Journal of Nursing Education

Major Article 

Validating the Japanese Self-Directed Learning Readiness Scale for Nursing Education

Yumiko Fujino-Oyama, PhD, RN; Rumi Maeda, MSN, RN; Mitsue Maru, DSN, RN; Tomoko Inoue, PhD, RN

Abstract

Background:

The Self-Directed Learning Readiness Scale for Nursing Education (SDLRSNE) assesses the extent to which an individual has the characteristics, capabilities, and attitudes required for self-directed learning. This study aimed to validate the Japanese version of the SDLRSNE with graduate-level nursing students.

Method:

Confirma-tory factor analyses, using data from a cross-sectional online survey of 376 nursing students, were conducted to examine construct validity. Relationships with potential related factors were analyzed to evaluate construct validity. Reliability was evaluated with item analysis and Cronbach's alpha.

Results:

Factor analyses revealed that three-factor and individual subscale models had a moderate-to-poor fit. No meaningful relationship with potential related factors was noted. Reliability measurements indicated a moderate fit to data.

Conclusion:

This study could not confirm that the Japanese version of the SDLRSNE had acceptable levels of reliability and validity when tested with graduate-level nursing students. Further research is needed to examine the psychometric properties of the Japanese version of the SDLRSNE with other adult nursing learners or with graduate-level nursing students in other countries. [J Nurs Educ. 2016;55(2):65–71.]

Abstract

Background:

The Self-Directed Learning Readiness Scale for Nursing Education (SDLRSNE) assesses the extent to which an individual has the characteristics, capabilities, and attitudes required for self-directed learning. This study aimed to validate the Japanese version of the SDLRSNE with graduate-level nursing students.

Method:

Confirma-tory factor analyses, using data from a cross-sectional online survey of 376 nursing students, were conducted to examine construct validity. Relationships with potential related factors were analyzed to evaluate construct validity. Reliability was evaluated with item analysis and Cronbach's alpha.

Results:

Factor analyses revealed that three-factor and individual subscale models had a moderate-to-poor fit. No meaningful relationship with potential related factors was noted. Reliability measurements indicated a moderate fit to data.

Conclusion:

This study could not confirm that the Japanese version of the SDLRSNE had acceptable levels of reliability and validity when tested with graduate-level nursing students. Further research is needed to examine the psychometric properties of the Japanese version of the SDLRSNE with other adult nursing learners or with graduate-level nursing students in other countries. [J Nurs Educ. 2016;55(2):65–71.]

As described by Knowles (1975), self-directed learning (SDL) is:

a process in which individuals take the initiatives, with or without the help of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating learning outcomes. (p. 23)

That learning approach is widely used in advanced education (Fisher, King, & Tague, 2001), and nurses are continuously required to reiterate this process for their professional development.

The SDL of graduate-level students in nursing is essential to enhance the educational effectiveness of nursing graduate schools. Graduate school is one of the most important environments for nurses' professional development. In Japan, the number of graduate schools in the nursing discipline increased from 14 programs in 1997 to 152 programs in 2014 (Japan Association of Nursing Programs in Universities, 2014). Thus, nurses with various backgrounds have more choices than before. Consequently, the demand for flexible learning systems, such as e-learning and night or weekend classes, has also increased. However, when nurses choose flexible learning systems they must have an autonomous learning attitude, such as SDL (Song & Hill, 2007).

