scenarios of the youths who spend most of their waking hours staring at the internet-connected systems and devices worldwide (Hayes, van Stolk-Cooke, & Muench, 2015 ; Pempek, Yevdokiya, & Calvert, 2009 ). Because of exponential growth of huge volumes of user generated online content and the subsequent potentiality to interact, communicate, collaborate, cooperate and share meaningful data in real-time with others conveniently, the social networking platforms are considered to be of profound significance for educational purposes and social media analytics (Hansen et al., 2010 ; Kuzma & Wright, 2013 ; Mazman & Usluel, 2010 ; Oh, Ozkaya, & LaRose, 2014 ; Pempek et al., 2009 ; Pentland, 2016 ; Sanchez, Cortijo, & Javed, 2014 ).

Therefore, there has been an increasing interest in the use of social media networks and transmedia skills for educational and learning benefits (Donlan, 2014 ; Forkosh-Baruch & Hershkovitz, 2012 ; Kuzma & Wright, 2013 ; Mazman & Usluel, 2010 ; Roblyer, McDaniel, Webb, Herman, & Witty, 2010 ; Sanchez et al., 2014 ; Scolari, 2018 ; Scolari, Masanet, Guerrero-Pico, & Establés, 2018 ; Sharma, Joshi, & Sharma, 2016 ). Although, social networks have become an integral part of modern society, but it has only recently been explored as a potential tool for educational purposes. A number of recent research studies have shown that social media platforms support educational activities directly or indirectly by making effective interaction and communication, educational information and resource sharing, active participation and cooperative learning (Ajjan & Hartshorne, 2008 ; Anderson, Hepworth, Kelly, & Metcalfe, 2010 ; Ertmer et al., 2011 ; Jones, 2015 ; Larusson & Alterman, 2009 ; Mason, 2006 ; Veletsianos & Navarrete, 2012 ).

Today, there is a tremendous variety of social networking platforms available on the Internet with various technological features, supporting a wide spectrum of interests and practices. Social media platforms such as Facebook, Instagram, Twitter or LinkedIn have an increasing user base (Chawinga & Zinn, 2016 ; Cheung, Chiu, & Lee, 2011 ; Dzvapatsva, Mitrovic, & Dietrich, 2014 ; Gikas & Grant, 2013 ). Among Social networking technologies, Facebook remains one of the largest and most popular (especially among student population) social networking sites in existence with over 1.47 billion daily active users and 2.23 billion monthly active users as of 2018, and 98% of university or college students are found to have a Facebook profile (Hargittai, 2007 ; Inma & Antoni, 2018 ; Lee, 2011 ; Madge, Meek, Wellens, & Hooley, 2009 ; Moretta & Buodo, 2019 ; Wandeep, Vimala, Omer, & Ajantha, 2019 ; Zhao, Grasmuck, & Martin, 2008 ). The nation with the highest number of Facebook users as of 2018, is India with 270 million users, followed by United States with 207.36 million users. The next is Indonesia with 140 million users, followed by Brazil with 130 million registered users.

http://www.businessofapps.com/data/facebook-statistics/#1

Facebook usage and engagement is on the rise worldwide. Majority of Facebook users (especially youth) not only frequently log onto this site, but also spend a considerable portion of their daily life on surfing Facebook. Researchers have carried out several studies to investigate the possibility of utilizing Facebook’s network for educational uses (Erdem & Kibar, 2014 ; Meishar-Tal, Kurtz, & Pieterse, 2012 ; Qureshi, Raza, & Whitty, 2015 ; Schroeder & Greenbowe, 2009 ). While recognizing the importance of the advanced features and capabilities of Facebook for pedagogical purposes, we feel that it is also important to use modeling approach in order to conceptualize the use of Facebook for educational purposes and test hypothetical model for deep social media analysis.

The focus of this research is to examine Japanese students’ behaviours towards social networking technology, and how students from non-English speaking backgrounds perceive the use of Facebook for learning English as a foreign language. Although several research studies on social networking exists explaining the motivation underlying users’ preferences using social networking system in general and Facebook in particular, but to the best of our knowledge no report has been published using structural equation model for demonstrating the diffusion, benefit and acceptance of Facebook by students from non-English to English as second language learning context.

Facebook literature review

Facebook is the most predominant and widely visited social media landscape across the globe. Facebook users include individuals from different age groups, education levels, religions, languages, and cultural backgrounds .

( https://newsroom.fb.com/company-info/ ).

Communicating through Facebook phenomenon has drastically changed the way people maintain their social status and extend social connections with the world around them. Today, Facebook communication platform is very popular among the adolescents and young adults because of several crucial aspects including its user friendly nature, better interface, top notch security of accounts, and it provides instant communication and immediate feedback opportunities (Erdem & Kibar, 2014 ; Inma & Antoni, 2018 ; Moretta & Buodo, 2019 ). In addition, Facebook allow its users to utilize several interesting features in order to perform numerous activities and tasks such as creating personal profile for presenting themselves to others, nurturing existing relationships (friends, family members, coworkers, and acquaintances), uploading pictures and videos, posting status updates, expressing personal opinions and views in writing or through live streaming, establishing new relationships, creating Facebook pages/groups representing public figures, institutions, organizations, businesses, brands or products and other entities (Cheung et al., 2011 ; Junco, 2015 ; Thompson & Lougheed, 2012 ; Tsay-Vogel, Shanahan, & Signorielli, 2016 ; Valkenburg et al., 2017 ). The purpose of Facebook adoption and usage differs greatly depending on users’ interests, priorities, affiliations, attitudes and perceptions (Brailovskaia & Bierhoff, 2016 ; Hayes et al., 2015 ; Junco, 2015 ; Kuzma & Wright, 2013 ; Toker & Baturay, 2019 ; Wandeep et al., 2019 ).

