Regis
Komperda
,
Kathryn N.
Hosbein
and
Jack
Barbera
*
Department of Chemistry, Portland State University, Portland, OR, USA. E-mail: jbarbera@pdx.edu
First published on 25th October 2017
Increased understanding of the importance of the affective domain in chemistry education research has led to the development and adaptation of instruments to measure chemistry-specific affective traits, including motivation. Many of these instruments are adapted from other fields by using the word ‘chemistry’ in place of other disciplines or more general ‘science’ wording. Psychometric evidence is then provided for the functioning of the new adapted instrument. When an instrument is adapted from general language to specific (e.g. replacing ‘science’ with ‘chemistry’), an opportunity exists to compare the functioning of the original instrument in the same context as the adapted instrument. This information is important for understanding which types of modifications may have small or large impacts on instrument functioning and in which contexts these modifications may have more or less influence. In this study, data were collected from the online administration of scales from two science motivation instruments in chemistry courses for science majors and for non-science majors. Participants in each course were randomly assigned to view either the science version or chemistry version of the items. Response patterns indicated that students respond differently to different wordings of the items, with generally more favorable response to the science wording of items. Confirmatory factor analysis was used to investigate the internal structure of each instrument, however acceptable data-model fit was not obtained under any administration conditions. Additionally, no discernable pattern could be detected regarding the conditions showing better data-model fit. These results suggest that even seemingly small changes to item wording and administration context can affect instrument functioning, especially if the change in wording affects the construct measured by the instrument. This research further supports the need to provide psychometric evidence of instrument functioning each time an instrument is used and before any comparisons are made of responses to different versions of the instrument.
These chemistry-specific motivation instruments can be broadly classified into two types of development processes. In the first type, the items are written explicitly for the instrument and are often developed based on a specific theoretical framework. This was the process used for development of the College Chemistry Self-Efficacy Scale (CCSS; Uzuntiryaki and Aydin, 2009) using Bandura's (1993) social-cognitive theory. More frequently, chemistry-specific motivation scales or entire instruments are developed by adapting existing scales from other research areas and replacing terms, such as ‘science’, ‘math’, or ‘psychology’, with ‘chemistry’. This process was used to develop the Chemistry Self-Concept Inventory (CSCI; Bauer, 2005), the Academic Motivation Scale – Chemistry (Liu et al., 2017), the Chemistry Motivation Questionnaire (Salta and Koulougliotis, 2015; Hibbard et al., 2016), and the chemistry-specific interest and effort belief scales (Ferrell and Barbera, 2015).
When adapting an existing instrument or individual scales by changing item wordings or using items in a context other than the one in which they were originally developed and tested, it is necessary for the adapted scales to undergo psychometric evaluation to demonstrate that the new chemistry-specific versions are functioning acceptably and evidence should be provided to show that the data obtained from the instrument can be considered valid and reliable (AERA, APA and NCME, 2014; Arjoon et al., 2013) and is therefore providing a true measure of the construct of interest. This type of psychometric evidence has generally been provided when adapting existing motivation instruments for use in chemistry education research (CER), but the data collected usually only come from administration of the chemistry-specific version of the instrument in a specific course type with the goal of demonstrating acceptable instrument functioning in that particular context. What is absent from these studies is a broader understanding of how the functioning of the adapted instrument compares to the functioning of the original instrument with the same student population, or how the adapted instrument functions in student populations that may differ from those in which the original instrument was developed. This information could prove useful to others wanting to adapt existing instruments to fit their specific research or instructional goals and then draw conclusions comparing data from the adapted instrument and the original instrument.
The original development and testing of the various forms of the original SMQ and SMQ II occurred with a population of science and non-science majors enrolled in university-level biology courses (Glynn and Koballa, 2006; Glynn et al., 2007, 2009, 2011). Though the administration context was biology-specific, the SMQ II is worded to address science motivation generally; the biology wording of the instrument was not tested. Based on their results, the developers suggested that it should be possible to create discipline-specific versions (replacing the word ‘science’ with ‘biology’, ‘chemistry’, or ‘physics’) which would then need to undergo further psychometric analysis (Glynn et al., 2011).
Two instances of the chemistry-specific versions of the SMQ II have been documented in the CER literature: one with US college students (Hibbard et al., 2016) and the other with Greek public secondary school students (Salta and Koulougliotis, 2015). As part of an evaluation of the effectiveness of a flipped general chemistry course sequence, Hibbard et al. administered the chemistry-worded instrument (CMQ II) to approximately 60 female students. Means, standard deviations, and Cronbach's alpha values were provided for each of the five motivation scales and an overall alpha value for the entire instrument was reported. Other than an extremely small alpha value for the grade motivation scale (0.13), all alpha values were similar to those provided by the instrument developers (0.81–0.92). No information was provided about the factor structure of the instrument.
Salta and Koulougliotis (2015) translated the CMQ II into Greek and administered it to 330 students aged 14–17. Aspects of items addressing labs were removed as laboratory activities were deemed not applicable to the Greek secondary school context. As part of their examination of differences in scale means by gender and age, the authors reported means, standard deviations, and Cronbach's alpha values for each of the five motivation scales. Additionally, the correlated five-factor model of the instrument was tested with confirmatory factor analysis (CFA). Data-model fit for both the entire Greek sample and subsets split by age and gender was similar to data-model fit obtained by the original SMQ II developers using the science wording in college biology courses (Glynn et al., 2011). However, these results still do not provide evidence for the five-factor structure of the CMQ II or of the SMQ II when administered to university-level chemistry students in the US, therefore, the SMQ II was chosen as one of the motivation instruments to be investigated in this study.