SDL readiness is the extent to which an individual has the characteristics, capabilities, and attitudes required for SDL. In other words, it is the amount of responsibility the learner accepts for his or her own learning (Fisher et al., 2001). Two main scales measure SDL readiness, including the Guglielmino version of Self-Directed Learning Readiness Scale (SDLRS; Guglielmino, 1978) and the Fisher version of the SDLRS (Fisher et al., 2001). The Guglielmino version of the SDLRS was developed for general use with adults and was translated worldwide (including Japan). It is used in various fields and disciplines. However, the development process for the Guglielmino SDLRS is questionable because it has not been validated since the number of items was changed from 41 to 58. Thus, some researchers recommend against using the Guglielmino SDLRS (Field, 1989). Furthermore, some authors have reported that the Guglielmino SDLRS is difficult to use due to its complicated factor structure and high usage fee (Field, 1989; Fisher et al., 2001). Therefore, Fisher et al. (2001) developed a new SDLRS for nursing students, using item pools derived from the Guglielmino SDLRS and other scales. The Fisher SDLRS has already been translated and validated in other languages, such as Spanish (Fasce, Pérez, Ortiz, Parra, & Matus, 2011) and Turkish (Kocaman, Dicle, & Ugur, 2009) and is widely used in research in nursing and other health care fields (Abraham et al., 2011; Deyo, Huynh, Rochester, Sturpe, & Kiser, 2011; Fasce et al., 2011). However, although the Fisher SDLRS has confirmed high reliability, it has been reported to have unsound validity (Hendry & Ginns, 2009). In response, Fisher and King (2010) reexamined the factor structure of the scale to provide evidence of validity. Consequently, the validity of the Fisher SDLRS was verified, and the instrument was renamed the Self-Directed Learning Readiness Scale for Nursing Education (SDLRSNE). The SDLRSNE is currently used as the primary version of the SDLRS.

The SDLRSNE has a relatively simple structure, including 40 items evaluating three factors—self-management, desire for learning, and self-control—and is free to use. Thus, it is accessible for nurse educators who aim to evaluate educational effectiveness. The SDLRSNE was developed to measure nursing students' SDL readiness, and several studies have been conducted with undergraduate nursing students (El-Gilany & Abusaad Fel, 2013; Gagnon, Gagnon, Desmartis, & Njoya, 2013; Phillips, Turnbull, & He, 2015). The factor structure of the SDLRSNE is based on Garrison's SDL principles (Deyo et al., 2011; Garrison, 1997; Song & Hill, 2007), which include self-management (control), motivation (entering/task), and self-monitoring (responsibility). Therefore, it may be possible to apply the SDLRSNE more broadly to assess adult learners in nursing. Furthermore, validation is also needed to determine whether the SDLRSNE is valid and reliable when used with graduate-level students (Williams & Brown, 2013). In addition, it may be useful for the educational evaluation of graduate schools in Japan, as well as for comparative international studies. Hence, the aim of the current study was to examine the reliability and validity of the Japanese version of the SDLRSNE when used with graduate-level nursing students.

Method

Translation

The first author (Y.O.) translated the original version of the SDLRSNE into Japanese. Then, an English-language specialist, who had not read the original items, back-translated the items into English. The original developer of the SDLRSNE was asked to review the original and the back-translated versions, and then a preliminary Japanese version of the SDLRSNE was developed after minor corrections to the wording of some items. Finally, the authors developed the Japanese version of the SDLRSNE after evaluating face validity. The details of this process are described elsewhere (Oyama, Maeda, & Maru, 2015).

Participants

Participants in the current study included graduate-level nursing students from 45 of the 144 graduate schools in Japan (18 national, 17 prefectural or municipal, and 10 private universities) who agreed to participate. University participation was gained through the administration office of each graduate school in November 2014. The Web site address for the online cross-sectional questionnaire survey was e-mailed to nursing graduate students from the 45 graduate schools. A second e-mail reminder was sent 2 weeks after the first e-mail. An informed consent was included in the survey. Participants were free to decline to participate without penalty. The survey data were collected from November 2014 to December 2014. Participants who fully completed the survey were assumed to have provided consent to participate in the study. The authors estimated the number of potential participants to be 1,555, based on the enrollment limit of each graduate school, producing an estimated response rate of 24.2% (N = 376). The study was approved by the ethical committee of the Tokyo Medical and Dental University, School of Medicine (No. 1870).

Measures

Self-Directed Learning Readiness Scale for Nursing Education. The SDLRSNE consists of 40 items rated on a 5-point Likert scale, ranging from 1 = strongly disagree to 5 = strongly agree. Of those 40 items, four items are reversed scored for data analysis. Scores range from 40 to 200 points, and 150 points is used as the cut-off for having SDL readiness. The SDLRSNE also includes the following three subscales: Self-Management (13 items; e.g., “I have good management skills”), Desire for Learning (12 items; e.g., “I want to learn new information”), and Self-Control (15 items; e.g., “I am responsible for my own decisions/actions”; Fisher et al., 2001; Fisher & King, 2010).

