Technology and Health Care 22 (2014) 379–386 DOI 10.3233/THC-140794 IOS Press
379
Psychometric properties and factor structure of an L2 reading motivation questionnaire Hee-jung Kima and Sunhee Choib,∗
a Department
b Department
of English, Hanbyeol High School, Wanju, Korea of English Education, Jeonju University, Jeonju, Korea
Received 28 October 2013 Accepted 23 January 2014 Abstract. The purpose of this study is to investigate the psychometric properties and factor structure of a popular second language reading motivation questionnaire developed by Mori (2002). 550 first year high school students in Korea answered the 30-item questionnaire which consists of statements indicating different degrees of English reading motivation. Exploratory factor analysis was conducted with principal axis factoring and promax rotation, which yielded a four-factor solution. The factors included ‘Intercultural and Intellectual Orientation’, ‘Reading Efficacy’, ‘Intrinsic Motivation’, and ‘Negative Attitudes’. The results supported the multidimensionality of the construct of L2 reading motivation, but could not replicate the nine factor structure which was originally proposed by Mori. The implications for further research on L2 reading motivation and development of a more valid L2 reading motivation scale are discussed. Keywords: Reading motivation, second language learning, factor analysis, scale development
1. Introduction Second language (L2) reading is a crucial component of L2 learning and mastering L2 reading skill involves a variety of skills, knowledge, and strategies [1]. Yet, one of the most essential elements for successful L2 reading is one’s active involvement in the reading task propelled by his or her motivation to read in L2 [2]. The purpose of this study is to identify the internal factor structure of L2 reading motivation and to discover the psychometric properties of the items included in a commonly used L2 reading motivation scale. In order to achieve this goal, an L2 reading motivation questionnaire developed by Mori [3] was used to measure English reading motivation of Korean high school students and the measures were factor analyzed. The information and insights gained from the study will help identify the items of the L2 reading motivation questionnaire that are pure measures of the target construct, and calibrate its subscales by distinguishing good items from not so good ones [4]. ∗ Corresponding author: Sunhee Choi, Department of English Education, Jeonju University, 303 Cheonjam-ro, Jeonju, 560759, Korea. E-mail: [email protected].
c 2014 – IOS Press and the authors. All rights reserved 0928-7329/14/$27.50
380
H.-J. Kim and S. Choi / Psychometric properties and factor structure of an L2 reading motivation questionnaire Table 1 Motivations for Reading Questionnaire (MRQ)
Categories Self-efficacy
Dimensions Efficacy – Belief that one can be successful at reading Challenge – Willingness to take on difficult reading material Work avoidance – Desire to avoid reading activity Intrinsic motivation and learning goals Curiosity – Desire to read topics of interest Involvement – Enjoyment received from reading Importance – Value placed on reading Extrinsic motivation and performance goals Recognition – Pleasure of receiving a tangible form of recognition for success in reading Grades – Desire for positive school evaluations by teacher Competition – Desire to outperform others in reading Social motivation for reading Social reasons – Sharing meaning gained from reading with others Compliance – Reading to meet others’ expectations
2. Reading motivation Reading motivation is a psychological construct which influences one’s reading behaviors, and it is hypothesized that the construct is multidimensional, composed of a set of latent variables [5]. When we are highly motivated to read, we tend to read more, actively involve ourselves in selecting books, put more efforts in comprehending what we read, and to persist in the face of difficulty [2,6]. Different scholars have proposed different measurement tools to investigate the multidimensional aspects of reading motivation. As for first language (L1) reading motivation, a popular scale, the Motivations for Reading Questionnaire (MRQ), was developed by Wigfield and Guthrie [6] applying several social-psychological motivational theories. The MRQ is made up with three categories and 11 dimensions as illustrated in Table 1 [7]. With regard to L2 reading motivation, Mori [3] proposed a nine-factor model and a corresponding scale which was developed based on MRQ. Mori’s L2 reading motivation questionnaire is a Likert scale with 30 items. It is different from the MRQ in that it excludes the subscales for competition, recognition, and social reasons which Mori believed are irrelevant to EFL college students, the subjects of her own research. Instead, she added seven items to measure one’s integrative orientation, which reflects one’ interest in the people and culture of L2, a unique domain for L2 learning. 3. Methods 3.1. Participants and instrument The participants were 550 10th grade students from two high schools located in Jeonbuk province in Korea. The sample consisted of 264 female (48%) and 286 male students (52%). For the study, Mori’s L2 reading motivation questionnaire was adopted. The questionnaire is a 30-item self-report measure which asks respondents to indicate the extent of their agreement with each item on a five-point Likert scale ranging from strongly disagree (1) to strongly agree (5). 3.2. Statistical analysis Exploratory Factor Analysis (EFA) was conducted with SPSS 18.0 following the recommendations made by Fabrigar et al. [8] as well as other related research [4,9,10]. Before conducting EFA, the skew-