The other motivation items used in the present study have not been previously published, and were adapted from educational and psychological research pilot studies with undergraduate students using self-determination theory (SDT; Ryan and Deci, 2000; Skinner et al., 2017) to understand the role of science motivation in academic success. Four of the nine SDT items used in this study are indicators of perceived value while the other five are indicators of belonging. Together the nine items are hypothesized to comprise a correlated two-factor model that SDT describes as promoting internalization, which ultimately sustains intrinsic motivation. These items were selected for inclusion in the current study because they represented a theoretically distinct measure of motivation from the SMQ II and provided an opportunity to examine whether both motivation instruments functioned similarly when their wording was changed from science to chemistry-specific.
All surveys were open for a non-exam week selected by the instructor between the end of October and the beginning of December 2016. When taking the survey, students were randomly presented with either the science or chemistry wording for all 25 motivation items. The motivation items were presented in a randomized order followed by demographic questions.
The surveys for both course sections were open for the last week of class before final exams. As with the SMQ II items, students saw either the science or the chemistry wording of all SDT items and the items were presented in a randomized order. Demographic information for students responding to the SDT items was obtained from university records as part of the approved IRB for the project.
The first step in examining the internal structure of an instrument is considering potential models for the relationships between the individual items and the underlying constructs the items are designed to measure. For both the SMQ II and SDT items, one potential model is a simple single-factor model where all items are expected to be associated with a single motivation construct. However, each set of items was developed from a theoretical framework of motivation to have distinct factors. The SMQ II theoretical framework hypothesizes that the 25 items actually measure five distinct, yet related aspects of motivation: intrinsic motivation, self-determination, self-efficacy, grade motivation, and career motivation. Therefore, this proposed model of the SMQ II items can be tested as a correlated five-factor model. For the SDT items, the theoretical framework hypothesizes two distinct aspects of motivation, value and belonging, which can be tested with a correlated two-factor model. Confirmatory factor analysis (CFA) was used to provide evidence for the internal structure of both the SMQ II and SDT items by testing how well the data fit the proposed models for each set of items. All analyses were done with the R package lavaan (Version 0.5-23.1097; Rosseel, 2012). Each model, for each instrument, was tested for each wording within each course, resulting in a total of 12 CFAs.
When conducting CFA, an estimator must be chosen that matches the characteristics of the data. Most CFA studies in the CER literature use the maximum likelihood (ML) estimator (or its robust variant; MLR) which assumes a continuous response scale (Uzuntiryaki and Aydin, 2009; Brandriet et al., 2011, 2013; Xu and Lewis, 2011; Ferrell and Barbera, 2015; Salta and Koulougliotis, 2015; Lastusaari et al., 2016; Villafañe et al., 2016; Bunce et al., 2017; Liu et al., 2017). However, examination of the response patterns and descriptive statistics (Appendix 1) for the SMQ II and SDT items in this study indicated that the data were highly skewed due to a tendency for students to infrequently select response options on the extreme ends of the response scales. Though data collected on a five-point Likert-type scale is often considered to be continuous, there were many SMQ II and SDT items in which students only used four of the response scale options resulting in data that were more categorical in nature than continuous. As a result of the categorical nature of the data and its non-normal distribution, robust diagonally weighted least squares (WLSMV) was chosen as the estimator for the CFAs.
The determination of acceptable CFA data-model fit is typically evaluated by meeting cutoff values for specific fit indices. Fit indices can be categorized into three classes: incremental, parsimonious, and absolute. It is recommended to evaluate fit indices from more than one class (Mueller and Hancock, 2010). Incremental fit indices such as the comparative fit index (CFI) and the Tucker-Lewis index (TLI), range from 0 to 1 where a larger value indicates that the proposed model is a better fit for the data than a null model with no relationships among the individual items. For parsimonious fit indices, such as the root mean square error of approximation (RMSEA), values closer to 0 are better because they indicate a smaller difference between the observed data covariance matrix and the model-implied covariance matrix, while accounting for the complexity of the model. Absolute fit indices function similarly to parsimonious fit indices, though the standardized root mean square residual (SRMR) will always decrease as more parameters are added to the model, regardless of their usefulness.
The cutoff values for fit indices most frequently cited in the CER literature are based on the work of Hu and Bentler (1999) using ML estimation. Hu and Bentler advise cutoffs near 0.95 for the CFI and TLI, near 0.06 for the RMSEA, and near 0.08 for the SRMR. Studies of the WLSMV estimator (Yu, 2002; Beauducel and Herzberg, 2006; Bandalos, 2008) have indicated that the CFI, TLI, and RMSEA tend to indicate a better-fitting model than may actually exist and advocate for more stringent cutoff criteria, especially when the number of response categories is smaller than four. Therefore, for this study, a value of greater than or equal to 0.95 was chosen as an acceptable cutoff for the CFI and TLI and a value at or below 0.05 was chosen as an acceptable cutoff for the RMSEA. The use of the SRMR is not recommended with the WLSMV estimator and as a result, only the CFI, TLI, and RMSEA values will be used to determine acceptable data model fit for this study. Additionally, because the CFI and TLI are both incremental fit indices while the RMESA is a parsimonious fit index, it was deemed necessary for both types of indices to reach the cutoff values to draw a conclusion of acceptable data-model fit. Chi-square values are reported for comparison purposes but not used as indicators of data-model fit (Schermelleh-Engel et al., 2003; Mueller and Hancock, 2008).
