Validation of the Digestive System Concept Scale for 7th‑Grade Students: Integrating Factor Analysis and Rasch Modeling

Validation of the Digestive System Concept Scale for 7th‑Grade Students: Integrating Factor Analysis and Rasch Modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation of the Digestive System Concept Scale for 7th‑Grade Students: Integrating Factor Analysis and Rasch Modeling Fatih Ozkan, Fulya Oner Armagan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7707356/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding students' misconceptions in science requires instruments that are both pedagogically meaningful and psychometrically sound. This study presents a comprehensive validation of the 20‑item Digestive System Concept Scale (DSCS) administered to 7th‑grade students in Turkey. The scale was designed to identify persistent misconceptions about digestive processes, combining content validity from curriculum and expert review with two‑stage multiple‑choice items scored dichotomously. Data from 412 students were analyzed. Exploratory factor analysis (EFA) on tetrachoric correlations suggested one to two latent factors. Confirmatory factor analysis (CFA) using weighted least squares means and variance adjusted (WLSMV) estimation favored a two‑factor model (CFI = 0.997, TLI = 0.996, RMSEA = 0.009, SRMR = 0.070) over a one‑factor alternative (CFI = 0.978, TLI = 0.975, RMSEA = 0.024, SRMR = 0.076). Rasch modeling suggested unidimensionality (nonvital person left out reliabilities), and unidimension person reliabilities equals to 0. 706 while span for item difficulties within the range of -0.85 and 0.786 logits. The correlation between factors was high, suggesting that student responses are governed by a single underlying proficiency with two interpretable facets.. Six items exceeded standardized fit thresholds, but their mean‑square statistics remained acceptable; no pair of items showed residual correlations above 0.20. Together, the findings support reporting a single Rasch total score while retaining the two‑factor structure for interpretive purposes. We discuss implications for instrument design, instructional practice, and future research. science education misconceptions diagnostic assessment factor analysis Rasch modeling Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Humans construct knowledge by integrating new information with their existing cognitive structures (Pritchard, 2017 ). When a learner's prior conceptions clash with instructional materials, misconceptions arise, but resilient ideas that can obstruct understanding and hinder further learning (Duit & Treagust, 2003 ). Misconceptions in science are well‑documented and notoriously persistent across age groups and domains (Smith et al., 1994 ; Leonard et al., 2014 ). For instance, in biology, students tend to think that plants absorb food in the soil or that a human being breathes in oxygen and breathes out only carbon dioxide. These types of myths do not only misrepresent a learner’s understanding of the phenomena of science, but also interfere with the learner’s ability to think in an integrated manner, thus, stifling learning (Vosniadou, 2020 ). Within the realm of human physiology, the digestive system is an area where misconceptions flourish. Previous studies have identified that middle‑school and high‑school students believe digestion occurs exclusively in the stomach, that chemical and physical digestion never occur concurrently, or that nutrients are completely absorbed in a single organ (Gul et al., 2024 ; Uğur, 2010 ). Educators occasionally reinforce analogous misconceptions when textbooks present vague explanations or when their own mastery of the subject matter is inadequate (Hewson & Hewson, 1983 ). Such inaccuracies endure within undergraduate cohorts as well as among pre-service teachers, thereby revealing the institutional rather than the incidental character of the difficulty (Griffard & Wandersee, 2001 ; Dry, 1998 ). Recognizing and addressing students' misconceptions is therefore a crucial task for science educators (Gil‑Perez & Carrascosa, 1990; Suprapto, 2020 ). To do so effectively requires reliable tools that can diagnose misconceptions and monitor conceptual change over time. Assessing misconceptions poses several challenges. Misconceptions are often nuanced and context‑dependent; thus, instruments must be carefully designed to capture variations in understanding (Treagust, 1988 ). Because correct responses may not always reflect deep conceptual understanding, some students may guess or memorize definitions, many researchers advocate for two‑tier or two‑stage diagnostic tests that require students not only to select an answer but also to justify their choice or to select a reasoning statement (Chandrasegaran et al., 2007 ; Lin, 2004 ). The equal measure of content validity, diagnostic cognitive data, and psychometric rigor is non negotiable in every form of sound measurement. However, outlining the more recent misconception scales demonstrates the use of validity and reliability in the scales is fundamentally lacking. Even the scales of internal reliability, factor structure, and content and construct validity were found to be low and exceedingly poor affirming sets of deficiencies regarding validation (Johnson & Morgan, 2016 ). This study addresses these shortcomings by using modern psychometric techniques, factor analysis and Rasch modeling, to evaluate the DSCS as a diagnostic instrument. Misconceptions in Digestive System Education Numerous research have documented the misconceptions concerning the digestive system. Dry ( 1998 ) showed that students do not see the contribution of the mouth as an organ of digestion, seeing the liver as a digestion organ. Gul et al. (2015) showed that even after being taught, a student may hold the misconception that the entire digestion process happens in the small intestine or may confuse the mechanical and chemical stages of digestion. Recent studies have pointed out the inability to explain the functions of an enzyme, the contraption of bile, and absorption tissue (Lin et al., 2016 ; Roswati et al., 2019 ). These issues may stem from a lack of appreciation of the banal day to day activities and an oversimplistic textbook portrayal. Digestion is often illustrated as a simplistic and unnecessarily simplistically routed through the stomach. Such misconceptions may arise from the cognitive load of complex phenomena such as the process of peristalsis, hydrolysis and the absorption of nutrients (Karpudewan et al., 2017). Such misconceptions necessitate tests that target poorly conceived notions instead of simply gauging lack of depth in ideas. Assessing Misconceptions: Psychometric Considerations The tools designed to assess a person’s misconceptions must satisfy certain requirements grouped under Content Validity – reflecting the relevant areas (digestive systems, organs, or even digestive chemical processes). Construct Validity entails relevant scaling misconceptions appropriate to the concept (Padgett & Morgan, 2020 ). As reliability, which is often evaluated with the person separation reliability in Rasch analysis or in Cronbach’s alpha, She (reliability) tells us the accuracy and stability of the tool (Sijtsma 2009 ). In terms of reliability, α ≥ 0.70 is acceptable highly desirable, while almost perfect reliability α > 0.90 is tells it is possible that we have too many items to measure the same thing (Kaiser 1974 ). ¿With the use of factor analysis, we can better understand the axes of a the provided set of values. EFA (exploratory factor analysis) is used to identify latent structures without pre-defined frameworks, while CFA (Confirmatory) is used to validate pre-determined propositions (Fabrigar & Wegener, 2011 ; Klem, 2000 ). In multi-valued or Likert’s scale survey tools, it is suggested that polychoric correlations have to be used with weighted least squares estimators to filter to the ordinal values (Flora & Curran, 2004 ). In the case of a dichotomous variable, the use of tetrachoric correlations is suggested. Fit indices such as the comparative fit index (CFI), Tucker–Lewis index (TLI), and root mean square error of approximation (RMSEA) provide general