Given the widespread acceptance and growing popularity of Facebook based social media among the young population, it is necessary to understand how Facebook usage influences on users’ social routine, emotional connection, social integration and educational activities. A plethora of research studies have been conducted on Facebook from different facets. Several studies have demonstrated the influence of prolonged use of Facebook on the psychological characteristics (attitudes and behaviors) of users (Amichai-Hamburger & Vinitzky, 2010 ; Correa et al., 2010 ; Dickie & Meier, 2015 ; Hayes et al., 2015 ; Lönnqvist & große Deters, 2016 ; Ross et al., 2009 ; Ryan & Xenos, 2011 ; Sagioglou & Greitemeyer, 2014 ; Shen et al., 2015 ; Wilson, Fornasier, & White, 2010 ). Some other existing studies pinpoint the effect of Facebook media interaction between users (Brailovskaia & Bierhoff, 2016 ; Mendes, Furtado, Militao, & de Castro, 2015 ), and several other studies have been carried out to investigate the privacy, trust and security concerns on Facebook (Junior, Xavier, & Prates, 2014 ; Külcü & Henkoğlu, 2014 ; Saeri, Ogilvie, La Macchia, Smith, & Louis, 2014 ; Tsay-Vogel et al., 2016 ).

Until now, researchers and social media specialists virtually do not have reached a unanimous agreement on whether use of Facebook elicit positive effects, negative effects or both on the life style and well-being of its users, especially youth, perhaps because Facebook usage promote both positive and negative feelings. The overall outcome depends on numerous factors and parameters, which are directly or indirectly related to attitudinal, behavioral and innate characteristics of users or series of Facebook specific features, among others (Debatin, Lovejoy, Horn, & Hughes, 2009 ; Fogel & Nehmad, 2009 ; Pempek et al., 2009 ; Urista, Dong, & Day, 2009 ). There seems to be a consensus among networking technology researchers on the idea that Facebook user experience and boost adequate positive emotions (Kross et al., 2013 ; Oh et al., 2014 ).

Even though, Facebook platform offer tremendous educational potentials, Facebook has been extensively utilized for social activities (interaction with each other based on shared interests, backgrounds or characteristics) rather than using it as a substitute for educational purposes. However, the growing popularity of Facebook based social networking among college/university students across the globe has led social media communication researchers and educators to investigate and identify several determinants that may motivate and attract the students to embrace Facebook in educational contexts or to highlight its potential influences on students’ non-academic and academic outcomes (Dabner, 2012 ; Toker & Baturay, 2019 ).

Recently, there has been a tremendous increase in the number of published papers emphasizing the potential benefits of implementing Facebook in the educational and learning domain. The rapidly growing body of literature suggest that today’s student community perceive Facebook as a suitable environment for educational sectors and the reported works have yielded mixed results. For example Ellison, Steinfield, and Lampe ( 2007 ), reported that Facebook play an important role in the initiation of constructive relationships among college students and progression or maintenance of social capital. According to Madge et al. ( 2009 ), Facebook can be used for informal learning because it acts as a social glue that keeps students connected with people who they do not meet or see often due to distance or other reasons, provide them both the opportunities and capabilities to get to know other students and potentially support them transition into college or university life. Similarly, Prescott, Wilson, and Becket ( 2013 ) demonstrated that university students use Facebook for informal education especially for peer to peer support purposes. Thompson and Lougheed ( 2012 ) found 80.24% of university students believe that Facebook has a significant and positive impact on campus engagement for cross-cultural networking activities and interactions. Another study carried out by Junco ( 2015 ), explained the role of Facebook use and multitasking by comparing academic success of different levels of American undergraduate students based on their cumulative grade point average scores (GPA). Studies have also indicated that the familiarity of educators with Facebook and its main features open possibilities to create virtual study group for educational activities (Charlton, Magoulas, & Laurillard, 2012 ). Teachers and students connection via Facebook system can be potentially useful as an available means of communication between teachers and students. According to Mazer, Murphy, and Simonds ( 2007 ), professors who use Facebook profiles often post questions on the wall of their Facebook pages and participate in online discussions with students.

Researchers such as Bowers-Campbell ( 2008 ) described that educators can utilize Facebook to discuss students concerns and problems related to their assignments, examinations and other educational issues of shy or quiet students. Nonetheless, various studies indicated that the benefit of using Facebook based platform for teaching and learning activities extend beyond the social and cultural aspects (Erdem & Kibar, 2014 ; Toker & Baturay, 2019 ; Towner & Munoz, 2011 ). This view was further advocated by the results obtained by Towner and Munoz ( 2011 ) who observed that about half of the participants in that study responded that they use their Facebook educational groups not only for communicating and discussing social events, but also for formal learning purposes including sharing study material, communicating with concerned teachers about courses, exams and homework assignments. Similar results were published by Mao ( 2014 ) using an explanatory sequential mixed methods design approach for high school students. Yunus and Salehi ( 2012 ) investigated the perception of university students towards Facebook groups for improving their writing skills. Hamid, Waycott, Kurnia, and Chang ( 2015 ) found similar results for student–student and student–lecturer interactions among Malaysian and Australian universities participants. Several other studies reported the similar results for American students (Gikas & Grant, 2013 ), for Serbian students (Milosevic, Zivkovic, Arsic, & Manasijevic, 2015 ) and for Egyptian students (Sobaih, Moustafa, Ghandforoush, & Khan, 2016 ), respectively. The overall social media research suggests that the encouragement, motivation and satisfaction of students to utilize Facebook for purposes other than just social and networking activities (both formal and informal learning), has increased over the past few years (Aydin, 2014 ; Erdem & Kibar, 2014 ).