To determine the suitability of either Cronbach's alpha or omega total as an estimate of scale internal consistency, the single-factor CFA models for each scale were tested under both the less restrictive congeneric model, where the relationships between each item and the factor (loadings) are free to take the value that is the best fit for the data, and also under the more restrictive tau-equivalent model, where all item loadings on the factor are forced to be equivalent (Cho and Kim, 2015; Harshman and Stains, 2017; McNeish, 2017). Alpha was used as an internal consistency estimate for a scale if the tau-equivalent model had acceptable data-model fit. Omega total was used as an internal consistency estimate for a scale if the tau-equivalent model had unacceptable data-model fit but the congeneric model had acceptable data-model fit. If neither model had acceptable data-model fit, a scale was determined not to meet the assumptions necessary to report internal consistency, therefore no internal consistency estimates were provided. The R package userfriendlyscience (Version 0.6-1; Peters, 2017) was used for alpha and omega calculations with polychoric correlations to account for the ordinal nature of the response scale (Gadermann et al., 2012).
Course | Wording | SMQ II (N = 660) | SDT (N = 410) |
---|---|---|---|
Note: SDT scales were not administered in introductory courses. | |||
General Chemistry | Science | 146 | 208 |
Chemistry | 141 | 202 | |
Introductory Chemistry | Science | 189 | — |
Chemistry | 184 | — |
Fig. 2 shows the response distributions for all items on the SMQ II. Response distributions in the left column are from students in the general chemistry courses and response distributions in the right column are from students in the introductory chemistry courses.
Response distributions for general chemistry students on the value and belonging SDT items are shown in Fig. 3. Looking first at the results from general chemistry courses, large differences were apparent in the response patterns for the science wording and chemistry wording of items related to some aspects of motivation, such as intrinsic motivation, career motivation, and belonging. In general, when seeing the science wording of the items, the general chemistry students responded in a way that indicates higher levels of those specific motivation aspects, either by choosing a higher frequency response for the SMQ II items (Fig. 2) or by agreeing more with the positively worded SDT items and disagreeing more with the negatively worded SDT items (Fig. 3). Yet for other aspects of motivation, such as self-determination, self-efficacy, grade motivation, and value, the differences in responses to the two wordings for general chemistry students were less pronounced.
The SMQ II response patterns for the introductory chemistry students were generally similar to the general chemistry students in that large differences based on wording were seen in responses to intrinsic and career motivation items where again higher frequency responses were chosen for the science wording as compared to the chemistry wording. Additionally, both groups of students generally selected higher frequency responses for the chemistry wording of the grade motivation items. However, the introductory chemistry students were overall selecting lower frequency responses to the items than the general chemistry students. Another notable difference between the introductory chemistry students and general chemistry students is that the general chemistry students were more likely to select higher frequency responses to the chemistry worded self-determination items whereas the introductory chemistry students were more likely to select higher frequency responses to the science worded self-determination items.
Course | Wording | χ 2 | CFI | TLI | RMSEA |
---|---|---|---|---|---|
Note. For all models df = 275 and p < 0.01. | |||||
General Chemistry | Science | 965 | 0.80 | 0.78 | 0.13 |
General Chemistry | Chemistry | 1577 | 0.75 | 0.72 | 0.18 |
Introductory Chemistry | Science | 1373 | 0.85 | 0.84 | 0.15 |
Introductory Chemistry | Chemistry | 1919 | 0.77 | 0.74 | 0.18 |
Wording | χ 2 | CFI | TLI | RMSEA |
---|---|---|---|---|
Note. For all models df = 27 and p < 0.01. | ||||
Science | 361 | 0.89 | 0.86 | 0.24 |
Chemistry | 438 | 0.84 | 0.78 | 0.28 |
As the single-factor models showed unacceptable fit to the data, multi-factor models corresponding to the theoretical frameworks for the SMQ II and the SDT items were tested. The SMQ II was hypothesized to have an internal structure comprised of five correlated factors representing intrinsic motivation, self-determination, self-efficacy, grade motivation, and career motivation with five items associated with each factor. The SDT items were hypothesized to have an internal structure with two correlated factors representing the value and belonging aspects of SDT with four items and five items associated with each factor, respectively. Again, all multi-factor models were tested for each administration condition.
For both the SMQ II and SDT items, the multi-factor models had better data-model fit than the single-factor models (Tables 4 and 5). However, the data-model fit was unacceptable for all wording conditions and for each course type. In the general chemistry courses, the five-factor model of the SMQ II had worse data-model fit for the science wording responses. For the chemistry wording responses in the general chemistry course the CFI and TLI were above 0.95, but the RMSEA was not below 0.05, indicating unacceptable data-model fit. The opposite pattern of data-model fit was observed for the SMQ II administered in the introductory chemistry courses, though again the models had unacceptable data-model fit in all conditions. In the introductory chemistry courses the science wording responses to the SMQ II met cutoff criteria for the CFI and TLI, but the RMSEA did not meet the cutoff criteria for this study, indicating unacceptable data-model fit.
Course | Wording | χ 2 | CFI | TLI | RMSEA |
---|---|---|---|---|---|
Note. For all models df = 265 and p < 0.01; values within acceptable cutoffs are bolded. | |||||
General Chemistry | Science | 483 | 0.94 | 0.93 | 0.08 |
General Chemistry | Chemistry | 468 | 0.96 | 0.96 | 0.07 |
Introductory Chemistry | Science | 487 | 0.97 | 0.97 | 0.07 |
Introductory Chemistry | Chemistry | 657 | 0.94 | 0.94 | 0.09 |
Wording | χ 2 | CFI | TLI | RMSEA |
---|---|---|---|---|
Note. For all models df = 26 and p < 0.01; values within acceptable cutoffs are bolded. | ||||
Science | 49 | 0.99 | 0.99 | 0.07 |
Chemistry | 94 | 0.97 | 0.96 | 0.11 |
Though none of the four administration conditions of the SMQ II met the criteria for acceptable data-model fit used in this study with the WLSMV estimator, patterns in the fit indices suggest better fit for the SMQ II items, based on fit indices being closer to the cutoff values used in this study, when the chemistry-worded version was administered to general chemistry students and the science-worded version was administered to introductory chemistry students. The pattern of fit indices for the SDT items administered in the general chemistry courses followed a similar pattern as the SMQ II administered in introductory chemistry courses where slightly better data-model fit was seen for the science wording responses though, again, under no conditions was data-model fit acceptable. For both wordings of the SDT items the CFI and TLI were above the cutoff values, but the RMSEA never met the cutoff value.