H.-J. Kim and S. Choi / Psychometric properties and factor structure of an L2 reading motivation questionnaire
381
Table 2 Descriptive statistics and reliability analysis Items
Mean
SD
Skewness
Kurtosis
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
3.51 3.38 3.93 2.93 3.36 3.03 2.92 3.06 2.88 2.90 2.57 2.52 2.62 3.26 2.81 2.56 3.29 3.67 3.34 3.54 2.95 2.61 2.61 3.36 3.17 3.28 2.10 2.74 2.86 2.70
1.185 1.127 0.979 1.111 1.149 1.068 0.973 1.013 0.998 1.048 0.959 0.978 1.025 1.169 1.001 0.989 1.010 0.883 0.972 1.003 1.088 0.938 1.003 0.899 1.018 0.969 0.883 0.952 0.860 1.002
−0.604 −0.465 −0.916 0.084 −0.295 −0.099 0.105 −0.092 0.148 0.112 0.187 0.228 0.175 −0.381 −0.077 0.259 −0.133 −0.634 −0.262 −0.290 0.167 0.202 0.470 −0.338 −0.195 −0.335 0.767 0.348 −0.012 0.367
−0.447 −0.531 0.644 −0.663 −0.671 −0.648 −0.589 −0.640 −0.529 −0.540 −0.280 −0.338 −0.423 −0.732 −0.381 −0.321 −0.469 0.632 −0.165 −0.331 −0.677 −0.158 −0.056 0.229 −0.484 −0.211 0.578 −0.086 0.033 −0.277
Corrected item-total correlation 0.539 0.587 0.488 0.498 0.591 0.631 0.416 0.436 0.606 0.567 0.606 0.678 0.730 0.664 0.757 0.677 0.466 0.448 0.671 0.068 0.391 0.617 0.547 0.606 0.550 0.571 0.495 0.570 0.560 0.625
α if item deleted 0.934 0.933 0.935 0.935 0.933 0.933 0.935 0.935 0.933 0.934 0.933 0.932 0.932 0.932 0.931 0.932 0.935 0.935 0.932 0.939 0.936 0.933 0.934 0.933 0.934 0.934 0.934 0.934 0.934 0.933
Squared multiple correlation (R2 ) 0.502 0.484 0.424 0.340 0.542 0.511 0.342 0.449 0.542 0.396 0.683 0.634 0.659 0.606 0.660 0.616 0.620 0.416 0.641 0.091 0.517 0.519 0.449 0.600 0.548 0.601 0.459 0.481 0.412 0.548
ness and kurtosis tests were executed on the items to examine the distribution of the data. As shown in Table 2, the data are normally distributed and therefore are acceptable for further analyses. Next, the reliability of the initial scale was assessed by calculating the internal consistency of the items. The Cronbach’s alpha coefficient of the 30-item scale was excellent with the value of 0.936. Moreover, none of the items would increase the coefficient alpha by 0.1 or more when deleted. However, Item 20 had a very low communality calculated with squared multiple correlations (R2 ) which refers to how much variance it shares with the other variables. Since low communalities imply that items are not related to the domain of interest, it was decided to delete the item from the scale before proceeding with the factor extraction procedure [11]. Before conducting an exploratory factor analysis (EFA), it was determined whether a Pearson productmoment correlation matrix of the data gathered through the questionnaire was adequate for EFA. The KMO statistics (0.942) falls into the excellent range, which means that the sample size (550) is sufficient relative to the number of items. Bartlett’s test of sphericity is also highly significant (χ2 = 8687.358, df = 435, p = 0.000) confirming that the correlation matrix used in the present study is not an identity matrix. The MSA values of all items exceeded 0.600 with most of them greater than 0.900, and this suggests that the correlations among individual items are strong and the correlation matrix is factorable. To identify the internal factor structure of the L2 reading motivation, the Principal Axis Factoring (PAF) was performed. The number of initial factors that correspond to the dimensions of a target construct was
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H.-J. Kim and S. Choi / Psychometric properties and factor structure of an L2 reading motivation questionnaire Table 3 Results of parallel analysis∗ Factors 1 2 3 4 5 6 7 8 9 10 ∗
Actual data eigenvalue 10.546525 2.490703 1.052430 0.688934 0.549239 0.456831 0.277051 0.205420 0.193855 0.161748
Mean random order eigenvalue 0.505786 0.443865 0.395517 0.354355 0.316727 0.282351 0.248689 0.218130 0.189718 0.161708
95th percentile value 0.570757 0.497745 0.436910 0.393672 0.353789 0.316735 0.278168 0.247990 0.218156 0.189219
Factors 11 through 29 were removed due to the space limit and their statistical insignificance.
Fig. 1. The scree plot.
decided based on results of the scree test and parallel analysis using eigenvalues from the reproduced correlation matrix. In addition, the residual correlation matrix was also inspected. 4. Factor extraction 4.1. The scree test A scree plot maps extracted factors against their eigenvalues in descending order. Figure 1 presents the scree plot associated with the analysis of the data for 29 items measuring L2 reading motivation. As can be seen, there are two significant break points, one at Factor 3 and the other at Factor 6. Therefore, it is sensible to examine the rotated solutions with three to six extracted factors and see which solution yields the most interpretable and theoretically sensible pattern of results.