It is well established that the academic performance and dedication of students for the effective learning outcome is largely dependent on the degree of students’ engagement in appropriate range of educational activities (attending lectures and performing tasks) in academic settings. It is very important and highly essential to adopt student-centered innovative teaching and learning methodologies or to provide new technological solutions that facilitate active learning environments for enhancing students’ attention, focus, engagement, and active learning in educational domains. Social networking systems, (especially Facebook), offer multiple student centric features that are more alluring than conventional learning environments. Since student community explore Facebook in school, college and university campuses on their personal smartphones and other personal electronic gadgets, it is much easily accessible to them. Using the numerous advantageous features of Facebook educators can share many educational resources by simply posting different contents, assignments, learning modules, lesson plans, contemporary tests, training experiments and surveys on the virtual study group pages that students require to process as a task in the lectures or interactive teaching/learning tasks that can be of great help to the conventional way of teaching and learning strategies (Kirschner & Karpinski, 2010 ; Thompson, 2013 ). Although, supportive student-teacher relationships are important to boost achievement but teacher should undeniably remain highly cautious in using or posting content on a Facebook outlet, because poor communication can damage or ruin their reputation, credibility and integrity (Mazer et al., 2007 ).

From an educational perspective, Facebook offer new avenues for the future research and development in the areas of social media and educational technology. For example, the use of Facebook can be initiated by the institutions/universities to probe the suitability of this social networking site as teaching/learning tool and to identify its educational advantages that facilitate relatively a large population of young people to improve significantly the student’s engagement in the learning process. Considering previous literature findings, we propose a structural model equation model and test confirmatory factor analysis system to determine the student’s perceptions, attitudes, behaviours and experiences towards Facebook educational usage.

Research model and hypothesis

Throughout history, researchers have tried to understand and predict the concept of technological innovation paradigms. Many theories have been developed over time that provide with explanations of emergence of new technological systems in terms of diffusion, acceptance and benefit and also pointed out why some users become addicted to use certain technologies or become dependent upon them (Ajjan & Hartshorne, 2008 ; Ajzen, 1991 ; Carlile, Jordan, & Stack, 2004 ; Davis, 1989 ; Fishbein & Ajzen, 1975 ; Rogers, 2003 ; Venkatesh & Davis, 2000 ; Venkatesh, Morris, Davis, & Davis, 2003 ). Dozens of models have been constructed based on these theories, which is probably the easiest way of defining and analyzing the problems or situations so that the underlying issues can be more easily understood and addressed (Corrocher, 2011 ; Lai & Chen, 2011 ; Mazman & Usluel, 2010 ; Moore & Benbasat, 1991 ; Sanchez et al., 2014 ). In this study, we constructed a structural model by considering the technological as well as social aspects of social networking system to examine the student’s educational activities. Our Facebook model exclusively consist of three latent variable indicators (benefit of Facebook, purpose of Facebook and social media usage of Facebook) and 14 observable variables (Fig.  1 ). Benefit as a latent variable was described by five observed variables; perceived usefulness, perceived ease of use, social influence, collaborative platform and perceived enjoyment. The items designed for the Facebook usage and to asses benefit scale generated after a comprehensive literature survey on existing benefit, diffusion, acceptance and usage models and theories. Hence, related or similar themes were classified under some tentative categories (Fig. 1 a-c).

The research model consist of three latent variable indicators; ( a ) benefit of Facebook, ( b ) purpose of Facebook and ( c ) social media usage

Benefit of social networking system (Facebook)

A conceptual framework for understanding the acceptance, diffusion, benefit, and innovative use of social media technology for educational purposes (content development and virtual classroom use) must delineate characteristics of participating individuals/groups, media features, relations of media and individual social networks. The model presented in this work demonstrates the benefit of social networking system, particularly Facebook (Fig. 1 a). We depicted Facebook benefit as a latent variable and described nominal variable indicators including, perceived usefulness (fundamental determinant of user acceptance), perceived ease of use (determinant of user behavior), social influence (thoughts actions, reactions of users influenced by other individuals or groups), perceived enjoyment (intention to use technology for fun) and collaborative platform (can be used as collaboration).

Perceived usefulness and perceived ease of use

Perceived usefulness is an important determinant of information system that supports decision-making/problem-solving activities. The significance of perceived usefulness has been extensively acknowledged in E-banking sector (Eriksson, Kerem, & Nilsson, 2005 ; Guriting & Ndubisi, 2006 ; Liao & Cheung, 2002 ). According to these studies, usefulness is the subjective probability that using modern technology is a great way to complete or improve the outcomes for a given task. In 1985, Davis proposed the actual Technology Acceptance Model (TAM) as an extension of the Theory of Reasoned Action (Davis, 1989 ; Fishbein & Ajzen, 1975 ). TAM is one of the most popular prediction-oriented research models dedicated to predict the primary motivational factors for the use and acceptance of new technologies and systems. According to Davis ( 1993 ), perceived usefulness can be defined as “the degree to which an individual believes that using a new technology or an information system would enhance his or her productivity. Perceived ease of use, in contrast, can be defined as “the degree to which a person believes that using a particular technologies and systems would be from mental or physical exertions (Davis, 1989 ).