Potential Related Factors. The authors determined the appropriate variables to use as potential factors related to the SDLRSNE by a thorough review of published work and discussions. As a result, respondents were asked about their age, sex (Kar et al., 2014), educational level (diploma or associate's, bachelor's, or master's degree; Matsuura et al., 2003; Phillips et al., 2015), academic activity experience (Abraham et al., 2011; Deyo et al., 2011), type of university (national, prefectural or municipal, or private), employment status (full time, part time, or unemployed), and living arrangement (living alone or with other persons, living with a spouse or children, or living with a spouse and children). Respondents were asked to indicate whether they had experience with academic activities, which included writing a paper, presenting at a conference, or writing a book chapter. Respondents who answered yes to one or more experiences were then categorized as having “present” experience, and all others were categorized as “not present.”

Data Analysis

First, to evaluate reliability, the authors calculated the mean and standard deviation (SD) of each item and item–total correlation coefficients for item analysis. Then, the Kolmogorov-Smirnov test was conducted to examine normality of the total SDLRSNE score, and Cronbach's alpha values were calculated.

To evaluate construct validity, the authors conducted confirmative factor analyses (CFA), whereby fit of a three-factor model, which assumed that items load on an original factor, was investigated. The authors also investigated the fit of individual subscale models, which assumed that items load on each hypothesized original subscale. The value and p-value of chi square, the standardized root-mean-square residual (SRMR), the root-mean-square error of approximation (RMSEA), the comparative fit index (CFI), and the parsimony goodness-of-fit index (PGFI) were calculated to determine and compare model fit. The following referential values were used: SRMR ⩽0.09, RMSEA ⩽0.08, CFI ⩾0.90, and PGFI ⩾0.50 (Hooper, Coughlan, & Mullen, 2008). For further analysis of construct validity, the authors investigated the relationship between SDL readiness and potential related factors to investigate whether the SDLRSNE could be distinguished by the groups that were expected to have a different distribution of SDLRSNE scores (Shimazu, Sonnentag, Kubota, & Kawakami, 2012). For age, the Spearman correlation test was used. Analysis of variance and t tests with the Tukey test were used for three categories of variables and dichotomous variable, respectively. All statistical analyses were conducted using SAS version 9.4 software.

Results

Table 1 shows the characteristics of the 376 participants. Mean age was 38.6 years, and the majority (88.3%) of participants were women. In terms of educational level, 27.7% of all participants were master's students, and 71.5% had academic activity experience. Approximately half of the participants were employed full time (47.6%) and lived with a spouse or a spouse and children (48.1%).

Demographics of Study Participants (N = 376)

Table 1:

Demographics of Study Participants (N = 376)

Table 2 shows the results of item analysis of the SDLRSNE. No item showed a ceiling and floor effect, and all items, except for item 20, had appropriate item–total correlations (p < .001). Variance of the total score was normally distributed (Kolmogorov-Smirnov test p-value = .143), and the mean was 139.5 (SD = 17.2). The Cronbach's alpha values were .914 for the three-factor model, .841 for the Self-Management subscale, .781 for the Desire for Learning subscale, and .836 for the Self-Control subscale, indicating moderate-to-good internal consistency for the three-factor model and all three subscales (Table 3).

Results of Participants' (N = 376) Self-Directed Learning Readiness Scale Item AnalysisResults of Participants' (N = 376) Self-Directed Learning Readiness Scale Item Analysis

Table 2:

Results of Participants' (N = 376) Self-Directed Learning Readiness Scale Item Analysis

Model Fit Statistics for Confirmative Factor Analysis of Study Participants (N = 376)

Table 3:

Model Fit Statistics for Confirmative Factor Analysis of Study Participants (N = 376)

