confirmatory factor analyses were conducted and followed by estimating Cronbach’s alpha to explore internal consistency of the items. Exploratory factor analysis was used to understand the factor structure and confirmatory factor analysis was used to identify the best fitted model. In order to conduct both exploratory and confirmatory analyses of the Caring Relationship Surveys with different analytic samples, a random selection procedure in SPSS was used to divide both the student and teacher datasets in half. This process resulted in one sample of students (N=386) and teachers (N=314) for conducting exploratory factor analysis (EFA), and a second sample of students (N=386) and teachers (N=315) for conducting confirmatory factor analysis (CFA). I began analyses of each of the datasets by (1) estimating univariate descriptive statistics for individual items and the composites representing the four constructs; and (2) estimating bivariate correlations between all items, with an emphasis on interpreting the correlations of items within composites. Cronbach’s alphas for these composites were also estimated.
analysis (EFA) was conducted to understand the underlying factor structures of the items.
Specifically, these exploratory analyses provided initial information about consistency between the groups of items developed for the surveys and the four theoretical constructs underlying Noddings’ definition of care. Estimates of Cronbach’s alpha were used to understand the internal consistency of the resulting composites for both student and teacher instruments. I also conducted item
analyses to determine whether the estimated reliability of each composite would be lessened by the removal of each item in the composite, while retaining a reasonable internal consistency (alpha level).
The results of the exploratory factor analysis were used to inform the development of specific measurement models to be tested using confirmatory factor analysis (CFA) with the second student and teacher datasets. Even though the Caring Relationship Survey (CRS)—Student Version and Teacher Version were developed based on a strong theoretical framework, I chose to conduct EFA prior to CFA to understand the item loadings (Fabrigar & Wegener, 2012) in these newly developed questionnaires, providing me with an understanding of different dimensions of students’ and teachers’ perceptions of care.
One important aspect of the exploratory analyses conducted before the confirmatory analysis was to better understand the distinction between Engrossment and Motivational
Displacement sub-constructs that are difficult to distinguish from each other. Engrossment mainly describes internalized behaviors of teachers; however, creating items to measure this internalized process for students requires items to describe and connect to a specific observable action.
Motivational Displacement is often shown through an action from teachers that then is perceived by students. Therefore, the items were developed based on the visible actions of the teacher’s behaviors and intentions. Theoretically, it makes sense to first pay attention to students’ needs and goals (Engrossment), then to take actions to help them achieve their goals or meet their needs (Motivational Displacement). Exploratory analyses of items measuring Engrossment and
Motivational Displacement was necessary to help further consider these issues.
In addition to providing information about distinguishing between Engrossment and Motivational Displacement questions, another benefit of conducting EFA prior to CFA is to explore whether there are any potential underlying factors derived from Noddings’ theory that might not be perceived by teachers or students. When items were developed based on Noddings’ theory, some items were designed to describe idealized behaviors that are assumed in Noddings’ conception of care, but the items might not be perceived as care from a more practical perception of teachers or students. Ethical theory describes the ways in which people ought to act. The idealized behaviors embedded in Noddings’ definition of care do not reflect the current political and cultural
environment that might have an impact on people’s behaviors. For example, one of the items on the survey is “I consider my students' needs to be more important than my tasks as a teacher”. The tension that a teacher might experience in responding to this statement depends on the teacher’s beliefs. If a teacher believes that education is a set of tasks, they might disagree with the statement. Furthermore, teachers in school settings characterized by test-driven policies might consider their primary responsibilities to be accomplishing those tasks associated with helping students pass exams. From Noddings’ perspective, caring for individual students should always come before tasks, but this may not be valued in certain educational contexts. Because of potential ambiguity in how teachers may understand this item and other like it, EFA is a useful way to explore the
possibility that there could be another factor underlying the items.
Often EFA is employed by researchers who are using a set of items to explore possible underlying theoretical constructs and CFA is often used when researchers have a specific theory (and thus model) to test. However, it would be unlikely for scholars who employ EFA to have no expectations regarding the latent factors that might be informed by their analyses (Fabrigar & Wegener, 2012). The approach of conducting EFA and then CFA is often adapted by researchers developing a new survey instrument, as is the case in the current study. For example, both Ang
(2005) and Garza, Ryser, & Lee (2009) first conducted EFA and then CFA to establish reliability and validity of instruments they developed to understand aspects of teacher-student relationships. Furthermore, using EFA with one half of the data and then CFA with the rest of the data is common practice (Fabrigar & Wegener, 2012), because it provides a way to evaluate and specify the factor structure of the newly developed items with one sample and then to confirm this factor structure with another sample (Worthington & Whittaker, 2006). However, one concern of splitting the sample into two smaller groups is the potential effects on the accuracy of the model results since the resulting sample sizes can be too small. In terms of the sample size for exploratory factor analysis, there are different views regarding the optimal participant-item ratio. The suggestions range from 3:1 to 20:1 (Williams, Brown, & Onsman, 2012). For the current study, there are 43 items in the teacher questionnaire; therefore, a sample size of 315 for each of the sub-samples is within the suggested ratio range between 129 and 860. With 42 items in the student questionnaire, a sample size of 386 for each sub-sample also lands within the suggested ratio range between 126 and 840. There are also different ways of determining sample sizes for confirmatory factor analysis (Kelloway, 2015). Tomarken & Waller (2005) suggest that when using confirmatory factor analysis, a minimum sample size of 200 is recommended with any four factor models. With both sample sizes above 300 after splitting the sample, both contain an adequate number of responses to conduct confirmatory factor analysis.