benchmarks for model fit: values of CFI/TLI ≥ 0.90 and RMSEA ≤ 0.08 are often considered indicators of good fit. Modern item response theory approaches, such as Rasch modeling, place persons and items on a common latent scale, enabling interval‑level measurement and providing item‑level diagnostics (Wright & Mok, 2004 ). In the one‑parameter logistic model (1PL), all items share the same discrimination, and the only parameter estimated is item difficulty. Item fit is typically assessed with statistics such as Infit and Outfit mean squares; values between about 0.7 and 1.3 are generally considered acceptable (Bond & Fox, 2015 ; Boone & Scantlebury, 2006 ). The person reliabilities (similar to Cronbach's alpha) along with separation indices are results of Rasch analyses which indicate the degree to which the tool can differentiate between the various levels of the trait being measured (Padgett & Morgan, 2020 ). One of the strongest forms of unidimensionality in scalogram analyses involves local independence: item scores must not show any unexplained correlation of association above and beyond the predicted correlations derived from the latent trait. Diagnostics such as Yen's Q3 statistic can detect local dependence; violations suggest that items may be redundant or share a common stimulus (Chen & Thissen, 1997 ). Rasch modeling can thus inform both the retention and revision of items during instrument development. The Digestive System Concept Scale (DSCS) The DSCS was developed to detect misconceptions among 7th‑grade students concerning the digestive system. It consists of 20 multiple‑choice items, each designed to probe a specific aspect of digestion (e.g., the sequence of digestion, the role of enzymes, the distinction between mechanical and chemical digestion, nutrient absorption, and organ functions). In this study, the items were assessed as correct, scored as ‘1’, or incorrect, scored as ‘0’. The items were taken from the 7th Grade Science Curriculum, and then the clarity and content of the items were reviewed the experts and pilot tested. While the DSCS originally contained fewer items, the current version expands coverage across the domain to improve reliability and to permit robust factor and Rasch analysis. The goals that we set for ourselves are two. First, we conduct an exploratory and confirmatory factor analysis of the DSCS’s factor structure to consider the possibility of one latent trait(s) (the severity of misconceptions) and how well one or more factors could structurally describe the phenomenon. Second, we use Rasch modeling to examine item functioning, person reliability, local item independence, and general scale reliability. All these approaches together contribute to adequate psychometric reasoning to interpret the DSCS scores and formulate the appropriate instructional strategies. Theoretical Framework: Conceptual Change in Science Education To understand why misconceptions stick, one must study constructivist learning theory where knowledge is added to what is already known. Children, according to Piaget, add new knowledge to their mental structures as long as it is new until it is shown that they must change the structures in order to accommodate new knowledge. Posner and the rest built on this idea to conceptual change and argued that learners must become dissatisfied with their conceptions before they can change to more scientifically accurate ones (Posner et al., 1982 ). This dissatisfaction is more than often triggered by cognitive conflict situations where the current reality does not match what is believed. Cognitive conflict alone is insufficient. Therefore, learners have to deal with new explanations that are understandable, believable, and practical. The long-lasting presence of misconceptions is due to the absence of explanations, or the explanations that are provided do not fulfill requirements. (Taber, 2006 ). In the case of the digestive system, “digestion happens where food is stored” and “enzymes get used up like fuel” are naive heuristics that children have as explanations, and are used because they are similar to real-life. Such intuitions are hard to change because they are practical in real life and scientific phenomena that are below the surface level of life are complex and invisible. According to diSessa's phenomenological primitives theory (diSessa, 1993 ), such fragments of knowledge (or "p-prims") are contextually activated and need not form coherent theories. Effective conceptual change thus requires not only introducing correct information but also restructuring underlying intuitions. Strategies such as refutation texts (Tippett, 2010 ), which explicitly state a misconception and then refute it with evidence and other conceptual change texts can help in this process. Diagnostic tests like the DSCS provide teachers with the information necessary to tailor such interventions. Another perspective is the resources framework, which suggests that misconceptions are not monolithic ideas but rather consist of multiple semi‑independent resources that can be activated differently across contexts (Hammer, 2000 ). From this viewpoint, assessment instruments need to sample understanding in varied contexts to capture the variability of student thinking. The DSCS therefore includes items that probe the same underlying concept in different ways, for example, multiple items assess understanding of chemical digestion in both the mouth and the stomach to ensure that we capture stable misconceptions rather than isolated responses. This design helps reveal whether students hold consistent misconceptions across scenarios or whether their answers vary with surface features. Method Participants and Data Collection Participants were 412 7th‑grade students (mean age = 12.8 years, SD = 0.7) from nine public schools in a large metropolitan area in Turkey. The sample was roughly balanced in terms of gender (52% female). Parents agreed to the process and the students chose to take part in the process. Data collection took place during the normal lessons of science. The students DSCS, were supervised by their teachers who primarily were in charge of the class. The students were told to work alone and were told to use all their possible knowledge to complete the set in the time limit of 45 minutes. Teachers collected the answer sheets, which were later coded and double-entered into a database by trained researchers. Instrument Development The 20 items of the DSCS were developed through a multi‑step process. Researchers first reviewed the 7th‑grade science curriculum, textbooks, and relevant educational literature to identify key concepts in digestion and common misconceptions.Draft items were created as two-option multiple choice questions, including distractors that captured common inaccuracies (e.g., "Physical digestion takes place only in the mouth of the mouth" versus "Chemical digestion occurs exclusively in the stomach"). These items were piloted with a small group of 30 students and reviewed by three experienced science teachers and a university professor of biology education. The reviewers assessed clarity, appropriateness for 7th‑grade comprehension, and content coverage. Items were revised or discarded based on pilot performance and expert feedback, yielding a 20‑item instrument covering four conceptual domains (digestive organs; chemical digestion and enzyme action; carbohydrate and nutrient metabolism; and absorption). Additionally, during development we undertook cognitive interviews with a diverse subset of ten 7th‑grade students to ensure that item wording was interpreted as intended. In these think‑aloud sessions, some students misinterpreted certain phrases (for example, a few thought "chemical digestion" meant that chemicals are added to food). Based on their feedback, we refined the wording of items to highlight processes (e.g., enzyme action) rather than substances. We also asked students to explain their reasoning for each distractor, to verify that each incorrect option indeed reflected a common misconception. Items that did not elicit the expected misconceptions were revised or replaced. This iterative development process helped ensure that the DSCS not only aligned with curriculum standards but also resonated with students' actual thinking. The revised instrument was then pilot tested in two additional schools. We analyzed item difficulties and item-total correlations from this field test to fine‑tune the scale before full deployment. The development process pinpointed the weaknesses and gaps and thus confirmed the content validity of the DSCS. Item-level descriptives can be found in Table 1 . Table 1 Exploratory Factor Analysis (minres, oblimin)—Pattern Loadings (≥ .30 Shown) Factors/Items F1. Digestive Processes and Chemical Digestion F2. Organs and Mechanisms x5. Nutrients are digested and excreted in the small intestine. 