Facebook Platform provides a set of tools, features and opportunities, which enable users to enjoy several online activities. Therefore, we chose Facebook for this research investigation. In this case, Facebook perceived usefulness is defined as “the degree of psychological immediacy and belief formation that using Facebook networking technology enhances performances of its users.” Facebook perceived ease of use is referred as the degree to which an individual believes that using Facebook would be free of mental or physical efforts.

Hypothesis 1 (H1): Perceived Usefulness will have a significant influence on user Facebook acceptance and benefit modes

Hypothesis 2 (H2): Perceived Ease of Use will have a remarkable effect on Facebook benefit modes.

Social influence

Social influence is a broad term that refers to multiple phenomena. In the past decade, various measures and definitions of social influence have been introduced in the prominent area of technology innovation and benefit. Social influence best describes “the extent to which individuals perceive that important or significant others believe they should embrace new technology and systems” (Venkatesh et al., 2003 ). Triandis ( 1980 ) refereed social influence or social factors as “the individual’s internalization of the reference groups subjective culture, and specific interpersonal agreements that the individual has made with others, in specific social situations”.

In this study, the concept of Social Influence is described as the degree to which a Facebook user perceives the importance of his/her significant others will response upon performing specific behaviors.

Hypothesis 3 (H3): Social Influence will have a significant relationship with Facebook benefit.

Perceived enjoyment

Self-service technologies (SST) have become omnipresent in modern society. Spectrum of self-service technologies have successfully launched and promoted in order to replace conventional services. The benefits of such technologies has well investigated in different contexts. Davis, Bagozzi, and Warshaw ( 1992 ) described the term perceived enjoyment in terms of technology benefits and usage as “the level whereby any task is deemed to be enjoyable regardless of other parameters like performance outcomes as a result of the system or service use.” Eighmey and McCord ( 1998 ) expressed that perceived enjoyment is a key influencing factor of intention to use internet and have a positive impact on the users’ choices of web browsing and application usage. Recently, Curran and Meuter ( 2007 ) reported that the users’ behavioral intention toward using social media is generally examined by the degree of enjoyment they experience which reflects that the benefit of self-service technology can certainly be impacted by the perceived enjoyment. In this study, the perceived enjoyment is described as the extent to which a Facebook user experience high level of enjoyment and positive interaction with their peers.

Hypothesis 4 (H4): Perceived enjoyment will have a significant effect on Facebook benefit and interaction modes.

Collaborative platform

Collaborative platforms are procedures, behaviors, and discussions or consultation that relate to the collaboration between individuals, groups or systems. Social networking tools such as Facebook can be utilized to create models for collaborative platform to facilitate cross-program collaboration or to create comfortable and familiar social settings among different individuals or other entities. Maloney ( 2007 ), demonstrated that the conversational, collaborative and communal characteristics of Social Networks augment the awareness of the process of learning.

Hypothesis 5 (H5): Collaborative platform will have a tremendous impact on benefit of Facebook

Purposes of Facebook usage

Facebook has widely been used for so many diverse purposes, including contacting friends,, maintaining social relationships, following updates about friends, school or class, visiting other’s profiles to obtain information about them, communicating with teachers, requesting information, uploading, tagging and sharing personal information or multimedia content and creating Pages or groups or joining different groups of common interests or pursuits (Ellison et al., 2007 ; Ito et al., 2008 ; Livingstone, 2009 ; Patterson, 2012 ; Stutzman, 2006 ). Depending on the purposes, the content user’s put on the social media differs in the formality, considered audience, and desired consequences (Fig. 1 b). The main purpose of this study is to design a structural equation model and apply confirmatory factor analysis system in order to investigate diffusion, benefit and acceptance of Facebook by undergraduate, graduate and research students at the University of Toyama. The overall results generated based on users’ social relations, their daily activities, purposes of Facebook usage and Facebook educational usage. In addition, we analysed attitude, perception and behaviour of Japanese students’ toward Facebook for educational usage, and determined how students from non-English speaking backgrounds (especially Japanese students) at the University of Toyama perceive the use of Facebook for learning English as a foreign language. Finally, we designed and used the model, after studying a compressive literature based on benefit, diffusion, acceptance and usage theories.

Thereafter, we analysed various factors affecting the relationship between the benefit processes and social characteristics users portrayed on Facebook for different activities. The final data analysed and fitted using structural equation modelling validates that the model designed for evaluating the effects of multiple variables on benefit of Facebook are perfectly fitted.

Social relations

One main facet of social networking service (especially Facebook) is its emphasis on creating and maintaining relationships, which include meeting new people and making friends, nurturing and maintaining good relations with existing ones. The frequent usage and popularity of Facebook is well documented in recent reports (Mazman & Usluel, 2010 ).

Hypothesis 6 (H6): Social relations will have a great impact on purposes of Facebook usage.

Resource/material sharing

Facebook has been a boon for most social media users, permitting even the most non-tech savvy person a possibility to connect with friends, exchange ideas and information, and predominantly have online existence. Facebook allow anyone to post online or share their relevant resources, materials like links or photos, projects or other documents. The content management capabilities of Facebook including uploading of images, audio or video clips can be very beneficial for various purposes.