While there are no prior studies with the specific set of SDT items from this research to use as a baseline, the SDT items were chosen for comparison with the SMQ II items since both instruments were developed to address aspects of student motivation. The administration of both instruments to general chemistry students provided an opportunity to look for consistencies in the types of changes occurring as a result of students responding to either the science or chemistry wording. Randomizing the wording seen by students within the same course also provided an opportunity to control for classroom effects that may have been present if the different wordings were presented to different intact courses. Even with this control, the CFA data-model fit for both instruments was unacceptable and did not follow consistent patterns. As with the SMQ II items, while none of the tested models met the data-model cutoff criteria defined for this study, in some conditions the data-model fit indices were closer to acceptable cutoff criteria than in other conditions. The data-model fit for the five-factor SMQ II was better for the chemistry wording in the general chemistry course while the two-factor data-model fit for the SDT items was better for the science wording in the general chemistry course. These results make it difficult to offer any insights to which wording conditions of a motivation instrument are most likely to have acceptable data-model fit in a given course type.
In addition to unidimensionality, Cronbach's alpha has the fundamental assumption that all items are associated with the underlying factor the same degree. This assumption was tested with single-factor tau-equivalent CFA models where all item loadings were constrained to be equal. Of the 24 tau-equivalent models tested, only two showed acceptable data-model fit according to the cutoffs used in this study (CFI or TLI ≥ 0.95 and RMSEA ≤ 0.05). For these scales, a value of alpha is reported in Table 6.
Scale | Intrinsic motivation | Self-determination | Self-efficacy | Grade motivation | Career motivation | Value | Belonging | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Wordinga | S | C | S | C | S | C | S | C | S | C | S | C | S | C | |
a S = Science, C = Chemistry. b GC = General Chemistry, IC = Introductory Chemistry. | |||||||||||||||
Courseb | GC | NE | NE | NE | NE | NE | NE | NE | 0.80 | 0.85 | NE | 0.79 | NE | NE | NE |
IC | NE | NE | NE | 0.83 | NE | NE | 0.86 | NE | NE | NE | — | — | — | — |
Omega total is an internal consistency estimate that has the fundamental assumption of unidimensionality, but not of tau-equivalence. Omega allows the items to load to varying degrees when evaluating the single-factor model. This assumption was tested with single-factor congeneric CFA models where the item loadings were not constrained to be equal. Of the 22 scales that did not meet the condition of tau-equivalence, only three scales showed acceptable data-model fit to a single-factor congeneric model. For these scales, a value of omega is reported in Table 6. No estimate of internal consistency is provided for scales that did not demonstrate acceptable data-model fit to the less restrictive congeneric model.
The values provided in Table 6 are all above the typically acceptable internal consistency estimates presented in the literature, and similar to values obtained in other studies for the science and chemistry wording of the SMQ II (Glynn et al., 2011; Salta and Koulougliotis, 2015; Hibbard et al., 2016). However, there is no universally agreed upon criterion value for acceptable internal consistency (Arjoon et al., 2013; Taber, 2017). As a result, the internal consistency estimates provided in Table 6 can be used for comparison with prior work, but cannot be used as an absolute indicator of scale or instrument quality.
The purpose of this research was to examine the effects that changes in item wording or the type of course in which the instrument was administered have at both the individual item and the overall instrument level. Understanding these effects is necessary to determine the conditions under which the data obtained from an instrument show acceptable evidence for validity and reliability. There are numerous types of evidence that can be provided for the validity and reliability of data, and interested readers are encouraged to consult the Standards for Educational & Psychological Testing (AERA, APA and NCME, 2014) and the work of Arjoon et al. (2013). In this study, to provide evidence that the motivation constructs being measured, both general science and chemistry-specific, were measured equally well under all conditions validity evidence was examined by testing the internal structure of the SMQ II and SDT items with confirmatory factor analysis (CFA).
The results of the CFAs conducted for this study provide no evidence to support the proposed internal structure of either motivation instrument in any of the conditions tested in this study. This demonstrates that what might appear to be minor changes to the wording of an instrument or the context where it is administered can have an effect on the structural validity of the data generated. In the case of these two motivation instruments, changing the wording from ‘science’ to ‘chemistry’ shifts the focus of the items from measuring general motivation to measuring domain-specific motivation. These aspects of motivation represent different constructs (Pajares and Schunk, 2001) and the current forms of the SMQ II and SDT items are not adequately measuring the constructs within the general chemistry and introductory chemistry courses sampled for this research. As a result, any interpretation or further analysis of scale scores or overall instrument scores would be inappropriate and potentially misleading.
The present study highlights that even with an instrument such as the SMQ II, which has undergone extensive development and testing, using the same wording as the original developers or a modification suggested by the developers (changing science to chemistry) can have an influence on the quality of the data obtained when the instrument is administered in different types of chemistry courses. Though the SDT items had a less robust development history, they also demonstrated similar issues with data quality under different administration conditions. It is also important to note that while some individual motivation scales showed acceptable data-model fit during the internal consistency analysis (Appendix 2), the developers did not intend the instruments to be used as individual scales. Instead, the theoretical framework of both instruments hypothesized a relationship among distinct, yet related, aspects of motivation. Since neither motivation instrument showed acceptable evidence for data quality under the conditions of this study, caution should be taken if using the instruments in their current forms under conditions similar to those investigated here unless additional validity or reliability evidence can be provided for the quality of data obtained from administering either wording of the instrument in introductory or general chemistry courses.