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4.2. Parallel analysis Parallel analysis is considered as one of the best methods in determining the number of factors for extraction. The appropriate number of common factors is the number of eigenvalues from the actual data that are larger than their corresponding eigenvalues from randomly ordered data. The results in Table 3 reveal that the eigenvalues from the original dataset from Factor 1 through Factor 6 exceed those from both mean and 95th percentile values from the random dataset. 4.3. Residual correlation matrix Based on the results of the scree test and parallel analysis along with theoretical consideration, it was decided that three, four, five, or six factors could be retained. Accordingly, three- through six-factor solutions were examined. When six factors are extracted, Factors 4, 5, and 6 have only one or two salient factor loadings. As for the four- and five-factor solutions, every factor has more than three items loaded on. However, the five-factor solution was not clear enough to have a high level of interpretability because the items loaded on the factors do not seem to represent one consistent dimension. In the threefactor solution, there were salient loadings on every factor. However, this initial solution did not seem appropriate for the following two reasons: the extracted three factors accounted only for 48.261% of the cumulated common variance and there were a significantly large number of non-redundant residuals with absolute values great than 0.05. There were 70 of them in total (17%), which indicates that there may be more factors remaining to be extracted. All things being considered, the four-factor initial solution was selected for the further analyses. 5. Factor rotation In order to increase the interpretability of the factor structure, promax rotation, a commonly used oblique rotation method, was applied to the initial solution. Oblique rotation was chosen because the latent factors of reading motivation were known to be correlated with each other [12,13]. When promax rotation was conducted, four items (4, 10, 6, 22) loaded weakly on the identified rotated factors (i.e., loadings less than |0.35|) and thus were eliminated from the scale. Afterwards a new factor solution with 25 items was subjected to promax rotation again. The results indicated that the four-factor solution explained 52.97% of the total variance. As can be seen in Table 4, the rotated factor loadings display a simple and clear pattern with most items loading bigger than |0.4| on only one factor, and thus each factor was easily interpretable and psychologically meaningful. The reliability of the new 25-item scale was high with the Cronbach’s alpha coefficient of 0.930 and for each subscale the alpha values ranged from 0.817 to 0.887, which means that the items making up the subscales fit together. None of the items improved the Cronbach’s alpha values of the subscales when deleted, and the item-total correlation ranged from 0.443 to 0.745. This suggests that all the subscales consist of relatively consistent items. 5.1. Factors The first factor named ‘Cultural and Intellectual Orientation’ inlucded seven items. This factor represents extrinsic sources of motivation to read in L2 and indicates one’s desire to become more knowledgeable and intercultural by being able to read in English. It accounts for about 36% of total variance,
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H.-J. Kim and S. Choi / Psychometric properties and factor structure of an L2 reading motivation questionnaire Table 4 Factor pattern matrix
Factor∗ Factor 1 Intercultural and intellectual orientation (α = 0.871) 26. Learning to read in English is important because it will make me a more knowledgeable person. 24. Learning to read in English is important because it will broaden my view. 25. By learning to read in English, I hope to search information on the Internet. 19. By learning to read in English, I hope to learn about various opinions in the world. 18. Learning to read in English is important because it will be conducive to my general education. 5. By being able to read in English, I hope to understand more deeply about lifestyles and cultures of English speaking countries. 3. Learning to read in English is important in that we need to cope with internationalization. Factor 2– Reading efficacy (α = 0.826) 11. I am good at reading in English. 17. English reading is my weak subject. 21. My grades for English reading classes at junior schools were not good. 8. Long and difficult English passages put me off. 29. I tend to get deeply engaged when I read in English.
1
2
3
4
h2
0.859
0.047 −0.117
0.815 0.740 0.719
0.038 −0.070 −0.030 0.636 0.002 0.063 0.074 0.558 0.032 0.100 −0.004 0.642
0.008 0.633
0.611 −0.040 −0.036 −0.056 0.369 0.461 −0.136
0.356 −0.019 0.491
0.408 −0.070
0.114 −0.152 0.311
0.124 0.929 0.055 −0.844 0.101 −0.739 0.140 −0.518 0.243 0.393
0.032 0.210 0.108 0.044 0.131 0.112 0.056 0.316 0.096 −0.007
Factor 3– Intrinsic motivation (α = 0.887) 1. By learning to read in English, I hope I will be able to read English novels. 0.031 −0.299 14. By learning to read in English, I hope to read English newspapers and/or 0.165 −0.187 magazines. 12. I like reading English novels. −0.104 0.218 16. I like reading English newspapers and/or magazines. 0.048 0.213 2. I get immersed in interesting stories even if they are written in English. −0.084 0.016 15. It is fun to read in English. 0.126 0.250 13. I liked reading classes at junior high school and still do now. 0.063 0.390
0.782 0.636 0.510 0.441 0.358
0.779 −0.085 0.503 0.764 −0.017 0.638 0.746 0.046 0.652 0.659 0.107 0.597 0.626 −0.131 0.449 0.531 −0.037 0.658 0.443 −0.018 0.624
Factor 4– Negative attitude (α = 0.814) 28. I would not voluntarily read in English unless it is required as homework or 0.018 −0.046 −0.079 assignment. 27. It is a waste of time to learn to read in English. −0.345 0.079 0.183 9. I am taking a reading class merely because it is a required subject. −0.069 −0.154 −0.034 7. I am learning to read in English merely because I would like to get good 0.036 0.022 −0.098 grades. 30. It is a pain to read in English. −0.039 −0.338 −0.036 23. I do not have any desire to read in English even if the content is interesting. 0.084 −0.132 −0.251
0.452 0.548 0.409 0.397
Eigenvalues Variance explained (%)
0.684 Total 2.74 52.97
9.11 36.43
2.43 9.73
1.02 4.07
0.668 0.536 0.615 0.493 0.555 0.506 0.504 0.283
∗
Extraction Method: Principal Axis Factoring/Rotation Method: Promax with Kaiser Normalization. a Rotation converged in 7 iterations.
