Identifying the challenges of online education from the perspective of University of Medical Sciences Students in the COVID-19 pandemic: a Q-methodology-based study | BMC Medical Education4 min read
This cross-sectional analyze was executed using the Q methodology during the subsequent six techniques utilizing Barry and Proops method .
Phase 1 and 2: defining the concourse
At this phase, a concourse space was fashioned with the identification of the matter or idea of the analyze. The offered sights on the situation elevated for the concourse can be formed from a assessment of texts and authorities in this field .
In this review, the matter and notion for the concourse were being the problems of on the web education and learning all through the COVID-19 pandemic. The concourse provided a collection of various supplies associated to the investigation subject that was mentioned among the students. The pupils (P-established) who also experienced contributed before to the enhancement of the initial set of statements. Thirty-one particular learners participated in semi-structured interviews, and we tried using to establish their subjectivity about the investigation matter working with the Q approach .
In this analyze, the concourse (sample of people) provided college students of the University of Health care Sciences (paramedical learners) who had enough data about online training for the duration of the COVID-19 pandemic.
Stage 3: screening and assortment of statements (Q-sample)
In the course of the semi-structured interviews with 31 learners, 70 statements ended up extracted about the perceived difficulties of online schooling. The Q goods ended up picked quite diligently so that things did not overlap, and at the exact time, no point of view need to be missing. Thus, the variety process usually takes the most time and hard work of all the measures of the Q methodology. For that reason, exploration group taken out related unrelated, and ambiguous statements from the Q set. Eventually, 50 statements had been selected.
Phase 4: picked P-established
Students who participated in the concourse (interviews) had been chosen as a sample of individuals to take part in sorting in the Q examine (P-established). In the present study, learners have been picked by purposive sampling to include college students who experienced an academic, skilled, experimental marriage or prior information about the subject of study. This range of samples designed the contributors with much more varied mentalities enter the examine. It is advised that in Q experiments, the variety of members to form statements should really be less than the number of statements close to the analyze issue . In the present examine, the range of individuals who rated the troubles of on the web education packages was 31 (Table 1).
Phase 5: Q-sort
At this stage, the normal distribution table in the type of a Likert scale from − 5 to + 5 was developed offline. Suggestions on distributing the expressions on the typical distribution table have been delivered. In the first phase, the intent of the examine is the quantity of statements picked by means of the interview. In the second phase, position the statements in 3 columns: “I agree”, “I have no viewpoint,” and “I disagree. In the third phase, the statements (necessary) are distributed in the ordinary Likert distribution diagram (− 5 to 5+), detailing the motive for deciding upon the two ends of the Likert scale from their issue of look at and lastly moving into the demographic facts. So, in Q, the sorting process is subjective . In other text, sorting things in the regular distribution make it possible for each and every participant to existing their internal standpoint via sorting.
Stage 6: examination and interpretation of components
Students’ knowledge obtained from Q sorting were being entered into PQ-Method program model 2.35. The system of analysis and interpretation was executed in three levels: (a) identification of factors, (b) conversion of elements into element arrays (c) interpretation of variables applying element arrays.
The extraction of components in PQ-Method software was performed by the subsequent sequential techniques: (a) principal element examination, (b) identification of latent things, (c) varimax rotation and analysis of loading aspects for unique values earlier mentioned 1.00, d) estimation of the percentage of variance described by the discovered variables and (e) differentiation of interpretable components with at minimum two correlated Q forms .
Transform element to element arrays
The correlation among each Q sort and just one discovered element indicates the degree of interaction in between the Q sorts and the recognized factors [19, 23]. The handbook flagging in PQ-Approach program was used for this examine. The correlation coefficients of at the very least .364 had been thought of as the reduce-off issue (the absolute value of the issue load is increased than ((frac2.58sqrtN)). That factor load was 99% significant, respectively, and the value of N was equivalent to the quantity of Q statements (N = 50). Sorted for identified elements . Specs specified on a component are employed to create a aspect array for that element. The variable array represents the sorting of that factor (point of watch) applying z-scores. The component array for each individual issue determined the degree to which each individual expression was in the spectrum, so a extra precise interpretation of just about every component (subjectivity) was attained according to the placement of every single expression. (P-worth< 0.05 vs. 0.01) is also determined from the Z score to distinguish expressions .
Factor interpretation using factor arrays
Distinct Q expressions were identified, and factors were interpreted textually. The defining expressions for a factor were those that had a rating value of “+ 5”, “+ 4”, “4-,” 5- “in factor arrays that had different scores (P < 0.05) in a given factor Compared to their scores on other factors, the post-P-set interview was conducted at the end of Q sorting to confirm the diagnosis and interpretation of item subgroups among the identified factors.