A second limitation is the purely quantitative nature of the evidence for data quality presented in this study. Further work with the SMQ II and SDT motivation items will involve interviewing students to better understand how wording, course type, and demographic factors may influence their responses. Additionally, student responses to individual items will be examined in greater detail to investigate the response process validity and to determine if, and how, students’ responses change based on wording and how this may impact the intended meaning of an item.
This study represents a first step in understanding how students’ motivation may change based on the type of course in which the student is enrolled or when motivation is contextualized as general science or discipline-specific. However, prior to analyzing any differences in motivation based on context, it is necessary to have a functional instrument that can be used to collect valid and reliable data in each setting of interest. The development and testing of such an instrument will be the focus of future work.
Item | Course | Wording | Mean | St. dev. | Median | Min | Max | Skew | Kurtosis |
---|---|---|---|---|---|---|---|---|---|
Note: GC = General chemistry; IC = Introductory chemistry. | |||||||||
The [] I learn is relevant to my life | GC | Chemistry | 3.44 | 1.08 | 3.00 | 1.00 | 5.00 | −0.32 | −0.48 |
Science | 3.86 | 0.92 | 4.00 | 1.00 | 5.00 | −0.49 | −0.13 | ||
IC | Chemistry | 2.65 | 1.03 | 3.00 | 1.00 | 5.00 | 0.23 | −0.32 | |
Science | 3.29 | 1.04 | 3.00 | 1.00 | 5.00 | −0.37 | −0.32 | ||
Learning [] is interesting | GC | Chemistry | 3.81 | 0.98 | 4.00 | 1.00 | 5.00 | −0.57 | −0.08 |
Science | 4.34 | 0.78 | 4.50 | 1.00 | 5.00 | −1.19 | 1.58 | ||
IC | Chemistry | 2.97 | 1.08 | 3.00 | 1.00 | 5.00 | −0.21 | −0.53 | |
Science | 3.71 | 0.97 | 4.00 | 1.00 | 5.00 | −0.64 | 0.24 | ||
Learning [] makes my life more meaningful | GC | Chemistry | 3.21 | 1.07 | 3.00 | 1.00 | 5.00 | −0.08 | −0.57 |
Science | 3.94 | 1.02 | 4.00 | 1.00 | 5.00 | −0.85 | 0.25 | ||
IC | Chemistry | 2.25 | 1.00 | 2.00 | 1.00 | 5.00 | 0.57 | −0.07 | |
Science | 3.26 | 1.14 | 3.00 | 1.00 | 5.00 | −0.20 | −0.61 | ||
I am curious about discoveries in [] | GC | Chemistry | 3.62 | 1.02 | 4.00 | 1.00 | 5.00 | −0.34 | −0.54 |
Science | 4.33 | 0.87 | 5.00 | 1.00 | 5.00 | −1.43 | 2.05 | ||
IC | Chemistry | 2.78 | 1.09 | 3.00 | 1.00 | 5.00 | 0.31 | −0.54 | |
Science | 3.60 | 1.04 | 4.00 | 1.00 | 5.00 | −0.41 | −0.43 | ||
I enjoy learning [] | GC | Chemistry | 3.61 | 0.98 | 4.00 | 1.00 | 5.00 | −0.37 | −0.19 |
Science | 4.28 | 0.81 | 4.00 | 1.00 | 5.00 | -1.09 | 1.17 | ||
IC | Chemistry | 2.83 | 1.10 | 3.00 | 1.00 | 5.00 | −0.06 | −0.70 | |
Science | 3.61 | 0.98 | 4.00 | 1.00 | 5.00 | −0.54 | 0.04 | ||
I put enough effort into learning [] | GC | Chemistry | 3.91 | 0.85 | 4.00 | 2.00 | 5.00 | −0.31 | −0.69 |
Science | 4.02 | 0.82 | 4.00 | 1.00 | 5.00 | −0.64 | 0.39 | ||
IC | Chemistry | 3.88 | 0.81 | 4.00 | 1.00 | 5.00 | −0.46 | 0.15 | |
Science | 3.78 | 0.78 | 4.00 | 1.00 | 5.00 | −0.39 | 0.21 | ||
I use strategies to learn [] well | GC | Chemistry | 3.76 | 0.94 | 4.00 | 1.00 | 5.00 | −0.28 | −0.63 |
Science | 3.79 | 0.92 | 4.00 | 1.00 | 5.00 | −0.39 | −0.20 | ||
IC | Chemistry | 3.53 | 0.89 | 4.00 | 1.00 | 5.00 | −0.29 | 0.13 | |
Science | 3.43 | 0.82 | 3.00 | 1.00 | 5.00 | −0.36 | 0.45 | ||
I spend a lot of time learning [] | GC | Chemistry | 3.49 | 0.91 | 3.00 | 1.00 | 5.00 | −0.14 | −0.32 |
Science | 3.94 | 0.86 | 4.00 | 1.00 | 5.00 | −0.71 | 0.31 | ||