In this study, the purpose of Facebook use for material/resource sharing consists of various tasks including, exchanging videos and audios, multimedia resources, projects of other document files etc.

Hypothesis 7 (H7): Resource and material sharing will have a great impact on the purpose of Facebook use.

Daily activity

Facebook dominate daily activity landscape, as notable majorities of youth use Facebook site worldwide ( Cheung et al., 2011 ). The daily activities of the people who use Facebook with varying degrees of frequency include wasting time on Facebook, keeping up to date with what is happening around one’s different level of social connections, having fun, playing games, monitoring social feedback on their status or joining other groups.

Hypothesis 8 (H8): Daily activities will have a great impact on purposes of Facebook use.

Keeping track

Keeping track of trends on the Facebook platform is now very popular (Sanchez et al., 2014 ). With the help of social Facebook features, users can easily keep track of what is happening around them and about the continued progress of other people.

Hypothesis 9 (H9): Keeping track will have a great impact on purposes of Facebook usage.

Social media usage

Unlike conventional media, social media technology is a disruptive mode of information communication among individuals, networks and devices as it enables multiple users to participate in virtual-reality and have their input in the discussion, no matter the conversation. The rapid development and popularization of information technology facilitates access to social networking system to large numbers of student population. Among various social networking sites, Facebook is very popular among students. Owing to its various features and utilities, it is widely considered as a useful tool for personal, work or education related online activities. There is a growing interest in social media technology research to analyze functionality and potential of social media tools in educational contexts. Some studies have examined that many social networking sites especially Facebook directly or indirectly facilitate informal learning process because of their engaging role in users’ daily routines and activities. It is believed that social networking system can support multiple personal, work related, and educational activities (Fig. 1 c). According to (Lee & McLoughlin, 2008 ), social networking sites acts as useful pedagogical tools because student users can effectively use many features of these sites for connections, collaboration, content creation sharing, information aggregation and knowledge modification. Framing a comprehensive Facebook policy that includes guidelines, recommendations, best practices and training tips for educational usage is therefore very essential.

Personal usage

Facebook is a specific type of social networking that allows users to connect with others and create a page, group or community. It is more about establishing relationships, exchanging information, sharing ideas, discussing topics of profound importance, collaborating with other on numerous projects than just sending messages to contacts or posting post status updates in the feeds (Mazman & Usluel, 2010 ; Roblyer et al., 2010 ; Sanchez et al., 2014 ; Sharma et al., 2016 ). Users can both utilize Facebook platform to nurture their personal friendship, to keep in touch with personal family and close relatives who live far away, to post personal updates or engage with fans and followers. Facebook for personal and educational usage consist of activities such as online conversation among students and their parents and friends.

Hypothesis 10 (H10): personal usage will have a great impact on social media usage of Facebook.

Educational usage

From educational point of view, Facebook is appraised as an acceptable social media platform for education because of its structure and controlled utilities. Many researchers believe that Facebook is a useful tool to develop confidence to enhance the communication skills among students/learners in various educational settings (Mazman & Usluel, 2010 ). Facebook based collaboration among various individuals for educational purposes consist of multiple activities. For example, joining various groups related to users institutions academic or extracurricular activities, peer to peer sharing of ideas, homework, assignments or project reports, or joining virtual classes.

Hypothesis 11 (H11): educational usage will have a great impact on social media usage of Facebook.

Work related usage

Now a day, Facebook is increasingly utilized by employees in various organizations for sending messages for personal and professional purposes during working time. There are several benefits of using social media at workplace, such as, creation and strengthening of ties with other organizations, collecting and analyzing information about market and competitors, free calling and chatting with a coworker about any subject, actively sharing, projects, materials, resources, work progress reports or supporting coworkers by using online and offline web functions (Donlan, 2014 ; Forkosh-Baruch & Hershkovitz, 2012 ; Kuzma & Wright, 2013 ; Sanchez et al., 2014 ; Sharma et al., 2016 ).

Hypothesis 12 (H12): Work related usage would have a great impact on social media usage of Facebook.

Community identification

Social identity is an individual’s belief about himself or herself, which is generally characterized by distinguishing factors: the feeling of reciprocal interaction, sympathy and responsibility among members of a group, which promote a sense of belonging within the community (solidarity to the group), specific conformity to ingroup norms and outgroup derogation or discrimination against outgroups (Riedlinger et al., 2004 ). In general, personal identity deals with questions about ourselves. In contrast, social identity designates that a particular member belongs to a particular society or group (Hogg, 2012 ). The individual’s identification with a social group has a positive impact on his/her self-image (Apaolaza et al., 2013 ; Dholakia et al., 2004 ; Valkenburg et al., 2017 ). In Facebook usage context, social identity can be outlined as the users’ identification within an online social network and virtual community.

Hypothesis 13 (H13): Community identity will have a great impact on social media usage.

Perceived communication

The youths of today feeling more relaxed and comfortable expressing themselves over text or social networking system than communicating via traditional face-to-face or telephonic conversation. They spend a lot of time on social media channels in order to emote and discuss about their real world real-life interactions, relationships and experiences. With proper framework and guidance on the utility of social media especially Facebook, it can be easy to create and promote online connections between teachers and students within educational settings. This strategy can help to improve overall communication within students and teachers communities, which will ultimately lead to better educational discussions beyond the classroom (Christofides et al., 2009 ; Mazer et al., 2007 ; Ross et al., 2009 ).