which suggests that Korean high school students are more likely to read in English when they recognize the cultural and intellectual values associated with reading in English. The second extracted factor was labeled ‘Reading Efficacy’. It contains three of the original reading efficacy items, one item from reading avoidance, and one from reading involvement. The third factor which consists of seven items is named ‘Intrinsic Motivation’. Intrinsic motivation refers to the enjoyment and satisfaction that one feels while performing an inherently interesting activity [14]. Six items that loaded on the fourth factor were mainly from reading compliance, reading for grades, importance of reading in English, and reading avoidance. Despite the diversity of their origins, all items have one thing in common. They all reflect one’s negative
H.-J. Kim and S. Choi / Psychometric properties and factor structure of an L2 reading motivation questionnaire
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Table 5 Intercorrelations between L2 reading motivation scales Factor 1 2 3 4
1 1.000 0.287 0.646 −0.499
2
3
4
1.000 0.588 −0.549
1.000 −0.546
1.000
attitude towards reading in English, attaching no or little value to the task; hence the name ‘Negative Attitude’ was assigned. 5.2. Interfactor correlations Composite factor scores were generated by summing the scores of the items that have loaded on a given factor in order to obtain subscale reliabilities and interfactor correlations [15]. Table 5 presents the factor correlation matrix, which gives important information about the degree to which the factors are correlated with one another. The substantial correlation between Factors 1 and 3 might imply that people who have inherent interest in reading in English also believe that their ability to read in English will help them become well-educated and broaden their perspectives. Similarly, the high correlation between Factors 2 and 3 suggest that intrinsically motivated people have positive perceptions of their competence. The negative correlations between Factor 4 and the other factors also clearly indicate that the domain of negative attitudes toward reading in English will have adverse relationship with the other dimensions of L2 reading motivation. 6. Discussion and implications Based on the results presented so far, one might conclude that one’s motivation to read in a second language is composed of four dimensions, and it is legitimate to use Mori’s questionnaire without much revision. Yet, drawing such conclusion seems hasty and unwarranted since Mori’s questionnaire has not proven yet whether it accurately taps into every aspect of L2 reading motivation. Several reasons can be provided. First, ambiguous wording found in some of the items could mislead respondents. The presence of the items with negative adverbs and conjunctions in the subscales may have affected the performance of these items. In addition, some of Mori’s original subscales have only three or less items. EFA performs better when each factor has multiple measured variables. It is suggested that five are more measured variables reflecting each factor should be included [4,16]. The final reason is related to the current practice of exploratory factory analysis in L2 reading motivation. The majority of L2 reading motivation studies applied principal component analysis with orthogonal rotation. However, the purpose of principal component analysis is to summarize dataset, not to discover underlying latent variables. The orthogonal rotation technique such as varimax does not allow correlations among the factors, which is highly unrealistic in social science research. Such practice could have resulted in identification of unreliable factor structures in different studies. 7. Conclusion The present study conducted an exploratory factor analysis on English reading motivation of 550 Korean high school students. The data were collected using an L2 reading motivation questionnaire developed by Mori [3]. The results provided support for Mori’s original proposal that one’s motivation
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H.-J. Kim and S. Choi / Psychometric properties and factor structure of an L2 reading motivation questionnaire
to read in a second language is affected by several latent factors, that is, the multidimensionality of L2 reading motivation. However, the present study failed to replicate the nine-factor structure proposed by Mori. It obtained a four-factor structure instead: Intercultural and Intellectual Orientation, Reading Efficacy, Intrinsic Motivation, and Negative Attitudes. The lack of evidence for the proposed nine-factor structure for L2 reading motivation necessitates further understanding of the latent variables affecting L2 reading motivation and revision of the L2 reading motivation questionnaire. With relation to this, more research is needed to examine the relationship between L2 reading motivation and different psychological variables which might be related such as learners’ perceptions of English itself (interest, dislike, learning motivation etc.), which will ensure the validity and utility of the scale. Additionally, factor analyzing practices entailing several critical decision makings should be improved. Any results gained from the studies using a scale with questionable properties and adopting wrong statistical analysis processes could misinform both researchers and educators leading to ill-formed theoretical reasoning and pedagogical practices. As an alternative, a new scale can be developed as well by incorporating a broader range of theoretical and empirical studies related not only to reading but also to L2 learning in general. A new scale should be validated with both exploratory and confirmatory factor analysis and should be able to measure L2 reading motivation of different subject groups in different contexts. References [1]
Nuttall C. Teaching reading skills in a foreign language. Macmillan Education. Oxford, UK: Oxford University Press, 2005. [2] Guthrie JT, Wigfield A, VonSecker C. Effects of integrated instruction on motivation and strategy use in reading. Journal of Educational Psychology. 2000; 92: 331-341. [3] Mori S. Redefining motivation to read in a foreign language. Reading in a Foreign Language. 2002; 14: 91-110. [4] Fabrigar LR, Wegener DT. Exploratory factor analysis. New York: Oxford University Press, 2012. [5] Baker L, Wigfield A. Dimensions of children’s motivation for reading and their relations to reading activity and reading achievement. Reading Research Quarterly. 1999; 34: 452-477. [6] Wigfield A, Guthrie JT. Relations of children’s motivation for reading to the amount and breadth of their reading. Journal of Educational Psychology. 1997; 89: 420-432. [7] Wigfield A, Guthrie JT, McGough K. A questionnaire measure of children’s motivations for reading (Instructional Resource No. 22). Athens (GA): National Reading Research Center, 1996. [8] Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ. Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods. 1999; 4: 272-299. [9] Pett MA, Lackey NR, Sullivan JJ. Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks (CA): Sage Publications, 2003. [10] Thompson B. Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington DC: American Psychological Association, 2004. [11] DeVellis RF. Scale development: Theory and applications. 3rd ed. Thousand Oaks (CA): Sage Publications, 2011. [12] Wigfield A, Guthrie JT, Tonks S, Perencevich KC. Children’s motivation for reading: Domain specificity and instructional influences. The Journal of Educational Research. 2004; 97: 299-309. [13] Jung SH. University students’ motivation to read in English as a foreign language. Foreign Languages Education. 2008; 15: 219-241. [14] Noel KA. The internalization of language learning into the self and social identity. In: Dörnyei Z, Ushioda E, editors. Motivation, language identity, and the L2 self. Bristol, UK: Multilingual Matters, 2009. [15] Pedhazur, E. J., & Schmelkin, LP. Measurement, design, and analysis: An integrated approach. Hillsdale (NJ): Lawrence Erlbaum Associates, 1991. [16] Comrey AL, Lee HB. A first course in factor analysis. 2nd ed. Hillsdale (NJ): Erlbaum, 1992.