IC | Chemistry | 3.90 | 0.88 | 4.00 | 1.00 | 5.00 | −0.38 | −0.42 | |
Science | 3.68 | 0.89 | 4.00 | 1.00 | 5.00 | −0.47 | −0.09 | ||
I prepare well for [] tests and labs | GC | Chemistry | 3.89 | 0.85 | 4.00 | 2.00 | 5.00 | −0.35 | −0.58 |
Science | 3.97 | 0.76 | 4.00 | 2.00 | 5.00 | −0.31 | −0.41 | ||
IC | Chemistry | 3.73 | 0.85 | 4.00 | 1.00 | 5.00 | −0.48 | 0.20 | |
Science | 3.57 | 0.75 | 4.00 | 1.00 | 5.00 | −0.11 | 0.09 | ||
I study hard to learn [] | GC | Chemistry | 3.79 | 0.91 | 4.00 | 2.00 | 5.00 | −0.16 | −0.92 |
Science | 3.99 | 0.90 | 4.00 | 1.00 | 5.00 | −0.67 | 0.23 | ||
IC | Chemistry | 3.93 | 0.86 | 4.00 | 1.00 | 5.00 | −0.48 | −0.20 | |
Science | 3.88 | 0.83 | 4.00 | 1.00 | 5.00 | −0.39 | −0.10 | ||
I am confident I will do well on [] tests | GC | Chemistry | 3.57 | 1.06 | 4.00 | 1.00 | 5.00 | −0.48 | −0.31 |
Science | 3.67 | 0.89 | 4.00 | 1.00 | 5.00 | −0.61 | 0.67 | ||
IC | Chemistry | 2.96 | 1.00 | 3.00 | 1.00 | 5.00 | −0.06 | −0.32 | |
Science | 2.98 | 0.86 | 3.00 | 1.00 | 5.00 | −0.21 | −0.20 | ||
I am confident I will do well on [] labs and projects | GC | Chemistry | 3.63 | 1.02 | 4.00 | 1.00 | 5.00 | −0.64 | 0.10 |
Science | 3.86 | 0.80 | 4.00 | 1.00 | 5.00 | −0.39 | 0.18 | ||
IC | Chemistry | 3.33 | 0.97 | 3.00 | 1.00 | 5.00 | −0.31 | −0.14 | |
Science | 3.37 | 0.86 | 3.00 | 1.00 | 5.00 | −0.53 | 0.38 | ||
I believe I can master [] knowledge and skills | GC | Chemistry | 3.79 | 0.97 | 4.00 | 2.00 | 5.00 | −0.29 | −0.95 |
Science | 4.10 | 0.87 | 4.00 | 1.00 | 5.00 | −0.94 | 1.03 | ||
IC | Chemistry | 3.40 | 1.00 | 3.00 | 1.00 | 5.00 | −0.36 | −0.24 | |
Science | 3.54 | 1.00 | 4.00 | 1.00 | 5.00 | −0.51 | −0.08 | ||
I believe I can earn a grade of A in [] | GC | Chemistry | 3.77 | 1.18 | 4.00 | 1.00 | 5.00 | −0.67 | −0.49 |
Science | 3.92 | 1.03 | 4.00 | 1.00 | 5.00 | −0.79 | 0.08 | ||
IC | Chemistry | 2.99 | 1.21 | 3.00 | 1.00 | 5.00 | 0.06 | −0.89 | |
Science | 3.23 | 1.14 | 3.00 | 1.00 | 5.00 | −0.22 | −0.82 | ||
I am sure I can understand [] | GC | Chemistry | 3.91 | 0.95 | 4.00 | 1.00 | 5.00 | −0.62 | −0.30 |
Science | 4.13 | 0.80 | 4.00 | 2.00 | 5.00 | −0.48 | −0.64 | ||
IC | Chemistry | 3.54 | 0.94 | 4.00 | 1.00 | 5.00 | −0.52 | 0.35 | |
Science | 3.66 | 0.83 | 4.00 | 2.00 | 5.00 | −0.14 | −0.55 | ||
I like to do better than other students on [ ] tests | GC | Chemistry | 4.20 | 1.01 | 4.00 | 1.00 | 5.00 | −1.44 | 1.79 |
Science | 4.10 | 1.01 | 4.00 | 1.00 | 5.00 | −0.97 | 0.15 | ||
IC | Chemistry | 3.86 | 1.12 | 4.00 | 1.00 | 5.00 | −0.87 | 0.17 | |
Science | 3.88 | 1.04 | 4.00 | 1.00 | 5.00 | −0.80 | 0.27 | ||
Getting a good [] grade is important to me | GC | Chemistry | 4.68 | 0.53 | 5.00 | 3.00 | 5.00 | −1.34 | 0.81 |
Science | 4.60 | 0.63 | 5.00 | 2.00 | 5.00 | −1.47 | 1.72 | ||
IC | Chemistry | 4.58 | 0.72 | 5.00 | 1.00 | 5.00 | −1.90 | 3.96 | |
Science | 4.54 | 0.70 | 5.00 | 1.00 | 5.00 | −1.85 | 4.36 | ||
It is important that I get an A in [] | GC | Chemistry | 4.45 | 0.81 | 5.00 | 2.00 | 5.00 | −1.39 | 1.07 |
Science | 4.40 | 0.79 | 5.00 | 2.00 | 5.00 | −1.09 | 0.22 | ||
IC | Chemistry | 4.28 | 0.85 | 5.00 | 1.00 | 5.00 | −0.99 | 0.39 | |
Science | 4.20 | 0.94 | 4.00 | 1.00 | 5.00 | −1.05 | 0.50 | ||
I think about the grade I will get in [] | GC | Chemistry | 4.60 | 0.61 | 5.00 | 2.00 | 5.00 | −1.44 | 1.84 |
Science | 4.47 | 0.75 | 5.00 | 2.00 | 5.00 | −1.37 | 1.41 | ||
IC | Chemistry | 4.60 | 0.73 | 5.00 | 1.00 | 5.00 | −2.23 | 5.91 | |
Science | 4.51 | 0.69 | 5.00 | 2.00 | 5.00 | −1.34 | 1.49 | ||