Table 3 shows the results of CFA. In terms of the three-factor model, the chi-square value was 6087 (p < .001), indicating a poor fit between the original model and the data. However, the chi-square test is sensitive to sample size. Thus, the authors referred to other indices, including the SRMR = 0.097; RMSEA, 95% confidence interval (CI) = 0.081 [0.078, 0.085]; CFI = 0.654; and PGFI = 0.673. On the basis of referential values of those indices, the three-factor model showed a moderate-to-poor fit to the data. The fit indices of the Self-Management sub-scale were: SRMR = 0.064; RMSEA, 95% CI = 0.092 [0.081, 0.103]; CFI = 0.844; and PGFI = 0.748. The fit indices of the Desire for Learning subscale were: SRMR = 0.067; RMSEA, 95% CI = 0.09 [0.078, 0.103]; CFI = 0.848; and PGFI = 0.741. The fit indices of the Self-Control subscale were: SRMR = 0.078; RMSEA, 95% CI = 0.116 [0.106, 0.125]; CFI = 0.720; and PGFI = 0.713. On the basis of the referential values of those indices, all of the individual subscale models indicated a comparatively better good fit than the three-factor model but only moderate fit to the data.

Table 4 shows the results of the relationship between SDL readiness and potential related factors to further examine the construct validity of the SDLRSNE. Only the living arrangement item showed a significant relationship to the SDLRSNE (p = .014).

Relationship Between the Self-Directed Learning Readiness Scale for Nursing Education and Potential Related Factors of Study Participants (N = 376)

Table 4:

Relationship Between the Self-Directed Learning Readiness Scale for Nursing Education and Potential Related Factors of Study Participants (N = 376)

Discussion

The aim of the current study was to examine the validity and reliability of the Japanese version of the SDLRSNE when used with graduate-level nursing students. Therefore, the authors conducted an item analysis and examined internal consistency. Furthermore, the authors conducted CFA and examined the relationship between the total score of the Japanese version of the SDLRSNE and potential related factors.

The overall reliability of the Japanese version of the SDLRSNE was good to moderate; however, some issues remain. For example, Cronbach's alpha values indicated moderate-to-good internal consistency for the three-factor model and all three subscales. At the same time, the model with the larger number of items tended to have higher Cronbach's alpha values. Consequently, it is possible that the actual Cronbach's alpha values could be lower. The second issue was that the item “I am aware of my own limitations” (Self-Control) showed a low item–total correlation. This might have occurred because some participants positively interpreted this item, viewing it as an essential characteristic to properly grasp their capability and ability to utilize appropriate learning. In contrast, some participants interpreted the item negatively, viewing it as a limitation that restricts their personal capability. Thus, the inconsistency of interpretations might weaken the item–total correlation. The third issue was that the total score was substantially lower than scores obtained in other studies (El-Gilany & Abusaad Fel, 2013; Kocaman et al., 2009; Phillips et al., 2015). Similarly, previous nursing research in Japan using the other version of the SDLRS reported significantly lower total scores than studies conducted in foreign countries (Matsuura et al., 2003; Nishizono, 2013; Yamauchi, 2013). The government controls the curricula of basic nursing education in Japan, thus the curricula is common in nursing schools, regardless of whether it is a diploma, associate, or baccalaureate degree program. Therefore, it has been indicated that basic nursing education, especially clinical practice, has become mandatory education (Ministry of Health, Labour and Welfare, 2006). Because nurses (even at the graduate level) have not had sufficient opportunities to develop SDL attitudes, it may have resulted in the score differences found between nurses from Japan and foreign countries. Hence, it may be concluded that differences in educational cultural background affected the total score, rather than problems in the Japanese version of the SDLRSNE.

The overall validity of the Japanese version of the SDLRSNE was not strong. Regarding CFA, the authors attained only moderate-to-poor model fit indices. Several possible reasons for this finding include the sample size or sampling methods. However, the 95% CI of RMSEA was specifically examined, and it is evident that its range was not wide enough to affect the judgment of an absolute fit to the data. Consequently, the reason for poor fit indices may have resulted from a lack of fit with Japanese nursing graduate-level students in the context of the SDLRSNE or of the factor structure, rather than sample size or the sampling approach. Meanwhile, because not all fit indices showed poor fit, these findings are not necessarily controvertible. Therefore, further studies conducted in other countries are needed.