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379
Psychometric properties and factor structure of an L2 reading motivation questionnaire Hee-jung Kima and Sunhee Choib,∗
a Department
b Department
of English, Hanbyeol High School, Wanju, Korea of English Education, Jeonju University, Jeonju, Korea
Received 28 October 2013 Accepted 23 January 2014 Abstract. The purpose of this study is to investigate the psychometric properties and factor structure of a popular second language reading motivation questionnaire developed by Mori (2002). 550 first year high school students in Korea answered the 30-item questionnaire which consists of statements indicating different degrees of English reading motivation. Exploratory factor analysis was conducted with principal axis factoring and promax rotation, which yielded a four-factor solution. The factors included ‘Intercultural and Intellectual Orientation’, ‘Reading Efficacy’, ‘Intrinsic Motivation’, and ‘Negative Attitudes’. The results supported the multidimensionality of the construct of L2 reading motivation, but could not replicate the nine factor structure which was originally proposed by Mori. The implications for further research on L2 reading motivation and development of a more valid L2 reading motivation scale are discussed. Keywords: Reading motivation, second language learning, factor analysis, scale development
1. Introduction Second language (L2) reading is a crucial component of L2 learning and mastering L2 reading skill involves a variety of skills, knowledge, and strategies [1]. Yet, one of the most essential elements for successful L2 reading is one’s active involvement in the reading task propelled by his or her motivation to read in L2 [2]. The purpose of this study is to identify the internal factor structure of L2 reading motivation and to discover the psychometric properties of the items included in a commonly used L2 reading motivation scale. In order to achieve this goal, an L2 reading motivation questionnaire developed by Mori [3] was used to measure English reading motivation of Korean high school students and the measures were factor analyzed. The information and insights gained from the study will help identify the items of the L2 reading motivation questionnaire that are pure measures of the target construct, and calibrate its subscales by distinguishing good items from not so good ones [4]. ∗ Corresponding author: Sunhee Choi, Department of English Education, Jeonju University, 303 Cheonjam-ro, Jeonju, 560759, Korea. E-mail: [email protected].
c 2014 – IOS Press and the authors. All rights reserved 0928-7329/14/$27.50
380
H.-J. Kim and S. Choi / Psychometric properties and factor structure of an L2 reading motivation questionnaire Table 1 Motivations for Reading Questionnaire (MRQ)
Categories Self-efficacy
Dimensions Efficacy – Belief that one can be successful at reading Challenge – Willingness to take on difficult reading material Work avoidance – Desire to avoid reading activity Intrinsic motivation and learning goals Curiosity – Desire to read topics of interest Involvement – Enjoyment received from reading Importance – Value placed on reading Extrinsic motivation and performance goals Recognition – Pleasure of receiving a tangible form of recognition for success in reading Grades – Desire for positive school evaluations by teacher Competition – Desire to outperform others in reading Social motivation for reading Social reasons – Sharing meaning gained from reading with others Compliance – Reading to meet others’ expectations
2. Reading motivation Reading motivation is a psychological construct which influences one’s reading behaviors, and it is hypothesized that the construct is multidimensional, composed of a set of latent variables [5]. When we are highly motivated to read, we tend to read more, actively involve ourselves in selecting books, put more efforts in comprehending what we read, and to persist in the face of difficulty [2,6]. Different scholars have proposed different measurement tools to investigate the multidimensional aspects of reading motivation. As for first language (L1) reading motivation, a popular scale, the Motivations for Reading Questionnaire (MRQ), was developed by Wigfield and Guthrie [6] applying several social-psychological motivational theories. The MRQ is made up with three categories and 11 dimensions as illustrated in Table 1 [7]. With regard to L2 reading motivation, Mori [3] proposed a nine-factor model and a corresponding scale which was developed based on MRQ. Mori’s L2 reading motivation questionnaire is a Likert scale with 30 items. It is different from the MRQ in that it excludes the subscales for competition, recognition, and social reasons which Mori believed are irrelevant to EFL college students, the subjects of her own research. Instead, she added seven items to measure one’s integrative orientation, which reflects one’ interest in the people and culture of L2, a unique domain for L2 learning. 3. Methods 3.1. Participants and instrument The participants were 550 10th grade students from two high schools located in Jeonbuk province in Korea. The sample consisted of 264 female (48%) and 286 male students (52%). For the study, Mori’s L2 reading motivation questionnaire was adopted. The questionnaire is a 30-item self-report measure which asks respondents to indicate the extent of their agreement with each item on a five-point Likert scale ranging from strongly disagree (1) to strongly agree (5). 3.2. Statistical analysis Exploratory Factor Analysis (EFA) was conducted with SPSS 18.0 following the recommendations made by Fabrigar et al. [8] as well as other related research [4,9,10]. Before conducting EFA, the skew-