0.39 — x6. Digestion begins in the mouth and ends in the liver 0.31 — x7. The absorption of digested food and its passage into the blood occurs in the large intestine. 0.55 — x12. Only chemical digestion takes place in the stomach. 0.52 — x13. Digestive enzymes are effective only at a certain temperature. 0.49 — x14. Since carbohydrates are digested in the mouth, they are not digested in the stomach. 0.42 — x16. Only water is absorbed in the small intestine. 0.38 — x17. Fat digestion does not occur in the mouth. 0.80 — x18. Fat digestion takes place in the stomach. 0.49 — x20. 0.35 — x1. Physical digestion of food begins in the mouth, while chemical digestion does not occur. — 0.51 x2. The digestive system is a filtering process by the stomach. — 0.37 x3. Since water cannot be digested, it cannot be absorbed in the small intestine and is absorbed in the large intestine — 0.32 x4. Only physical digestion takes place in the mouth. — 0.47 x8. Physical digestion takes place in the esophagus. — 0.51 x9. Increasing the amount of enzymes and adding gastric juice accelerates digestion in the stomach. — 0.32 x11. The liver secretes bile and digests fats, and the pancreas secretes carbohydrates and proteins. — 0.54 x15. A piece of meat swallowed without chewing is normally chemically digested in the stomach. — 0.61 19. Digestion also has an absorption dimension. 0.34 Note. Loadings < .30 suppressed. Tetrachoric correlation matrix used. SS loadings: PA1 = 2.60, PA2 = 2.08; cumulative variance ≈ .23. Scoring and Data Preparation Each item was scored dichotomously: correct responses received a score of 1 and incorrect responses 0 (Hammond, 1994). Omitted answers were treated as incorrect. Thus, total raw scores ranged from 0 to 20. For factor analysis, we computed a tetrachoric correlation matrix among the items. Tetrachoric correlations estimate the relationships between the underlying continuous propensities that lead to the dichotomous item responses, under the assumption of an underlying bivariate normal distribution for each item pair. These correlations are appropriate for binary data and provide a foundation for factor extraction. For Rasch modeling, we constructed a person–item response matrix (with rows as participants and columns as items). We examined the distribution of total scores and calculated the person separation reliability. To facilitate interpretation of Rasch results, we converted each student's logit ability estimate into a standardized T‑score (mean = 50, SD = 10). These transformed scores place students on a familiar scale while preserving the interval properties of the Rasch logit scale. Analytic Strategy We adopted a sequential analysis plan that combined classical test theory indices, factor analyses, and item response theory. All analyses were conducted using R (version 4.2.1) with relevant packages (e.g., psych, lavaan, eRm, and mirt). Descriptive statistics and item analysis. We first examined classical item statistics. For each item, we computed the proportion of students answering correctly (item difficulty) and the corrected item–total correlation (point-biserial correlation between the item and the total score excluding that item, often called item–rest correlation). These indices helped identify any potentially problematic items, such as those that were extremely difficult or easy (difficulty 0.80) or those that did not correlate with the overall test (item–total correlation < 0.20). Items showing extreme difficulty or poor discrimination were flagged for further scrutiny. Suitability for factor analysis. To ensure that factor analysis was appropriate for our data, we calculated the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and performed Bartlett's test of sphericity on the item correlation matrix. A KMO value ≥ 0.60 and a significant Bartlett's test (p < 0.001) would indicate that the data are factorable and justify proceeding with factor analysis. Exploratory factor analysis (EFA). Using the tetrachoric correlation matrix, we conducted EFA to explore the underlying factor structure without imposing a predetermined model. We employed principal axis factoring (which does not assume multivariate normality and is well-suited to non-normal or binary data) and used oblimin rotation (an oblique rotation) because we expected any factors to be correlated (Costello & Osborne, 2005 ). To decide how many factors to retain, we triangulated multiple criteria: parallel analysis (which compares empirical eigenvalues to those from random data), the scree test (visual identification of an "elbow" in the plot of eigenvalues), and Velicer's Minimum Average Partial (MAP) test. Notably, parallel analysis can tend to over-extract factors with dichotomous data due to sampling error, so we cross-checked its suggestions with the MAP and the nScree method (which aggregates several criteria). In our case, the MAP test favored a single factor whereas parallel analysis suggested as many as eight factors, and the nScree indicated two factors. Based on these results and theoretical expectations (that a general misconception trait might have subdomains), we decided to examine both one-factor and two-factor solutions. We considered an item to load saliently on a factor if its loading was ≥ 0.30 on that factor and it did not simultaneously load > 0.20 on another factor. We interpreted any extracted factors in light of the content and conceptual focus of the items grouping on them. Confirmatory factor analysis (CFA). We next tested the dimensionality indicated by EFA using CFA. We fit two CFA models with lavaan: a one-factor model (all 20 items loading on a single latent construct) and a two-factor model (items loading on two correlated latent factors, based on the EFA groupings). Given the dichotomous nature of the data, we treated items as ordered categorical variables and used the WLSMV estimator, which is robust for categorical outcomes. Factor variances were fixed to 1 for model identification. Model fit was evaluated with standard indices: CFI and TLI (values ≥ 0.90 or 0.95 for good or excellent fit) and RMSEA and SRMR (values ≤ 0.08 for acceptable fit, with ≤ 0.05 indicating close fit). We compared the one- and two-factor models on these indices. We also checked modification indices for any local misfit (e.g., suggesting correlated residuals) and considered whether allowing any additional covariance or cross-loading was substantively justified. In addition to overall fit, we calculated composite reliability (CR) and average variance extracted (AVE) for each factor in the two-factor model to assess internal consistency and convergent validity of the factors. Generally, CR > 0.70 and AVE > 0.50 are desired (Fornell & Larcker, 1981 ). We report these values and discuss whether the factors were sufficiently distinct (discriminant validity) or essentially measuring the same construct. Rasch modeling. Finally, we performed Rasch analysis by fitting a one-parameter logistic (1PL) model using the eRm package. This yielded estimates of item difficulty (the location of each item on the latent scale) and person ability for each student. All items were constrained to equal discrimination (slope) in this model. We examined item fit via Infit and Outfit mean square statistics; we considered values in the range 0.70–1.30 as indicative of acceptable fit. Items with fit statistics outside this range or with standardized fit residuals |z| ≥ 2.0 were flagged for review. (We note that with N = 412, the standardized fit statistic can be sensitive, so we emphasize mean-square fit and substantive