Hypothesis 14 (H14): Communication will have a great impact on social media usage of Facebook.

To construct structural equation model for Facebook usage, we first analyzed and determined various factors and variables that influence potential Facebook benefit processes and users sentiments. It was assumed, while constructing the structural equation Facebook model, that Facebook benefits are directly or indirectly associated with purposes of Facebook usage. This lead to the logically derived explanation or hypothesis that when different individuals’ choose to get benefit from new instructional materials or technologies, they use the beneficial systems for different purposes in their everyday lives, which give individuals the opportunities to make better choices. For instance, if individuals perceive some media is effective, useful or easy to use they tend to get benefit from it for accomplishing their desired tasks. Likewise, members of a particular community may get benefit from a material or product because of the virtual peer influence on user’s behavior from a community or group that they interact or communicate with socially or professionally, the outcome of which can be a convoluted network of identification with multiple communities from random walks.

Hypothesis 15 (H15): Facebook benefit will have a significant and positive relationship with purposes of Facebook usage.

Considering that the members of a particular community or region succeed in achieving essential learning opportunities when they use social networking technology (Facebook), both media characteristics and socio cultural dimensions of individuals with social media usage affect the entire educational attainment context. Therefore, it is indicated that together with Facebook benefits, motives of Facebook members are in sustained inundation with the academic related usage of Facebook.

Hypothesis 16 (H16): Facebook benefit mediated by the purposes of Facebook usage will have a significant and positive relationship with social media usage of Facebook.

The process of data collection took place in the University of Toyama and most of the study subjects were undergraduate, postgraduate students and researchers. Questionnaires that were the main tool of data collection were distributed manually and through online to estimate the influence of the social media factors, to propose various research hypotheses, to test the hypotheses by constructing structural equation model and to predict the outcome using test confirmatory factor. A structured questionnaire was distributed to a large number of male and female students. However, only 385 students responded and returned the fully answered questionnaire which were used for further analysis. This number is seen acceptable as it is reported that such studies requires at least 150 respondents to actively participate. According to Hair et al. ( 2010 ), for structural equation modeling (SEM) technique, 150 is acceptable parameter for measuring less than seven constructs and modest communalities.

In addition, we collected the data by means of an online survey, which was developed by the researchers. The survey consisted of three sections. In the first section, demographic characteristics of Facebook users were collected through four questions. Members’ frequency of Facebook usage, length of time spent in Facebook, and memberships to Facebook groups were collected within this section.

The second section was composed of a 5-point Likert scale with 5 questions aimed at gathering the members’ purposes of Facebook Usage, 5 questions aimed at gathering about the member’s benefit of Facebook and 5 questions for usage of Facebook. We tested validity and reliability of the scales. For validity, expert opinion was attained to see if the questions were appropriate in measuring the intended research questions and if the statements were understandable. Based on the feedback received from the experts, the scale was modified. Then, confirmatory and explanatory analyses were conducted to identify the relations between factors and factor loads. For the reliability analysis, Cronbach’s alpha values were calculated for each of the scales and their sub-factors. While developing the Facebook benefit scale, diffusion, acceptance, usage and purpose theories and models were reviewed to expand the coverage of the scale items.

The data obtained for the explanatory factor analysis revealed that the factor loads of items varied from 0.530 to 0.907. The Cronbach’s alpha reliability coefficient of the scale was found to be 0.883. The Cronbach’s alpha values for each of the factors, factor loadings of the items and goodness of fit indexes of confirmatory factor analyze results are presented in the tables.

Participants and data collection

Although a large proportion of surveyors were accessed, the study group consisted of 385 Facebook users who responded to the manual and online survey. We had 7 variables with missing values less than 5%, which we replaced with the median for ordinal scale and mean for continues scale. We also deleted four rows as they had more than 20% missing values. The web address of the survey was spread out in Facebook to the University of Toyama students and people who took the survey forwarded the survey’s link to their friends in University of Toyama voluntarily. Also, with the aim of accessing an extensive crowded people survey’s link was written on the various Facebook groups’ wall that belongs to the University of Toyama students. The survey was kept open for these participants on the web for 4 weeks. Table  1 summarizes the demographic profile of the participants including their gender, educational level language used, in addition to the descriptive statistics of their frequency of Facebook usage and length of stay in Facebook. As shown in the Table  1 , percentage of males were 70.2% and percentage of females were 29.8%. All of the respondents were college students among which 23% of the participants used Facebook 1–3 times a day, and only 18% don’t use daily. 36.9% of the students were using Facebook from more than 4 years and 25% of the respondents use Facebook less than a year.