H.-J. Kim and S. Choi / Psychometric properties and factor structure of an L2 reading motivation questionnaire
381
Table 2 Descriptive statistics and reliability analysis Items
Mean
SD
Skewness
Kurtosis
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
3.51 3.38 3.93 2.93 3.36 3.03 2.92 3.06 2.88 2.90 2.57 2.52 2.62 3.26 2.81 2.56 3.29 3.67 3.34 3.54 2.95 2.61 2.61 3.36 3.17 3.28 2.10 2.74 2.86 2.70
1.185 1.127 0.979 1.111 1.149 1.068 0.973 1.013 0.998 1.048 0.959 0.978 1.025 1.169 1.001 0.989 1.010 0.883 0.972 1.003 1.088 0.938 1.003 0.899 1.018 0.969 0.883 0.952 0.860 1.002
−0.604 −0.465 −0.916 0.084 −0.295 −0.099 0.105 −0.092 0.148 0.112 0.187 0.228 0.175 −0.381 −0.077 0.259 −0.133 −0.634 −0.262 −0.290 0.167 0.202 0.470 −0.338 −0.195 −0.335 0.767 0.348 −0.012 0.367
−0.447 −0.531 0.644 −0.663 −0.671 −0.648 −0.589 −0.640 −0.529 −0.540 −0.280 −0.338 −0.423 −0.732 −0.381 −0.321 −0.469 0.632 −0.165 −0.331 −0.677 −0.158 −0.056 0.229 −0.484 −0.211 0.578 −0.086 0.033 −0.277
Corrected item-total correlation 0.539 0.587 0.488 0.498 0.591 0.631 0.416 0.436 0.606 0.567 0.606 0.678 0.730 0.664 0.757 0.677 0.466 0.448 0.671 0.068 0.391 0.617 0.547 0.606 0.550 0.571 0.495 0.570 0.560 0.625
α if item deleted 0.934 0.933 0.935 0.935 0.933 0.933 0.935 0.935 0.933 0.934 0.933 0.932 0.932 0.932 0.931 0.932 0.935 0.935 0.932 0.939 0.936 0.933 0.934 0.933 0.934 0.934 0.934 0.934 0.934 0.933
Squared multiple correlation (R2 ) 0.502 0.484 0.424 0.340 0.542 0.511 0.342 0.449 0.542 0.396 0.683 0.634 0.659 0.606 0.660 0.616 0.620 0.416 0.641 0.091 0.517 0.519 0.449 0.600 0.548 0.601 0.459 0.481 0.412 0.548
ness and kurtosis tests were executed on the items to examine the distribution of the data. As shown in Table 2, the data are normally distributed and therefore are acceptable for further analyses. Next, the reliability of the initial scale was assessed by calculating the internal consistency of the items. The Cronbach’s alpha coefficient of the 30-item scale was excellent with the value of 0.936. Moreover, none of the items would increase the coefficient alpha by 0.1 or more when deleted. However, Item 20 had a very low communality calculated with squared multiple correlations (R2 ) which refers to how much variance it shares with the other variables. Since low communalities imply that items are not related to the domain of interest, it was decided to delete the item from the scale before proceeding with the factor extraction procedure [11]. Before conducting an exploratory factor analysis (EFA), it was determined whether a Pearson productmoment correlation matrix of the data gathered through the questionnaire was adequate for EFA. The KMO statistics (0.942) falls into the excellent range, which means that the sample size (550) is sufficient relative to the number of items. Bartlett’s test of sphericity is also highly significant (χ2 = 8687.358, df = 435, p = 0.000) confirming that the correlation matrix used in the present study is not an identity matrix. The MSA values of all items exceeded 0.600 with most of them greater than 0.900, and this suggests that the correlations among individual items are strong and the correlation matrix is factorable. To identify the internal factor structure of the L2 reading motivation, the Principal Axis Factoring (PAF) was performed. The number of initial factors that correspond to the dimensions of a target construct was
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H.-J. Kim and S. Choi / Psychometric properties and factor structure of an L2 reading motivation questionnaire Table 3 Results of parallel analysis∗ Factors 1 2 3 4 5 6 7 8 9 10 ∗
Actual data eigenvalue 10.546525 2.490703 1.052430 0.688934 0.549239 0.456831 0.277051 0.205420 0.193855 0.161748
Mean random order eigenvalue 0.505786 0.443865 0.395517 0.354355 0.316727 0.282351 0.248689 0.218130 0.189718 0.161708
95th percentile value 0.570757 0.497745 0.436910 0.393672 0.353789 0.316735 0.278168 0.247990 0.218156 0.189219
Factors 11 through 29 were removed due to the space limit and their statistical insignificance.
Fig. 1. The scree plot.
decided based on results of the scree test and parallel analysis using eigenvalues from the reproduced correlation matrix. In addition, the residual correlation matrix was also inspected. 4. Factor extraction 4.1. The scree test A scree plot maps extracted factors against their eigenvalues in descending order. Figure 1 presents the scree plot associated with the analysis of the data for 29 items measuring L2 reading motivation. As can be seen, there are two significant break points, one at Factor 3 and the other at Factor 6. Therefore, it is sensible to examine the rotated solutions with three to six extracted factors and see which solution yields the most interpretable and theoretically sensible pattern of results.