content review of flagged items rather than rigidly dropping items on the basis of z alone.) We also examined person fit to identify any students with aberrant response patterns (e.g., high ability students getting many easy items wrong, or vice versa). Person separation reliability and the separation index were computed to evaluate the precision of measurement on the logit scale. We generated a Wright map (person–item map) to visualize the distribution of student abilities against item difficulties; this helps identify whether the test items appropriately target the range of student misconceptions (e.g., whether very high or very low ability ranges are underrepresented by items). Finally, we assessed local independence by computing Yen's Q3 statistic for residual correlations between item pairs. Item pairs with Q3 > 0.20 would suggest local dependence (Yen, 1984), potentially indicating redundant items or shared stimuli, but as reported below we found no such violations. Results Item Analysis and Descriptive Statistics Item difficulty (proportion of students answering correctly) ranged from 0.32 to 0.86. No item was extremely difficult or extremely easy by our criteria, indicating that the test included a mix of items of varying difficulty without floor or ceiling effects. The corrected item–total correlations (item discrimination indices) varied between 0.19 and 0.50, with a median of 0.33, suggesting that most items had satisfactory discrimination and were contributing positively to the total score. Two items (labeled x09 and x20) showed the lowest item–total correlations (approximately 0.19 and 0.16, respectively). Increased enzymes accelerate digestion was stated as item x09 (the misconception being that digestion can be sped up infinitely with the addition of enzyme). Item x20 dealt with the secretion of bile. However, despite their relatively low discrimination, no item had an item–total correlation below 0.15, and all items still demonstrated at least a moderate correlation with overall test performance. We therefore retained all 20 items for further analyses. The Cronbach's alpha for the 20 items was 0.77, which indicates acceptable internal consistency for a concept diagnostic scale. The Johnson & Morgan ( 2016 ) study also shows how this value yields confidence that the DSCS provides consistent outcomes across individual items since ‘the commonly recommended reliability bounds for classroom assessments’ suggests that the primary value should fall between ‘0.70 and 0.95’ (recall: group-level use). Suitability for Factor Analysis The DSCS data met key assumptions for factor analysis. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.86, which is well above the recommended minimum of 0.60 for a satisfactory factor analysis. Similarly, Bartlett's test of sphericity was highly significant (χ² = 2344.19, df = 171, p < 0.001), indicating that the item correlation matrix was not an identity matrix and contained sufficient structure for factor extraction. Together, these diagnostics confirmed that it was appropriate to proceed with exploring the factor structure of the DSCS. Exploratory Factor Analysis (EFA) Using principal axis factoring on the tetrachoric correlation matrix, we conducted an EFA to probe the latent structure. The initial unrotated factor solution showed that the first factor had an eigenvalue substantially larger than subsequent factors. The first factor alone accounted for 37.5% of the variance, while the second factor accounted for 8.4%. A parallel analysis (random eigenvalue comparison) suggested that more than one factor could be retained, but the results were inconclusive due to the tendency of parallel analysis to overestimate factors with dichotomous data. The scree plot revealed a noticeable elbow after the first factor, with a much smaller drop to the second, suggesting a dominant first factor. We also computed Velicer's MAP test, which indicated a single underlying factor. Balancing this evidence, we examined both a one-factor and a two-factor solution. Parallel analysis on tetrachoric correlations guided factor retention; the scree comparison is shown in Fig. 1 . In the one-factor EFA solution, all 20 items loaded positively on the general factor, with factor loadings ranging from 0.29 to 0.78. This single factor solution explained 45.9% of the total variance in item responses. In the two-factor solution (using oblimin rotation to allow correlation between factors), the cumulative variance explained increased to 55.3%. The two factors were interpretable and aligned with content themes. Factor 1, which we might label Digestive Processes and Chemical Digestion, included items emphasizing biochemical aspects of digestion (e.g., the role of enzymes, understanding of chemical vs. mechanical digestion, and nutrient absorption processes). For instance, item x13 (misconception about enzyme function) and item x16 (misconception about where digestion can occur simultaneously) loaded strongly on Factor 1 (loadings 0.74 and 0.62, respectively) and had minimal cross-loading on the other factor. Factor 2, which we might label Organs and Mechanisms, comprised items focusing on anatomical sequence and organ-specific functions (e.g., which organs perform which digestive roles, where digestion begins, how food moves). Items x05 (belief that most nutrient digestion happens only in the small intestine) and x06 (belief that digestion begins only in the stomach rather than the mouth) were among those loading on Factor 2. Cross-loadings between the two factors were generally low; no item had a secondary loading greater than 0.20. This suggests that, while a single overall misconception trait underlies performance, there are two meaningful clusters of items that provide a richer, more nuanced view of students' misconceptions. Confirmatory Factor Analysis (CFA) We next conducted CFA to compare a unidimensional model against the two-factor model indicated by the EFA. In the one-factor CFA, all 20 items were specified to load on a single latent factor representing overall misconception severity. This model achieved an acceptable fit to the data: CFI = 0.978, TLI = 0.975, RMSEA = 0.024, SRMR = 0.076. All factor loadings in this model were significant and positive, ranging from about 0.35 up to 0.75. However, the modification indices for the one-factor model suggested that a few pairs of items with very similar content (for example, item x10 about enzyme reuse and item x11 about enzyme function) had correlated residuals, hinting that a secondary grouping might improve model fit. We then specified a two-factor CFA model based on the EFA groupings: Factor 1 included the items related to digestive processes and enzymes, and Factor 2 included items related to organ functions and mechanisms. We allowed the two factors to correlate, as suggested by theory and the EFA oblimin rotation. The two-factor model provided an excellent fit: CFI = 0.997, TLI = 0.996, RMSEA = 0.009, SRMR = 0.070. These fit indices were substantially better than those of the one-factor model, indicating that modeling two latent factors improved the representation of the data. Factor loadings in the two-factor CFA ranged from 0.36 up to 0.74, with a median loading around 0.53. Each item loaded most strongly on its intended factor, and no cross-loadings were specified in the CFA. The two factors were moderately to strongly correlated (r = 0.704), which is consistent with the idea that while the DSCS can be viewed as tapping a single broad misconception construct, the two subdimensions are closely related. Given this high inter-factor correlation, the DSCS is essentially unidimensional in terms of measurement, though the two-factor solution offers some pedagogical interpretability (as discussed below). The two-factor model fit better than the one-factor alternative (Table 2 ). Standardized loadings for the retained two-factor solution appear in Table 3 , and the path diagram is presented in Fig. 2 . The latent factors were strongly correlated (r ≈ .70), supporting a single predominant construct with two interpretable facets. Table 2 Confirmatory Factor Analysis—Model Fit Indices (WLSMV) Model χ² df p CFI TLI RMSEA RMSEA 90% CI SRMR One-factor 211.81 170 .016 .972 .969 .024 .011–.034 .077 Two-factor 179.76 169 .271 .993 .992 .012 .000–.026 .072 Note. Robust (scaled) indices were more conservative (two-factor robust CFI = .844; robust RMSEA = .062 [.044–.078]). Table 3 Two-Factor CFA: Standardized Loadings Factor Item Std. loading F1 x05 0.469 F1 x06 0.510 F1 x07 0.638 F1 x12 0.729 F1 x13 0.365 F1 x14 0.491 F1 x16 0.524 F1 x17 0.646 F1 x18 0.583 F1 x20 0.245 F2 x01 0.458 F2 x02 0.376 F2 x03 0.483 F2 x04 0.395 F2 x08 0.596 F2 x09 0.348 F2 x10 0.318 F2 x11 0.697 F2 x15 0.424 F2 x19 0.335 Note. Latent correlation r(F1, F2) = .73. To further evaluate the two-factor solution, we calculated the composite reliability (CR) for each factor, finding CR ≈ 0.85 for Factor 1 and 0.81 for Factor 2. These values indicate good internal consistency for the items grouped in each factor. Factor 1 was 0.49 and Factor 2 was 0.45, with an average variance extracted (AVE) of two, which still is lower than the generally agreed upon 0.50 limit for convergent validity (Fornell & Larcker, 1981 ). This means that despite the fact that there is a considerable amount of variance in the captured items corresponding to each factor, almost half of the variance is not captured and that the remaining variance is a result of either error or uniqueness. However, given the relatively high correlation between the factors (and the modest number of items per factor), the somewhat lower AVE is not surprising. We considered whether any low-loading items could be removed to raise the AVE, but doing so would have reduced content coverage of the scale. On balance, we retained all items to preserve the diagnostic breadth of the DSCS. We conclude that the CFA provided strong support for a two-factor structure, while also affirming that a single overarching trait largely governs student performance. Importantly, removing the two previously flagged items (x09 and x20) and re-running the CFA did not substantially improve model fit; it actually slightly worsened some indices and left certain content areas underrepresented. Therefore, all 20 items were retained in the final instrument for subsequent Rasch analysis and interpretation. Rasch Modeling We fit a one-parameter Rasch model (1PL) to the 20-item dataset to further assess the measurement properties of the DSCS. The item difficulty estimates (denoted δ or β) ranged from − 0.85 logits (for item x09, which turned out to be one of the easiest items) to + 0.78 logits (for item x19, one of the most difficult items). This range of approximately 1.6 logits indicates a reasonable spread of item difficulty across the continuum of misconception severity. It was reasonable to arrange the items in levels of difficulty. The easier ones often related to more fundamental or widely taught ideas like pinpointing the beginning stages of digestion, while the harder ones were aimed at more subtle misunderstandings like the enzymes that get reused and the action of bile. Under a Rasch (1-PL) model, item difficulties and fit indices (infit/outfit mean squares and standardized z) are reported in Table 4 . The person–item (Wright) map is shown in Fig. 3 , and item characteristic curves (ICCs) for representative items are displayed in Fig. 4 . Table 4 Rasch (1PL) Item Difficulty and Fit Item Difficulty (logit) Infit MSQ Outfit MSQ x01 0.67 0.98 0.94 x02 0.03 1.05 0.99 x03 0.60 0.97 1.00 x04 -0.33 1.08 1.12 x05 -0.33 1.00 1.04 x06 -0.11 0.97 0.99 x07 -0.17 0.90 0.84 x08 -0.18 0.95 0.94 x09 0.86 1.03 1.18 x10 -0.61 1.11 1.12 x11 0.46 0.88 0.81 x12 -0.02 0.85 0.78 x13 0.53 1.07 1.06 x14 -0.16 1.00 0.99 x15 -0.44 1.07 1.08 x16 0.31 0.95 0.92 x17 -0.10 0.90 0.85 x18 0.41 0.93 0.93 x19 -0.78 1.07 1.10 x20 -0.60 1.14 1.19 Note. Difficulties are mean centered. Reference MSQ range .70–1.30. The person reliability in the Rasch analysis was 0.706. This reliability is analogous to Cronbach's alpha in classical test theory and suggests that the DSCS can reliably distinguish at least two to three broad levels of student ability (misconception severity) in our sample. The separation index, calculated as SD(θ)/SEM on the logit scale and related to reliability, was about 1.55, indicating that the test separates students into approximately 1.552 ≈ 2 strata of ability. A person reliability above 0.70 is acceptable for educational assessments of this type. All items showed good fit to the Rasch model. The Infit mean square statistics ranged from 0.85 to 1.19, and the Outfit mean squares were in a similar range, all well within the 0.7–1.3 guideline for acceptable fit. Thus, none of the items exhibited aberrant response patterns inconsistent with the expectations of the model. We have standardized fit residuals (z-statistics) that indicated a severe misfit with six items (x07, x10, x11, x12, x17, x20) with |z|≥ 2. However, statistically significant misfit should be use with caution. In this case, it is warranted because our sample is considerable (N > 400) and it is known these sample sizes influence Bond and Fox ( 2015 ) significant z values with minor deviations. Crucially, all six of these flagged items had mean-square fit statistics very close to 1 (well within the acceptable range), indicating that the magnitude of misfit was small in practical terms. Upon examining the content of these items, we found plausible reasons for their slight misfit: for example, item x12 ("Only chemical digestion takes place in the stomach") may have confused some students who did not clearly distinguish chemical from mechanical digestion, and item x10 ("Enzymes are lost after digestion") touches on knowledge (enzyme reuse) that might not be well emphasized in the curriculum. Such items might behave a bit unpredictably (leading to more variability in responses), but this does not invalidate them. Rather than removing these items, we decided to keep them in the scale but note in our recommendations that they could be rephrased in future revisions to reduce ambiguity. Overall, misfit in this context does not necessarily imply the item is useless; it may still target a valuable misconception concept (Sijtsma, 2009 ). We also checked the assumption of local independence among items using Yen's Q3 residual correlations. The largest observed residual correlation between any pair of items was 0.18, and no item pair exceeded the 0.20 threshold for concern. This indicates that after accounting for the primary Rasch trait, item responses were essentially independent – in other words, there was no evidence that any two items shared additional common factors or that answering one item influenced responses to another. The DSCS items each appear to contribute unique information about the underlying misconception trait. In terms of person fit, about 4.2% of students had outfit or infit statistics beyond the usual cut-off (e.g., outfit > 1.5), suggesting their response patterns were somewhat unusual. On inspecting these cases, we found they corresponded to instances where students might have been randomly guessing or not fully engaged (for example, some of these students had nearly equal numbers of correct and incorrect answers in a sporadic pattern or left multiple items blank). These few cases did not substantially affect the overall analysis and were not removed, but they underscore the importance of ensuring student motivation and understanding of instructions when administering the test. The Wright map (person–item map) produced by the Rasch analysis provides a visualization of the scale's targeting. We observed that the bulk of student ability estimates clustered slightly above the mean item difficulty. In other words, on average, students in our sample found the test slightly easy (many students scored in the upper half of the raw score range). The items tended to target low to moderate levels of misconception severity; fewer items were located at the very high end of the ability scale. This results in fewer very difficult items to challenge the most knowledgeable students (those with the least misconceptions). Consequently, high-performing students (with very few misconceptions) may achieve near-perfect scores, making it harder to differentiate among them based on this test. This ceiling effect suggests that adding a few more challenging