In terms of the relationship between the total SDLRSNE score and potential related factors, nearly all variables were not significantly related to the total SDLRSNE score. The group of participants living with a spouse or living with a spouse and children was the only variable that showed a significantly higher total SDLRSNE score, compared with the living alone or with others group. When graduate-level students, who are required to balance study and family responsibilities, are considered, it is reasonable to assume that the SDL readiness of participants who live with a spouse or with a spouse and children may be higher than that of participants who live alone or with others. This result may have occurred because the participants who live with a spouse or with a spouse and children contingently tended to have higher SDL readiness.

Although previous studies differ in their findings about the effects of demographic factors on SDL readiness, those differences may have occurred as a result of the research context (El-Gilany & Abusaad Fel, 2013). The participants' SDL readiness in the current study might be dependent on individuals' effort and capability, leading to failure to further prove divergent construct validity. The reason this failure occurred may be that only a few participants had experienced new learning systems, such as problem-based learning, that could enhance SDL readiness. For instance, the participants may not have experienced problem-based learning in Japan as a result of their age group (Fujikura, 2012) because this type of learning system was introduced in nursing schools only around 2005. Further research, perhaps using a qualitative design, should be conducted to identify factors affecting the SDL readiness of adult learners without experience with new learning systems designed to enhance their SDL readiness.

Limitations

The current study had several limitations. First, a relatively low response rate was attained. However, many nursing graduate schools in Japan do not meet their quota for enrollment. For example, some graduate schools that provided information regarding actual student enrollment numbers had met only half of their quota. Therefore, the actual response rate might be higher than our estimate of 24.2%. Second, other scales to examine concurrent validity were not used because the authors could not find an appropriate Japanese scale that measured a concept similar to the SDLRSNE. Examining other aspects of validity may strengthen the conclusions about the validity of the SDLRSNE.

Conclusion

The current study could not confirm that the Japanese version of the SDLRSNE had acceptable levels of reliability and validity when used with graduate-level nursing students. Further research is needed to examine the psychometric properties of the Japanese version of the SDLRSNE with other adult nursing learners or with graduate-level nursing students in other countries.

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Demographics of Study Participants (N = 376)

Demographic n (%)
Sex
  Male 44 (11.7)
  Female 332 (88.3)
Educational level
  Diploma or associate degree 151 (40.2)
  Bachelor's 121 (32.2)
  Master's 104 (27.7)
Experience with academic activities
  Present 269 (71.5)
  Not present 107 (28.5)
Type of university
  National 151 (40.2)
  Prefectural or municipal 121 (32.2)
  Private 104 (27.7)
Employment status
  Full time 179 (47.6)
  Part time 69 (18.4)
  Unemployed 128 (34)
Living arrangement
  Living alone or with other person(s) 195 (51.9)
  Living with a spouse or children 68 (18.1)
  Living with a spouse and children 113 (30)

Results of Participants' (N = 376) Self-Directed Learning Readiness Scale Item Analysis