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4.2. Parallel analysis Parallel analysis is considered as one of the best methods in determining the number of factors for extraction. The appropriate number of common factors is the number of eigenvalues from the actual data that are larger than their corresponding eigenvalues from randomly ordered data. The results in Table 3 reveal that the eigenvalues from the original dataset from Factor 1 through Factor 6 exceed those from both mean and 95th percentile values from the random dataset. 4.3. Residual correlation matrix Based on the results of the scree test and parallel analysis along with theoretical consideration, it was decided that three, four, five, or six factors could be retained. Accordingly, three- through six-factor solutions were examined. When six factors are extracted, Factors 4, 5, and 6 have only one or two salient factor loadings. As for the four- and five-factor solutions, every factor has more than three items loaded on. However, the five-factor solution was not clear enough to have a high level of interpretability because the items loaded on the factors do not seem to represent one consistent dimension. In the threefactor solution, there were salient loadings on every factor. However, this initial solution did not seem appropriate for the following two reasons: the extracted three factors accounted only for 48.261% of the cumulated common variance and there were a significantly large number of non-redundant residuals with absolute values great than 0.05. There were 70 of them in total (17%), which indicates that there may be more factors remaining to be extracted. All things being considered, the four-factor initial solution was selected for the further analyses. 5. Factor rotation In order to increase the interpretability of the factor structure, promax rotation, a commonly used oblique rotation method, was applied to the initial solution. Oblique rotation was chosen because the latent factors of reading motivation were known to be correlated with each other [12,13]. When promax rotation was conducted, four items (4, 10, 6, 22) loaded weakly on the identified rotated factors (i.e., loadings less than |0.35|) and thus were eliminated from the scale. Afterwards a new factor solution with 25 items was subjected to promax rotation again. The results indicated that the four-factor solution explained 52.97% of the total variance. As can be seen in Table 4, the rotated factor loadings display a simple and clear pattern with most items loading bigger than |0.4| on only one factor, and thus each factor was easily interpretable and psychologically meaningful. The reliability of the new 25-item scale was high with the Cronbach’s alpha coefficient of 0.930 and for each subscale the alpha values ranged from 0.817 to 0.887, which means that the items making up the subscales fit together. None of the items improved the Cronbach’s alpha values of the subscales when deleted, and the item-total correlation ranged from 0.443 to 0.745. This suggests that all the subscales consist of relatively consistent items. 5.1. Factors The first factor named ‘Cultural and Intellectual Orientation’ inlucded seven items. This factor represents extrinsic sources of motivation to read in L2 and indicates one’s desire to become more knowledgeable and intercultural by being able to read in English. It accounts for about 36% of total variance,
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H.-J. Kim and S. Choi / Psychometric properties and factor structure of an L2 reading motivation questionnaire Table 4 Factor pattern matrix
Factor∗ Factor 1 Intercultural and intellectual orientation (α = 0.871) 26. Learning to read in English is important because it will make me a more knowledgeable person. 24. Learning to read in English is important because it will broaden my view. 25. By learning to read in English, I hope to search information on the Internet. 19. By learning to read in English, I hope to learn about various opinions in the world. 18. Learning to read in English is important because it will be conducive to my general education. 5. By being able to read in English, I hope to understand more deeply about lifestyles and cultures of English speaking countries. 3. Learning to read in English is important in that we need to cope with internationalization. Factor 2– Reading efficacy (α = 0.826) 11. I am good at reading in English. 17. English reading is my weak subject. 21. My grades for English reading classes at junior schools were not good. 8. Long and difficult English passages put me off. 29. I tend to get deeply engaged when I read in English.
1
2
3
4
h2
0.859
0.047 −0.117
0.815 0.740 0.719
0.038 −0.070 −0.030 0.636 0.002 0.063 0.074 0.558 0.032 0.100 −0.004 0.642
0.008 0.633
0.611 −0.040 −0.036 −0.056 0.369 0.461 −0.136
0.356 −0.019 0.491
0.408 −0.070
0.114 −0.152 0.311
0.124 0.929 0.055 −0.844 0.101 −0.739 0.140 −0.518 0.243 0.393
0.032 0.210 0.108 0.044 0.131 0.112 0.056 0.316 0.096 −0.007
Factor 3– Intrinsic motivation (α = 0.887) 1. By learning to read in English, I hope I will be able to read English novels. 0.031 −0.299 14. By learning to read in English, I hope to read English newspapers and/or 0.165 −0.187 magazines. 12. I like reading English novels. −0.104 0.218 16. I like reading English newspapers and/or magazines. 0.048 0.213 2. I get immersed in interesting stories even if they are written in English. −0.084 0.016 15. It is fun to read in English. 0.126 0.250 13. I liked reading classes at junior high school and still do now. 0.063 0.390
0.782 0.636 0.510 0.441 0.358
0.779 −0.085 0.503 0.764 −0.017 0.638 0.746 0.046 0.652 0.659 0.107 0.597 0.626 −0.131 0.449 0.531 −0.037 0.658 0.443 −0.018 0.624
Factor 4– Negative attitude (α = 0.814) 28. I would not voluntarily read in English unless it is required as homework or 0.018 −0.046 −0.079 assignment. 27. It is a waste of time to learn to read in English. −0.345 0.079 0.183 9. I am taking a reading class merely because it is a required subject. −0.069 −0.154 −0.034 7. I am learning to read in English merely because I would like to get good 0.036 0.022 −0.098 grades. 30. It is a pain to read in English. −0.039 −0.338 −0.036 23. I do not have any desire to read in English even if the content is interesting. 0.084 −0.132 −0.251
0.452 0.548 0.409 0.397
Eigenvalues Variance explained (%)
0.684 Total 2.74 52.97
9.11 36.43
2.43 9.73
1.02 4.07
0.668 0.536 0.615 0.493 0.555 0.506 0.504 0.283
∗
Extraction Method: Principal Axis Factoring/Rotation Method: Promax with Kaiser Normalization. a Rotation converged in 7 iterations.