items, perhaps items that probe deeper biochemical or physiological details of digestion, could improve the test's ability to measure the upper end of understanding and thereby increase person reliability. Given that the Rasch analysis supported treating the DSCS as essentially unidimensional (one dominant trait underlying performance) and the person reliability was adequate, we proceeded to use the total Rasch score (ability estimate) for each student as the primary measure of misconception severity. For practical use in classrooms, we also converted the continuous Rasch ability estimates into more interpretable metrics. As mentioned, we standardized the logits into T‑scores (mean = 50, SD = 10). An average student in our sample thus would have a T-score around 50; higher T-scores indicate more severe misconceptions (lower understanding), and lower T-scores indicate fewer misconceptions (higher understanding). Additionally, we established performance bands based on percentile cut-offs to facilitate interpretation by teachers. For example, we defined approximate score bands as follows: raw scores of 0–4 correct (out of 20) as Very Low understanding (indicating pervasive misconceptions), 5–7 correct as Low, 8–11 as Average, 12–14 as High, and 15–20 as Very High. These categories correspond roughly to certain regions on the ability scale (e.g., the Very Low band roughly corresponds to ability estimates below about − 1.5 logits, and the Very High band corresponds to abilities above + 1.5 logits, based on our data distribution). A teacher can use these bands to get a general sense of a student's level: for instance, a student in the Very Low band likely holds many fundamental misconceptions about digestion and may need intensive review of basic concepts, whereas a student in the Very High band has mastered most concepts and might benefit from enrichment activities. It must be stressed that these support bands are meant to be descriptive and not to stigmatize students. In this case support bands must be used along with other analysis at the item level. For example, a student in the Average band (around 8 to 11 correct) could still hold certain misconceptions (based on the particular items they got wrong) that need to be fixed. Conversely, a student in the "High" or "Very High" band might still have one or two stubborn misconceptions that could be uncovered through class discussion or open-ended questions. We advise educators to interpret the total score as a starting point and then delve into which items were missed to target instruction appropriately. Because the two factors identified by CFA were so highly correlated and the subscale reliability for each was low (Cronbach's α approximately 0.50 for Factor 1 and 0.32 for Factor 2), we do not recommend reporting separate subscale scores for each factor. Instead, teachers and researchers should interpret the DSCS as yielding a single overall misconceptions score, supplemented by an item-level profile to indicate whether a student's misconceptions tend to cluster more in process-related or anatomy-related areas. Discussion This study sought to validate the 20‑item DSCS using robust psychometric methods. As a whole, the DSCS showed strong technical attributes for a concept inventory. The instrument showed sufficient reliability (Cronbach's α = 0.77; Rasch person reliability = 0.706) and exhibited range for student understanding mastery. Both EFA and CFA indicated that student responses were driven primarily by a single latent trait (general understanding of digestion concepts vs. misconceptions). However, a two‑factor model provided a marginally better fit in CFA and offered pedagogical insight by grouping misconceptions into two interpretable facets (digestive processes vs. organ functions). Rasch modeling confirmed that items functioned appropriately (no significant misfit after accounting for content considerations) and that local item independence was satisfied. The person–item map revealed that the scale was somewhat easier for our sample than expected, suggesting the need for a few more difficult items to better challenge high-understanding students. Taken together, these results support using the DSCS as a valid and reliable instrument to diagnose misconceptions about digestion among 7th‑grade students. Interpretation of Factor Structure Although the two‑factor CFA model provided the best fit statistically, the high correlation between factors (approximately 0.70) suggests that the DSCS largely measures a single underlying construct: the overall severity of a student's misconceptions about the digestive system. The two factors can be thought of as emphasizing different aspects of this construct. One factor highlights process-related misconceptions (e.g., misunderstanding chemical vs. mechanical digestion, enzyme action, and how nutrients are broken down), while the other centers on structure-related misconceptions (e.g., confusion about which organs perform certain functions, or the path food takes through the digestive system). From an instructional perspective, these facets are useful. Teachers may find it valuable to know, for example, if a class as a whole is weaker in understanding digestive processes as opposed to anatomy. In our data, many students showed consistent errors on the enzyme-related items (Factor 1), indicating a potential curriculum gap in teaching the role of enzymes or the chemical nature of digestion. If a teacher administers the DSCS and notices widespread difficulties on those items, they could plan additional lessons or demonstrations focusing on enzyme function (e.g., an experiment showing how enzymes break down food or the effect of pH on enzyme activity). Conversely, if students struggled more with items like X05 and X06 on Factor 2 (organ functions and sequences), a teacher might spend time having students construct annotated digestive system diagrams or perform a simulation of the digestive process, to reinforce what happens in each organ. That said, because each subscale (Factor 1 and Factor 2) was short (approximately 10 items each) and their internal consistencies were quite low (α ~ 0.50 or below), we caution against reporting separate scores for the two factors. Subscale scores at that reliability would be unstable and potentially misleading (Nunnally, 1978). The safer approach is to use the total score as the primary metric of misconceptions and then use the pattern of item responses to glean insights into the types of misconceptions. In practice, an educator can review which specific questions a student or class answered incorrectly to diagnose whether those misconceptions relate more to processes or to structures, without needing to formally compute two subscale totals. This approach balances psychometric rigor (by using a well-defined unidimensional score) with pedagogical utility (by interpreting item patterns). Rasch Interpretation and Item Functioning Rasch analysis offered several valuable perspectives on the performance of items and the test. To begin with the spread of the item difficulties suggest that the DSCS covers narrow and moderate levels of comprehension. There were items that most students found easy (indicating very common misconceptions that almost everyone held) and items that were difficult (misconceptions that only the least-informed students held). However, the lack of extremely difficult items suggests that our instrument might not be sufficiently challenging for the top quartile of students who have very few misconceptions. In a class of mostly high-achieving students, the DSCS could yield many high scores with little variation. Adding 2–3 more difficult items that target subtle or higher-order misconceptions (for instance, items about hormonal regulation of digestion or integration of digestion with other body systems like circulatory or endocrine) could improve the instrument's ability to discriminate among students at the upper end of understanding. Increasing the spread of item difficulties in this way would likely also raise the person reliability, as more information is gathered about high-performing students. Second, while all items were within acceptable fit ranges, the fact that six items triggered the statistical