Scale Item Mean (SD) I-T Correlation p
1. I solve problems using a plan. 3.44 (0.94) 0.63 <.001
2. I prioritize my work. 4.19 (0.69) 0.38 <.001
3. I do not manage my time well.a 3.06 (0.98) 0.43 <.001
4. I have good management skills. 2.75 (0.90) 0.52 <.001
5. I set strict time frames. 3.10 (1.03) 0.50 <.001
6. I prefer to plan my own learning. 3.38 (1.06) 0.59 <.001
7. I am systematic in my learning. 3.21 (0.92) 0.61 <.001
8. I am able to focus on a problem. 3.63 (0.83) 0.52 <.001
9. I need to know why. 4.26 (0.68) 0.40 <.001
10. I critically evaluate new ideas. 2.96 (0.94) 0.26 <.001
11. I prefer to set my own learning goals. 3.53 (0.95) 0.54 <.001
12. I learn from my mistakes. 4.03 (0.75) 0.36 <.001
13. I am open to new ideas. 3.99 (0.78) 0.28 <.001
14. When presented with a problem I cannot resolve, I will ask for assistance. 4.09 (0.79) 0.21 <.001
15. I am responsible. 4.14 (0.68) 0.46 <.001
16. I like to evaluate what I do. 3.39 (0.95) 0.56 <.001
17. I have high personal expectations. 3.00 (1.00) 0.49 <.001
18. I have high personal standards. 3.09 (0.97) 0.42 <.001
19. I have high beliefs in my abilities. 2.60 (0.93) 0.52 <.001
20. I am aware of my own limitations. 3.78 (0.87) 0.07 .194
21. I am confident in my ability to search out information. 3.01 (0.97) 0.53 <.001
22. I do not enjoy studying.a 3.79 (0.87) 0.55 <.001
23. I have a need to learn. 4.50 (0.50) 0.22 <.001
24. I enjoy a challenge. 4.08 (0.75) 0.55 <.001
25. I want to learn new information. 4.36 (0.57) 0.40 <.001
26. I enjoy learning new information. 4.31 (0.66) 0.42 <.001
27. I set specific times for my study. 3.01 (1.07) 0.50 <.001
28. I am self-disciplined. 2.83 (1.04) 0.43 <.001
29. I like to gather the facts before I make a decision. 3.76 (0.81) 0.43 <.001
30. I am disorganized.a 2.88 (1.06) 0.45 <.001
31. I am logical. 2.91 (0.96) 0.54 <.001
32. I am methodical. 2.79 (1.10) 0.42 <.001
33. I evaluate my own performance. 3.06 (0.97) 0.51 <.001
34. I prefer to set my own criteria for evaluating my performance. 2.91 (0.96) 0.53 <.001
35. I am responsible for my own decisions/actions. 4.05 (0.69) 0.47 <.001
36. I can be trusted to pursue my own learning. 3.60 (0.85) 0.55 <.001
37. I can find out information for myself. 3.48 (0.85) 0.51 <.001
38. I like to make decisions for myself. 3.83 (0.88) 0.50 <.001
39. I prefer to set my own goals. 3.53 (0.91) 0.58 <.001
40. I am not in control of my life.a 3.22 (1.15) 0.46 <.001
Total Self-Directed Learning Readiness Scale for Nursing Education score 139.5 (17.2)

Model Fit Statistics for Confirmative Factor Analysis of Study Participants (N = 376)

Variable Three-Factor Model Individual Subscale Model

Self-Management Desire for Learning Self-Control
Chi square 6078 270 219 541
Chi-square df 737 65 54 90
Pr < chi square <.001 <.001 <.001 <.001
SRMR .097 .064 .067 .078
RMSEA [95% CI] .081 [.078, .085] .092 [.081, .103] .09 [.078, .103] .116 [.106, .125]
CFI .654 .844 .848 .720
PGFI .673 .748 .741 .713
Cronbach's alpha .914 .841 .781 .836

Relationship Between the Self-Directed Learning Readiness Scale for Nursing Education and Potential Related Factors of Study Participants (N = 376)

Variable Mean p
Age (years) 0.006a .9
Sex
  Male 143.9 .070
  Female 138.9
Educational level
  Diploma or associate degree 139.3 .34
  Bachelor's 138.1
  Master's 141.4
Experience with academic activities
  Present 140.2 .24
  Not present 137.7
Type of university
  National 139.6 .94
  Prefectural or municipal 139
  Private 139.8
Employment status
  Full time 138.1 .164
  Part time 142.7
  Unemployed 139.8
Living arrangement
  Living alone or with other person(s) 137.2 .01*
  Living with a spouse or children 143.8
  Living with a spouse and children 141
Authors

Dr. Fujino-Oyama is Assistant Professor, and Ms. Maeda is Assistant Professor, Nursing Career Pathway Center, Dr. Inoue is Professor, Department of Critical and Invasive-Palliative Care Nursing, Graduate School of Health Care Sciences, Tokyo Medical and Dental University, Tokyo; and Dr. Maru is Professor, International Nursing Development, School of Nursing and Rehabilitation, Konan Women's University, Kobe, Japan.

This study was supported by a grant (15K20663) from the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant-in-Aid for Young Scientists (B), founded by the JSPS.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

The authors thank Dr. Murray Fisher, Associate Professor, University of Sydney, for his valuable advice and support in the research process.

Address correspondence to Yumiko Fujino-Oyama, PhD, RN, Assistant Professor, Nursing Career Pathway Center, Graduate School of Health Care Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan; e-mail: oyama.ncp@tmd.ac.jp.

Received: May 26, 2015

Accepted: November 02, 2015

 

10.3928/01484834-20160114-02

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