Scoring high on [] tests and labs matters to me | GC | Chemistry | 4.67 | 0.57 | 5.00 | 2.00 | 5.00 | −1.75 | 3.24 |
Science | 4.53 | 0.75 | 5.00 | 1.00 | 5.00 | −1.68 | 2.92 | ||
IC | Chemistry | 4.60 | 0.66 | 5.00 | 1.00 | 5.00 | −1.85 | 4.30 | |
Science | 4.51 | 0.73 | 5.00 | 2.00 | 5.00 | −1.37 | 1.16 | ||
Learning [] will help me get a good job | GC | Chemistry | 3.81 | 1.03 | 4.00 | 1.00 | 5.00 | −0.50 | −0.61 |
Science | 4.44 | 0.76 | 5.00 | 1.00 | 5.00 | −1.38 | 2.10 | ||
IC | Chemistry | 3.36 | 1.13 | 3.50 | 1.00 | 5.00 | −0.28 | −0.81 | |
Science | 4.04 | 0.95 | 4.00 | 1.00 | 5.00 | −0.86 | 0.35 | ||
Knowing [] will give me a career advantage | GC | Chemistry | 3.96 | 1.04 | 4.00 | 1.00 | 5.00 | −0.84 | 0.00 |
Science | 4.51 | 0.74 | 5.00 | 2.00 | 5.00 | −1.44 | 1.35 | ||
IC | Chemistry | 3.46 | 1.15 | 3.00 | 1.00 | 5.00 | -0.20 | −0.95 | |
Science | 4.11 | 0.92 | 4.00 | 1.00 | 5.00 | −0.79 | −0.08 | ||
Understanding [] will benefit me in my career | GC | Chemistry | 3.94 | 1.05 | 4.00 | 1.00 | 5.00 | −0.85 | 0.11 |
Science | 4.58 | 0.71 | 5.00 | 1.00 | 5.00 | −2.05 | 5.12 | ||
IC | Chemistry | 3.32 | 1.20 | 3.00 | 1.00 | 5.00 | −0.16 | −0.99 | |
Science | 4.21 | 0.93 | 4.00 | 1.00 | 5.00 | −0.98 | 0.12 | ||
My career will involve [] | GC | Chemistry | 3.55 | 1.17 | 4.00 | 1.00 | 5.00 | −0.31 | −0.83 |
Science | 4.55 | 0.66 | 5.00 | 2.00 | 5.00 | −1.32 | 1.01 | ||
IC | Chemistry | 2.99 | 1.17 | 3.00 | 1.00 | 5.00 | 0.13 | −0.82 | |
Science | 4.11 | 1.00 | 4.00 | 1.00 | 5.00 | −0.90 | −0.05 | ||
I will use [] problem−solving skills in my career | GC | Chemistry | 3.62 | 1.00 | 4.00 | 1.00 | 5.00 | −0.26 | −0.51 |
Science | 4.33 | 0.86 | 5.00 | 1.00 | 5.00 | −1.44 | 2.20 | ||
IC | Chemistry | 2.99 | 1.13 | 3.00 | 1.00 | 5.00 | 0.00 | −0.67 | |
Science | 3.70 | 1.07 | 4.00 | 1.00 | 5.00 | −0.51 | −0.33 |
Item | Wording | Mean | St. dev. | Median | Min | Max | Skew | Kurtosis |
---|---|---|---|---|---|---|---|---|
I believe that [] can help make the world a better place | Chemistry | 4.12 | 0.77 | 4.00 | 1.00 | 5.00 | −0.72 | 1.05 |
Science | 4.56 | 0.60 | 5.00 | 2.00 | 5.00 | −1.16 | 0.95 | |
I can see lots of ways that [] makes a positive difference in our everyday lives | Chemistry | 4.06 | 0.86 | 4.00 | 1.00 | 5.00 | −0.87 | 0.99 |
Science | 4.50 | 0.66 | 5.00 | 2.00 | 5.00 | −1.04 | 0.33 | |
[] can help solve some of the world's problems | Chemistry | 4.15 | 0.77 | 4.00 | 1.00 | 5.00 | −0.78 | 0.77 |
Science | 4.50 | 0.68 | 5.00 | 1.00 | 5.00 | −1.48 | 3.01 | |
If everyone learned more about [], we could all make more informed decisions about politics, medicine, and the environment | Chemistry | 3.90 | 0.91 | 4.00 | 1.00 | 5.00 | −0.78 | 0.63 |
Science | 4.45 | 0.69 | 5.00 | 2.00 | 5.00 | −0.93 | −0.02 | |
A major in [] is a good fit for me | Chemistry | 2.70 | 1.13 | 3.00 | 1.00 | 5.00 | 0.31 | −0.60 |
Science | 3.89 | 0.94 | 4.00 | 1.00 | 5.00 | −0.47 | −0.57 | |
I am the kind of person who can succeed in [] | Chemistry | 3.77 | 0.93 | 4.00 | 1.00 | 5.00 | −0.67 | 0.42 |
Science | 4.06 | 0.80 | 4.00 | 2.00 | 5.00 | −0.62 | 0.01 | |
I feel at home in [] | Chemistry | 3.07 | 1.04 | 3.00 | 1.00 | 5.00 | 0.05 | −0.64 |
Science | 3.70 | 0.98 | 4.00 | 1.00 | 5.00 | −0.34 | −0.52 | |
Sometimes I feel like I don’t belong in [] | Chemistry | 2.82 | 1.22 | 3.00 | 1.00 | 5.00 | 0.21 | −1.06 |
Science | 2.52 | 1.14 | 2.00 | 1.00 | 5.00 | 0.27 | −0.97 | |
I’m not the type of person to get a degree in [] | Chemistry | 3.01 | 1.19 | 3.00 | 1.00 | 5.00 | 0.08 | −0.91 |