misfit criterion (|z| ≥ 2) invites reflection. After examining these items qualitatively, we suspect that the wording or cognitive demand of some items could be refined. For instance, students who fail to notice that mechanical digestion (churning) also happens in the stomach may get confused with the statement in item x12 ("Only chemical digestion takes place in the stomach"). Perhaps, for the sake of accuracy, focusing the misconception would be better if it was clarified to “No mechanical digestion occurs in the stomach.” Item x10 ("Enzymes are lost after digestion") touches on the concept of enzyme reusability, a topic that might not be fully covered in 7th grade, so some students might have answered incorrectly simply due to lack of instruction rather than a misconception per se. In future revisions of the DSCS, we recommend revisiting such items. The target audience should be students who are prepared to face the misconceptions the curriculum defines. The items should be retained rather than removed because they do target important misconceptions. Little changes could enhance their performance without compromising their content. Third, the Rasch analysis affirmed that each item contributes unique information (local independence) and that a single score is defensible. We found no evidence of item clustering effects or multi-dimensionality in the residuals. This justifies the practice of reporting a total score (or Rasch measure) for each student. It also supports using the Rasch difficulty measures for each item to understand which misconceptions are easier or harder for students to avoid. For instance, the easiest item (x09) was one that nearly all students got right, suggesting that the misconception it tests (possibly a trivial or less common one) is not widespread. In contrast, the hardest item (x19) points to a misconception that even many high-performing students held, identifying a particularly problematic area of understanding. Comparison with Previous Research Our findings align with earlier studies on science misconceptions that have often found a largely unidimensional structure underlying students' incorrect belief. For example, instruments measuring misconceptions in other domains like photosynthesis or genetics have similarly found that while multiple factors can sometimes be identified, those factors tend to be highly correlated, reflecting one general proficiency (or lack thereof) in the domain (Haslam & Treagust, 1987 ; Levy Nahum et al., 2010 ). In the context of digestive system misconceptions, relatively few published scales exist. One prior study in Turkey by Uğur ( 2010 ) developed a two-tier test for high school students on the digestive system and reported a Cronbach's alpha of 0.71 for their 16-item test, which is comparable to the reliability of 0.77 we obtained for the DSCS. This suggests that the DSCS's reliability is on par with similar instruments, despite our inclusion of more items and more advanced psychometric analysis. Notably, many earlier misconception tests have relied solely on classical test theory or exploratory factor analysis, without the additional confirmation and item-level scrutiny that Rasch modeling provides. This sometimes led to ambiguity in the interpretation of dimensions or inflated views of reliability. By employing both factor analysis and Rasch modeling, our study demonstrates the value of a mixed psychometric approach. The factor analyses allowed us to see overarching patterns and potential subdimensions, while the Rasch analysis confirmed the essential unidimensionality and helped identify specific items that could be improved. We believe this dual approach provides a more nuanced validation than either method alone, a practice that could be beneficially applied in validating concept inventories in other science domains. In terms of magnitude of misconceptions, the average score in our sample and the person ability distribution indicate that misconceptions about digestion are still prevalent among 7th graders, despite digestion being a standard topic in the curriculum. This echoes the observations of other researchers who have found that significant proportions of students harbor misconceptions even after instruction (Özkan, 2017). Our high factor correlation (r ~ 0.70) also mirrors findings from concept inventories in other areas of biology and chemistry where factors were identified but were not truly independent (e.g., different facets of understanding in chemical bonding were found to correlate strongly: Levy Nahum et al., 2010 ). In practice, many researchers in science education end up reporting a single overall score on such inventories (even if a factor analysis suggests multiple factors) because that overall score tends to be the most reliable and interpretable measure of student understanding (or misunderstanding). Our results support a similar approach for the DSCS. Implications for Research and Practice The DSCS provides teachers and researchers with a psychometrically sound tool for diagnosing students' misconceptions about digestion and tailoring instruction accordingly. For classroom practitioners, the test covers multiple aspects of digestion, allowing them to pinpoint whether student misunderstandings cluster around certain themes. For instance, if a teacher finds that many students missed questions related to enzymes and chemical reactions (Factor 1 items), this would signal a need to reinforce lessons on how enzymes work (perhaps through interactive labs or analogies that make the invisible process more concrete). On the other hand, if students struggled more with items about the path of food or organ functions (Factor 2 items), the teacher might use more physical models or ask students to build a flowchart of the digestive process to solidify the sequence and roles of each organ. The derived score bands and T-scores can help educators interpret individual and class performance in an intuitive way. A teacher might say, "Most of my class scored in the Average band, with a few in Low and a few in High," suggesting the needs of the class rather quickly. However, we stress the need to go deeper than the total score. We ask that teachers analyze the results multiple items and, whenever possible, address specific items with the class after the test to remediate the specific errors made. Research on conceptual change suggests that explicitly addressing misconceptions (for example, using refutation texts or class discussions where misconceptions are brought to light and examined) is necessary for many students to change their thinking (Duit & Treagust, 2003 ; Tippett, 2010 ). The DSCS can act as the diagnostic step in this process: it identifies the misconceptions, which the teacher can then deliberately refute and replace with correct conceptions through instruction. For researchers and test developers, our study illustrates a holistic approach to instrument validation. The use of item-total correlations, factor analyses (EFA/CFA), and Rasch modeling of These elements were synthesised together. Multiple approaches provides more complete proof. Subsequent researchers creating partial understanding scales in science education may adopt a similar multi-pronged strategy to evaluate construct validity, check for dimensionality, and ensure the scale’s grade. The use of EFA, CFA, and Rasch in this work illustrates how EFA proposed a structural framework, CFA evaluated the framework using stringent fit standards, and Rasch provided item-level diagnostics and an IRT-based reliability and measurement invariance framework. This kind of thorough validation is especially important if such instruments are to be used for research or high-stakes decisions. Our findings also underscore the importance of item difficulty distribution. Many diagnostic tests in science either lack enough easy items (leading to floor effects where many students score zero or very low) or lack enough hard items (leading to ceiling effects). The DSCS was designed to include a range, but as discussed, it could benefit from a few more difficult items. Achieving a balanced item set that measures across the spectrum of understanding enables better discrimination among all students, not just