Tynjälä (2008) expressed that some factors have challenged not only educational institutions but also work organizations to develop new ways of ensuring the level of competence of the workforce. These factors can be listed as the rapid development of information and communications technology, the growing production of knowledge in the economy, increasing internationalization and globalization as well as changes in occupational structures and changes in the contents and organization of work. Additionally, the nature of many jobs has shifted as a result of these factors (Jacobs & Park, 2009). This change in the nature of work requires employees to learn constantly so that the companies can survive in a highly competitive environment and the employees can contribute to their companies at maximum level. Therefore, workplace learning has become essential for employees and organizations competing in international markets (Tynjälä, 2008).

According to the workplace learning theory of Billett (2001a), workplaces structure and routinely provide learning experiences as part of everyday work activities and through guidance from other workers. He approaches workplace learning from a perspective of the reciprocal relationship between working and learning: what the workplace affords and how these affordances are benefited by the individuals. Workplace affordances can be defined as the readiness of the workplace to afford opportunities for individuals to participate in work activities and support (Billett, 2001b). Workplace affordances emphasize the role that the norms and social practices that comprise workplaces play in regulating individuals’ engagement in and learning through work (Billett, 2006). They are one of the main determiners of the quality of workplace learning (Billett, 2001b). However, personal factors are positioned at the heart of the learning since they influence individuals’ choice whether to benefit from affordances provided by the workplace (Billett, 2014). Subjectivity and agency are among these factors. They structure individuals’ cognitive experiences by interpreting and constructing the affordances provided for them (Billett, 2008; 2009). Thus, it is important to consider the relationships among learning, affordances, and personal factors while trying to explain workplace learning (Billett, 2008).

In this study, universities are regarded as workplaces. In these workplaces, research assistants can simultaneously work and carry on their postgraduate education. By working and studying, they learn to be the future supervisors, researchers, lecturers, and professors. In order to investigate research assistants’ workplace learning mechanisms, it is important to use appropriate measures to assess the learning potential of the universities as workplaces. The purpose of the study is to adapt the Learning Potential of the Workplace Scale (Nikolova et al., 2014) into Turkish language and culture. The results of the study would enable researchers to make cross-cultural comparisons on the affordances provided by universities for research assistants.

Learning Potential of the Workplace Scale, which was developed by Nikolova et al. (2014), was used as the data collection tool in this study. This instrument includes 12 items and 4 factors. The items were rated on a 5-point Likert type scale ranging from 1 (not applicable at all) to 5 (completely applicable). Nikolova et al. (2014) conducted a confirmatory factor analysis (CFA) to test the fit of the hypothesized model to their data. They found a good fit to the data, χ2/df= 3.15, RMSEA=.05, NFI=.98, CFI=.99, TLI=.98, the factor loadings ranged from .63 to .93, and Cronbach alpha coefficients of all factors were above .70. They also provided evidence for convergent and divergent validity. For the scale adaptation process, the corresponding author Dr. Irina Nikolova was contacted through e-mail, and her permission to adapt the scale was granted. Then, the items were translated into Turkish by four interpreters who were graduated from English Language Teaching programs and were fluent in both English and Turkish. The translated items were reviewed. The items representing the measured quality best, and the items with the most consistent translation were chosen. The obtained form was sent to four experts, two of whom evaluated the items in terms of Turkish language, and two of whom evaluated the scale in terms of content validity. Minor revisions were made based on these feedbacks. Then, a focus group interview was conducted with two research assistants. After the interview, minor revisions were made in terms of the meaning and comprehensibility of the scale. The data were collected from 296 research assistants (57.1% female and 42.9% male) from over 20 universities in Turkey. The mean age of the participants was 29.55 (SD=3.68). Most of the participants (56.1 %) had temporary contracts and most of them (68.2%) were doctoral students while some of them (20.3%) were studying at Master’s level. Minority (11.5%) of the participants were PhD graduates. The data were collected on-line by accessing the participants through e-mails. The factorial structure of the Turkish version of the Learning Potential of the Workplace Scale was examined using CFA. The internal validity of the factors was examined using Cronbach alpha coefficients. The critical alpha value was set at .05 for all the significance tests. SPSS 20, AMOS 24, and Mplus 6.12 software were used during the data analyses.

Prior to the CFA, the assumptions of CFA, which were outliers, univariate and multivariate normality, and multicollinearity, were examined (Flora, LaBrish, & Chalmers, 2012). Six outliers were detected using Mahalanobis distance values; however, since they were not influential on the results, they were maintained in the dataset. Univariate normality was checked using skewness and kurtosis values, and Q-Q plots. Since the skewness and kurtosis values were between ±1 and the data distributed on a 45-degree line, univariate normality was assumed (Kline, 2011; Tabachnick & Fidell, 2001). On the other hand, multivariate normality was checked using Mardia’s test. The test revealed a multivariate kurtosis was above 10 and the critical value was above 1.96, which indicated that the data violated the assumption of multivariate normality (Mardia, 1970; Shen, Schüttemeyer, & Braun, 2009). Finally, multicollinearity was checked by estimating the correlations among the items and factors. Since all of the correlations were below .90, there was no multicollinearity problem in the data (Kline, 2011). As the data did not distribute multivariately normal, the maximum likelihood estimation with robust standard errors (MLR) method, an estimation method used in SEM analyses which did not require multivariate normality was used in this study (Muthen & Muthen, 2007). The model fit was evaluated using multiple model fit indices, Normed Chi-square (Chi-square/degree of freedom), CFI, RMSEA, SRMR, which were recommended by MacCallum, Browne, and Sugawara (1996). The Chi-square test was found statistically significant, χ2 (48) = 105.714, p<.05. The other indexes were found as follows: χ2/df = 2.20, CFI= .97, RMSEA=.06 (90% confidence interval=.047-.080), and SRMR= .02. The findings showed a good fit of the data to the model (Kline, 2011). All of the items were loaded in their expected factors statistically significantly, and factor loadings were ranged from .54 to .96, which were above the critical value of .30 (Hair et al., 2006). The Cronbach alpha coefficients of the factors ranged from .78 to .95 indicating high score reliability (Kline, 2011).

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