which suggests that Korean high school students are more likely to read in English when they recognize the cultural and intellectual values associated with reading in English. The second extracted factor was labeled ‘Reading Efficacy’. It contains three of the original reading efficacy items, one item from reading avoidance, and one from reading involvement. The third factor which consists of seven items is named ‘Intrinsic Motivation’. Intrinsic motivation refers to the enjoyment and satisfaction that one feels while performing an inherently interesting activity [14]. Six items that loaded on the fourth factor were mainly from reading compliance, reading for grades, importance of reading in English, and reading avoidance. Despite the diversity of their origins, all items have one thing in common. They all reflect one’s negative
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Table 5 Intercorrelations between L2 reading motivation scales Factor 1 2 3 4
1 1.000 0.287 0.646 −0.499
2
3
4
1.000 0.588 −0.549
1.000 −0.546
1.000
attitude towards reading in English, attaching no or little value to the task; hence the name ‘Negative Attitude’ was assigned. 5.2. Interfactor correlations Composite factor scores were generated by summing the scores of the items that have loaded on a given factor in order to obtain subscale reliabilities and interfactor correlations [15]. Table 5 presents the factor correlation matrix, which gives important information about the degree to which the factors are correlated with one another. The substantial correlation between Factors 1 and 3 might imply that people who have inherent interest in reading in English also believe that their ability to read in English will help them become well-educated and broaden their perspectives. Similarly, the high correlation between Factors 2 and 3 suggest that intrinsically motivated people have positive perceptions of their competence. The negative correlations between Factor 4 and the other factors also clearly indicate that the domain of negative attitudes toward reading in English will have adverse relationship with the other dimensions of L2 reading motivation. 6. Discussion and implications Based on the results presented so far, one might conclude that one’s motivation to read in a second language is composed of four dimensions, and it is legitimate to use Mori’s questionnaire without much revision. Yet, drawing such conclusion seems hasty and unwarranted since Mori’s questionnaire has not proven yet whether it accurately taps into every aspect of L2 reading motivation. Several reasons can be provided. First, ambiguous wording found in some of the items could mislead respondents. The presence of the items with negative adverbs and conjunctions in the subscales may have affected the performance of these items. In addition, some of Mori’s original subscales have only three or less items. EFA performs better when each factor has multiple measured variables. It is suggested that five are more measured variables reflecting each factor should be included [4,16]. The final reason is related to the current practice of exploratory factory analysis in L2 reading motivation. The majority of L2 reading motivation studies applied principal component analysis with orthogonal rotation. However, the purpose of principal component analysis is to summarize dataset, not to discover underlying latent variables. The orthogonal rotation technique such as varimax does not allow correlations among the factors, which is highly unrealistic in social science research. Such practice could have resulted in identification of unreliable factor structures in different studies. 7. Conclusion The present study conducted an exploratory factor analysis on English reading motivation of 550 Korean high school students. The data were collected using an L2 reading motivation questionnaire developed by Mori [3]. The results provided support for Mori’s original proposal that one’s motivation
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to read in a second language is affected by several latent factors, that is, the multidimensionality of L2 reading motivation. However, the present study failed to replicate the nine-factor structure proposed by Mori. It obtained a four-factor structure instead: Intercultural and Intellectual Orientation, Reading Efficacy, Intrinsic Motivation, and Negative Attitudes. The lack of evidence for the proposed nine-factor structure for L2 reading motivation necessitates further understanding of the latent variables affecting L2 reading motivation and revision of the L2 reading motivation questionnaire. With relation to this, more research is needed to examine the relationship between L2 reading motivation and different psychological variables which might be related such as learners’ perceptions of English itself (interest, dislike, learning motivation etc.), which will ensure the validity and utility of the scale. Additionally, factor analyzing practices entailing several critical decision makings should be improved. Any results gained from the studies using a scale with questionable properties and adopting wrong statistical analysis processes could misinform both researchers and educators leading to ill-formed theoretical reasoning and pedagogical practices. As an alternative, a new scale can be developed as well by incorporating a broader range of theoretical and empirical studies related not only to reading but also to L2 learning in general. A new scale should be validated with both exploratory and confirmatory factor analysis and should be able to measure L2 reading motivation of different subject groups in different contexts. References [1]
Nuttall C. Teaching reading skills in a foreign language. Macmillan Education. Oxford, UK: Oxford University Press, 2005. [2] Guthrie JT, Wigfield A, VonSecker C. Effects of integrated instruction on motivation and strategy use in reading. Journal of Educational Psychology. 2000; 92: 331-341. [3] Mori S. Redefining motivation to read in a foreign language. Reading in a Foreign Language. 2002; 14: 91-110. [4] Fabrigar LR, Wegener DT. Exploratory factor analysis. New York: Oxford University Press, 2012. [5] Baker L, Wigfield A. Dimensions of children’s motivation for reading and their relations to reading activity and reading achievement. Reading Research Quarterly. 1999; 34: 452-477. [6] Wigfield A, Guthrie JT. Relations of children’s motivation for reading to the amount and breadth of their reading. Journal of Educational Psychology. 1997; 89: 420-432. [7] Wigfield A, Guthrie JT, McGough K. A questionnaire measure of children’s motivations for reading (Instructional Resource No. 22). Athens (GA): National Reading Research Center, 1996. [8] Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ. Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods. 1999; 4: 272-299. [9] Pett MA, Lackey NR, Sullivan JJ. Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks (CA): Sage Publications, 2003. [10] Thompson B. Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington DC: American Psychological Association, 2004. [11] DeVellis RF. Scale development: Theory and applications. 3rd ed. Thousand Oaks (CA): Sage Publications, 2011. [12] Wigfield A, Guthrie JT, Tonks S, Perencevich KC. Children’s motivation for reading: Domain specificity and instructional influences. The Journal of Educational Research. 2004; 97: 299-309. [13] Jung SH. University students’ motivation to read in English as a foreign language. Foreign Languages Education. 2008; 15: 219-241. [14] Noel KA. The internalization of language learning into the self and social identity. In: Dörnyei Z, Ushioda E, editors. Motivation, language identity, and the L2 self. Bristol, UK: Multilingual Matters, 2009. [15] Pedhazur, E. J., & Schmelkin, LP. Measurement, design, and analysis: An integrated approach. Hillsdale (NJ): Lawrence Erlbaum Associates, 1991. [16] Comrey AL, Lee HB. A first course in factor analysis. 2nd ed. Hillsdale (NJ): Erlbaum, 1992.
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