Testing the structural model

In this study, we used structural equation modelling (SEM) and confirmatory factor analysis (CFA) tools for data analysis and testing relationships between variables. We performed SEM and CFA using SPSS (a software for statistical data analysis) and AMOS (a software that can be used to perform structural equation modeling). In brief, structural equation modeling is a family of multivariate statistical analysis methods used to model a network of complex structural relationships between one or more measured variables and latent constructs. Confirmatory factor analysis (CFA) method is used to verify the factor structure of a set of observed variables (Joseph, Marko, Torsten, & Christian, 2012 ). The proposed equation model that explain educational usage of Facebook was constructed using three latent variables, namely, Facebook benefit, users’ purposes and social media usage of Facebook were examined. Facebook benefit (“benefit”) was an Exogenous (independent) latent variable and “B_Usef” (perceived usefulness), “B_Euse” (perceived ease of use), “B_SocInf” (social influence) “B_Enj” (perceived enjoyment) and “B_ColPlm” (Collaborative platform) were observed variables to be accepted as significant anticipator of benefit. Users’ purposes (“purpose”) was Endogenous (dependent) latent variable and “P_DailyAct” (Daily activities), “P_MnRS” (resource/material sharing) and “P_SoReln” (Social Relations) and “P_KpTrk” (Keeping Track) were observed variables accepted as significant anticipator of purpose. Social media usage of Facebook (“Usage”) was another Endogenous (dependent) latent variable and “U_WrkR” (work related), “U_PerUsg” (personal usage), “U_EduUsg” (educational usage), “U_Iden” (perceived Identification) and “U_Com” (Communication) were observed variables, being accepted as significant anticipator of educational usage. We got three factors in the total variance experienced and that was the exact number of factors we wanted and the total variance experienced by the model was 71% and that is reasonable percentage of variance (Table  2 ).

The c 2 /df (chi-square /degree of freedom), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Non-Normed Fit Index/Tucker Lewis index (NNFI/TLI) and Incremental Fit Index (IFI) were examined to check the felicitousness of the solution and goodness-of-fit of the model (Table  3 ). As shown in Table  3 , all the indices exceeded their commonly accepted levels, demonstrating that the measurement model exhibited a good fit. Standardized and unstandardized path coefficients of structural model are shown in Figs.  2 and 3 , respectively. We observed the pattern matrix (Additional file 1 : Tables S1-S7) of all the three factors and the coefficients between benefit and its observed variables were found to be significant ( p  < .005 or t > 1.96). The overall results showed that the five observed variables, which are perceived usefulness, perceived ease of use, social influence, perceived enjoyment and collaborative platform have appreciably positive effect on Benefit (β = 0.87, β = 0.87, β = 0.90, β = 0.85, β = 0.80). (H1, H2, H3, H4, H5 supported).

The result of proposed research model (standardized estimates)

The result of the proposed research model (unstandardized estimates)

While checking the coefficients between purposes of Facebook usage and its observed variables, we found it to be significant ( p  < .005 or t > 1.96). The results obtained for four observed variables, namely social relations, material and resource sharing, keeping track and daily activity showed an appreciably positive impact on purpose (β = 0.79, β = 0.76, β = 0.90, β = 0.86). (H6, H7, H8 and H9 supported). The results for coefficients between usage of Facebook and its observed variables also showed good significance ( p  < .005 or t > 1.96). the overall data showed that that the five observed variables namely; communication, perceived identification and work related, personal usage and educational usage have also appreciably positive effect on Facebook usage (β = 0.76, β = 0.88, β = 0.86, β = 0.89, β = 0.77). (H9, H10, H11, H12 and H13 are supported).

In addition, we also tried to analyze the effect of Facebook benefit on Facebook purpose, and we obtained standardized path coefficient value (0.24) and the t-value (4.29). Thus, we assume that Facebook benefit has a positive effect on users’ purposes ( p  < .005) (H14 supported). Besides this, we found that Facebook benefit has approximately 74% (R 2 ) of the variance of purposes of Facebook usage. We also tried to examine the effect of users’ purposes on the usage of Facebook, the standardized path coefficient was found to be 0.40 and the t-value was found to be (7.03). Hence, our finding supports the view that purposes of Facebook usage has a significant positive effect on the usage of Facebook ( p  < .005) (H13 supported). Similarly, it was found that users’ purposes with their determinants by Facebook purpose account for approximately 65% (R 2 ) of the variance of usage of Facebook.

The Cronbach’s alpha reliability coefficient of the scale was found to be 0.883. The Cronbach’s alpha values for each of the factors, factor loadings of the items and goodness of fit indexes of confirmatory factor analyze results are presented in the tables (Tables  4 , 5 and 6 ).

While analyzing this study, we have tested a structural equation model using SPSS and AMOS to explain the educational use of Facebook. Taking support of this model we investigated, Usage of Facebook which is examined in five dimensions of Facebook communication including perceived identification, educational use, work related and personal usage. While testing the model, usage of Facebook is explained indirectly by purposes of Facebook and directly by benefit of Facebook. Benefit of Facebook was found to have a significant positive relationship with perceived usefulness, perceived ease of use, social influence perceived enjoyment and collaborative platform. Perceived enjoyment is determined as the most important factor in predicting the Benefit of Facebook. Therefore, enjoyment as perceived by Facebook users can be indicated as one of the major reasons for the expeditious use of Facebook and the expeditious increase in the number of its users.

In this study, we investigated three main factors for their influence on learner’s performance to learn English. This study was carried out in Japan where most of the people are shy to speak in English due to less practice in speaking English. Fourteen hypotheses of this study were accepted and this finding is consistent with previous researches. Learners’ skills also appeared to be developed through the use of social media in the context of learning English.