Science | 1.93 | 0.97 | 2.00 | 1.00 | 5.00 | 0.96 | 0.50 |
Scale | Course | Wording | χ 2 | p | CFI | TLI | RMSEA |
---|---|---|---|---|---|---|---|
Note: for all models df = 5 and WLSMV estimator used; values within acceptable cutoffs are bolded. | |||||||
Intrinsic motivation | GC | Science | 21.7 | <0.01 | 0.98 | 0.97 | 0.15 |
Chemistry | 61.4 | <0.01 | 0.96 | 0.92 | 0.28 | ||
IC | Science | 10.2 | 0.07 | 1.00 | 1.00 | 0.08 | |
Chemistry | 49.3 | <0.01 | 0.98 | 0.96 | 0.22 | ||
Self-determination | GC | Science | 16.9 | <0.01 | 0.98 | 0.95 | 0.13 |
Chemistry | 8.7 | 0.12 | 1.00 | 1.00 | 0.07 | ||
IC | Science | 17.6 | <0.01 | 0.98 | 0.97 | 0.12 | |
Chemistry | 7.2 | 0.20 | 1.00 | 1.00 | 0.05 | ||
Self-efficacy | GC | Science | 13.4 | 0.02 | 0.99 | 0.98 | 0.11 |
Chemistry | 15.3 | <0.01 | 0.99 | 0.99 | 0.12 | ||
IC | Science | 10.0 | 0.07 | 1.00 | 0.99 | 0.07 | |
Chemistry | 18.5 | <0.01 | 0.99 | 0.98 | 0.12 | ||
Grade motivation | GC | Science | 10.1 | 0.07 | 0.99 | 0.99 | 0.08 |
Chemistry | 2.5 | 0.78 | 1.00 | 1.01 | 0.00 | ||
IC | Science | 6.4 | 0.27 | 1.00 | 1.00 | 0.04 | |
Chemistry | 9.5 | 0.09 | 0.99 | 0.99 | 0.07 | ||
Career motivation | GC | Science | 7.3 | 0.20 | 1.00 | 1.00 | 0.06 |
Chemistry | 12.2 | 0.03 | 1.00 | 0.99 | 0.10 | ||
IC | Science | 21.0 | <0.01 | 1.00 | 0.99 | 0.13 | |
Chemistry | 52.6 | <0.01 | 0.99 | 0.98 | 0.23 |
Scale | Course | Wording | χ 2 | p | CFI | TLI | RMSEA |
---|---|---|---|---|---|---|---|
Note: for all models df = 9 and WLSMV estimator used; values within acceptable cutoffs are bolded. | |||||||
Intrinsic motivation | GC | Science | 29.2 | <0.01 | 0.98 | 0.98 | 0.12 |
Chemistry | 94.5 | <0.01 | 0.94 | 0.93 | 0.26 | ||
IC | Science | 88.3 | <0.01 | 0.96 | 0.96 | 0.22 | |
Chemistry | 83.6 | <0.01 | 0.97 | 0.96 | 0.21 | ||
Self-determination | GC | Science | 29.1 | <0.01 | 0.96 | 0.96 | 0.12 |
Chemistry | 38.8 | <0.01 | 0.98 | 0.98 | 0.15 | ||
IC | Science | 36.0 | <0.01 | 0.96 | 0.96 | 0.13 | |
Chemistry | 61.0 | <0.01 | 0.96 | 0.95 | 0.18 | ||
Self-efficacy | GC | Science | 22.3 | 0.01 | 0.98 | 0.98 | 0.10 |
Chemistry | 15.6 | 0.08 | 1.00 | 1.00 | 0.07 | ||
IC | Science | 57.6 | <0.01 | 0.97 | 0.97 | 0.17 | |
Chemistry | 17.0 | 0.05 | 0.99 | 0.99 | 0.07 | ||
Grade motivation | GC | Science | 54.6 | <0.01 | 0.95 | 0.94 | 0.19 |
Chemistry | 61.3 | <0.01 | 0.94 | 0.93 | 0.20 | ||
IC | Science | 44.7 | <0.01 | 0.96 | 0.96 | 0.15 | |
Chemistry | 35.2 | <0.01 | 0.96 | 0.95 | 0.13 | ||
Career motivation | GC | Science | 11.1 | 0.27 | 1.00 | 1.00 | 0.04 |
Chemistry | 38.5 | <0.01 | 0.99 | 0.99 | 0.15 | ||
IC | Science | 44.6 | <0.01 | 0.99 | 0.99 | 0.15 | |
Chemistry | 85.6 | <0.01 | 0.98 | 0.98 | 0.22 |
Scale | Wording | χ 2 | df | p | CFI | TLI | RMSEA |
---|---|---|---|---|---|---|---|
Note: WLSMV estimator used; values within acceptable cutoffs are bolded. | |||||||
Value | Science | 5.6 | 2 | 0.06 | 1.00 | 1.00 | 0.09 |
Chemistry | 9.6 | 2 | <0.01 | 1.00 | 0.99 | 0.14 | |
Belonging | Science | 11.3 | 5 | 0.05 | 1.00 | 0.99 | 0.08 |
Chemistry | 49.2 | 5 | <0.01 | 0.96 | 0.93 | 0.21 |
Scale | Wording | χ 2 | df | p | CFI | TLI | RMSEA |
---|---|---|---|---|---|---|---|
Note: WLSMV estimator used; values within acceptable cutoffs are bolded. | |||||||
Value | Science | 7.4 | 5 | 0.19 | 1.00 | 1.00 | 0.05 |
Chemistry | 70.6 | 5 | <0.01 | 0.96 | 0.95 | 0.26 | |
Belonging | Science | 51.1 | 9 | <0.01 | 0.97 | 0.97 | 0.15 |
Chemistry | 50.9 | 9 | <0.01 | 0.97 | 0.96 | 0.15 |
This journal is © The Royal Society of Chemistry 2018 |