The results of the present study also disclosed that users’ purposes and information-seeking behaviors in web-based media have a strong and positive correlation with users’ social communities, resource and networking information sharing, routine activities and daily task tracking. Our finding is consistent with previously published reports. For example, (Lenhart & Madden, 2017 ) reported that 55% of teenagers use social networking technology and most of users utilize social media social for having fun, meeting new people and making friends, staying in touch with existing friends and posting messages, images and files, etc. In 2008, Joinson described that people use social networking sites to keep in touch with old friends from high school, college, or a past job, reconnect with professional network and lost contacts, communicate with people with similar interests, join social groups for different purposes, organize community-driven awareness events, post and share photos, view and save friends’ photographs etc. The results obtained for this study revealed that the consumption of social media (Facebook) for resource and material sharing is one of most vital factor among all of the intensive purposes studied. Our research have showed that social media can be associated with the fact that interactive Facebook mediated social media technology facilitate the virtual sharing of study material, educational resources, ideas, career interests and related information. The overall results indicate that the proper use of Facebook can have various positive effects on communication, perceived identification and emotional reactions, personal and work-related purposes, academic performances, but it all depends on how you user use advanced features of this platform.

Based on our findings, we speculated that consuming Facebook with the motive of maintaining personal and professional social relationships is directly associate with utilization for communication purposes. In this study, we observed that most of the Facebook users who participated in research work not only nurtured and balanced their relationships and communication patterns with their classmates and near and dear ones but they also shared ideas and exchanged information and views during overall communication process. Selwyn ( 2009 ) also reported that students’ use of Facebook in educational contexts could be classified into five different categories; recounting the events of their social media based learning activities and reflecting on their university on the linkages between their university experiences and educational contents, practical information regarding exchanging academic materials and resources, and exchanging potentially humorous ideas or entertaining materials. All of the assumptions support our results, which suggest that use of various Facebook features in daily activities, are strongly related with its educational usage capabilities.

In this study we also found that Facebook usage have a positive relationship with its observed variables and all the observed variables have almost equal distributions, that means students are willing to accept Facebook as one of the study tools. This study also shows that user’s purpose in using Facebook have a positive relationship with Facebook benefit. That means student’s find using Facebook very beneficial and useful for study purposes. The model was stipulated as good or even better on the radiance of the values that we got from the analysis and the 14 hypotheses of the study were verified and accepted. New correlations were also added to the model and the validity of the model was confirmed by the indices and goodness of fit indices. All of these results confirm that social media has many advantages like being useful, ease to use, communicable and can be lot beneficial for learning new things.

Conclusions

Currently, social media and communication technology have become ubiquitous and is absolutely reshaping almost every facet of human life in an unprecedented manner, and education sector is no exception. Social networking technologies have attracted hundreds of millions of users with different ages, gender, culture, language, educational qualifications or social status to build social networks, communicate and interact with each other for mutual assistance through the past years, which is of paramount importance for identifying the human behavior and relation patterns in different platforms. As one of these social networks, Facebook has diffused rapidly throughout the world because of its popularity and general pervasiveness in everyday life. Facebook has become a significant variable that influence the psychological and social functioning of numerous people, especially youth, from different backgrounds worldwide. Social media growth drifts reflect that more and more students of different age groups and educational levels are catching on and using social networking platforms. In parallel, demands and realistic expectations of school/college or university students in an age of digital social media are enormously changing at a rapid pace.

Although social media technology has the several features for improving the learning process, the use of social networking system has not made significant inroads into classroom or formal educational usage. In this work, we used structural equations modeling to conceptualize the use of Facebook and test a hypothetical model for deep social media analysis. We analysed the influence of various factors and parameters that may have a large effect on student motivation to adopt and use different Web 2.0 tools (Facebook), for various potential educational application. We designed and applied this model to different sample, from different demographic categories (most of the participants were students from the University of Toyama). The main objective of the study was to understand the factors that may attract students to adopt Facebook for education related activities. The comprehensive understanding of these factors can help the social media researchers to identify, develop and apply different strategies for not only increasing the adoption of social media (Facebook) for informal educational activities but to also fully realize the potential benefits of social media for formal learning at numerous educational settings.

Our research on Facebook suggests that social media networks compete with educational work for students’ attention. Hence, it is the duty of the student to make the right choice in relation to the use of social media networks for personal or for professional networking in different contexts. We speculate that cultural differences between students from different nationalities and their language barriers may greatly influence students’ temperament and learning styles, behavioral norms and expectations. Overall, this research work may have several implications for better understanding of students’ perceptions towards Facebook. The research work presented in this paper should be of great significance to teachers, researchers, education management and policymakers, social media and Internet regulators, and parents as well.

Availability of data and materials

The authors of this paper are ready to reproduce the materials and the data including usability test raw data that are presented in the manuscript. The raw data is accessible when scientist or reviewer wishing to use or assess it.

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SA; have conducted the actual study University of Toyama. BAM; collected the data prepared the paper template, HS ; advised how to write the paper, and reviewed the paper many times. FAM; reviewed and commented the paper and provided some background information. MS; act as a research advisor and supervised the project progress. All authors read and approved the final manuscript.

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Table S1. Pattern Matrix. Table S2. Covariance matrix of latent variables. Table S3. Standardized Regression Weights. Table S4. Factor Correlation Matrix. Table S5. One-Sample T-test Value. Table S6. Regression Weights: Hypothesis testing results. Table S7. Factor Score Weights. (DOCX 166 kb)

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Abrahim, S., Mir, B.A., Suhara, H. et al. Structural equation modeling and confirmatory factor analysis of social media use and education. Int J Educ Technol High Educ 16 , 32 (2019). https://doi.org/10.1186/s41239-019-0157-y

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Received : 12 October 2018

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Published : 22 July 2019

DOI : https://doi.org/10.1186/s41239-019-0157-y

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• Social media
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