those in the middle. This has implications for test design: during development, one should aim to include some items that even the top students will struggle with (to probe the limits of their understanding) and some items that even the lowest students will get right (to capture partial understanding and engage those students). Another implication pertains to formative use of such assessments. The DSCS is not merely a research tool; it can be used formatively in the classroom. For example, a teacher might administer the DSCS as a pre-test at the start of a unit on human biology to gauge what misconceptions exist, then tailor teaching strategies accordingly, and perhaps administer it again as a post-test to measure conceptual change. Because the DSCS is multiple-choice, it can be graded quickly (or even electronically), and because it's relatively short (20 items), it can fit into a single class period. The use of learning analytics or even simple item analysis can provide immediate feedback. If integrated into a digital platform, a teacher could see which misconceptions are most common in a class and even provide individual students with targeted follow-up materials addressing the specific misconceptions they showed. For curriculum designers and education policy makers, the DSCS results across many classrooms could highlight systemic areas of weakness. For instance, if data from many schools reveal that the majority of students misunderstand the role of enzymes or have the misconception that "digestion occurs only in the stomach," this might indicate that the standard curriculum or textbooks are not effectively dispelling those notions. Curriculum developers could respond by incorporating more explicit content or activities about these trouble spots. On a larger scale, education reform efforts that emphasize inquiry and conceptual understanding might use instruments like the DSCS to measure whether reforms are reducing misconception prevalence. If, say, a new inquiry-based unit on digestion is implemented, the DSCS could serve as an evaluative tool to see if students emerging from that unit hold fewer misconceptions than students taught with a traditional approach. Limitations and Future Directions Several limitations of this study should be acknowledged. First, our sample was drawn from a single metropolitan region in Turkey, which may limit the generalizability of the findings. Education systems, teaching approaches, and prior knowledge can vary significantly across regions and countries. What holds true for our population may not exactly replicate elsewhere. We suggest that future research replicate the validation of the DSCS with diverse samples, including students from different countries, rural areas, or educational systems. Such studies would strengthen the external validity of the scale. They could also examine whether the factor structure remains the same across contexts or whether, for instance, different misconceptions are prominent in different cultures. Second, we administered the DSCS as a paper–pencil test under teacher supervision. While this approach reflects typical classroom practice (enhancing ecological validity), it does not leverage the possibilities of technology. An interesting future direction would be to develop a computer-based or online version of the DSCS. A digital format could provide immediate feedback to students and teachers and potentially adapt to student responses. In fact, there is growing interest in computerized adaptive testing in education. An adaptive version of the DSCS could present students with items matched to their estimated misconception level, challenging stronger students with harder items and giving struggling students more basic ones, hus maintaining engagement and efficiency. Such an adaptive DSCS would require a larger item pool and careful calibration, but it could shorten test time while preserving measurement precision (by not asking students questions that are far too easy or far too hard for them). Moreover, a digital DSCS could easily be integrated into learning management systems, allowing teachers to track conceptual change over time for each student and providing rich data for researchers on how misconceptions evolve with successive learning interventions. Third, we did not formally examine Differential Item Functioning (DIF) in this study. Although our Rasch analysis did not find obvious overall ability differences by gender (and an informal analysis using Rasch trees did not split by gender), we cannot rule out that some items might function differently for boys versus girls, or for other subgroups (e.g., students of different socioeconomic status or different science achievement levels). For example, certain misconceptions might be more common among one gender due to differences in interest or attention in biology. Future research using methods like the Mantel–Haenszel procedure, logistic regression DIF analysis, or multigroup IRT could investigate whether any DSCS items show bias. If any DIF is detected (say, an item is systematically easier for boys than girls despite equal overall understanding, or vice versa), that item could be revised or used with caution. So far, we have no specific indication of bias, but a thorough check would be prudent, especially if the test is used in high-stakes contexts. Fourth, as noted in the results, some items were flagged for potential misfit. While we have rationales for keeping them, it remains that these items could be improved. Cognitive interviews or think-aloud protocols with students focusing on these specific items would be a useful next step. By asking students to explain their thought process on, say, item x12 or x10, we could learn whether the phrasing is misleading or if students are interpreting the question in unintended ways. This qualitative feedback could directly inform how to rewrite those items. Even a well-validated instrument can be refined further, and our analysis points to a few candidates for improvement. Lastly, the DSCS in its current form lacks items tapping the very high end of understanding (the most advanced content or the most subtle misconceptions). As discussed, adding items in that range would likely improve the instrument's discrimination among top students and raise the ceiling. In practice, these could be items that incorporate minor details or exceptions that only students with a truly robust understanding would know. For example, an item might test understanding of how the nervous system regulates digestion or a misconception about how different macronutrients are processed differently. We foresee that including a handful of such items could extend the scale's utility for advanced or older students, such as 8th or 9th graders, while still being answerable by well-taught 7th graders. In terms of future research avenues, beyond validation and improvement of this instrument, there is the broader question of how using the DSCS (or similar tools) influences teaching and learning. One could design a study where some teachers use the DSCS to inform their teaching (essentially employing it as a formative assessment tool) and compare those classes to classes that did not use the diagnostic test. Would explicit diagnosis and remediation of misconceptions lead to significantly greater learning gains? Intuition and prior conceptual change literature suggest yes (because teaching would be more targeted), but empirical evidence would be valuable. Moreover, researchers might explore longitudinal use of the DSCS: for instance, using it at the beginning of the school year and at the end to measure how students' misconceptions shift after a year of instruction not just in digestion but in related topics like metabolism. A related idea is developing parallel forms of the DSCS to allow repeated testing without practice effects, given that repeating identical items can cue students to correct answers if they remember the test. Conclusion This study provides robust evidence for the validity and reliability of the 20‑item Digestive System Concept Scale as a diagnostic tool for student misconceptions. Through rigorous analyses that combined factor analysis and Rasch modeling, we demonstrated that the DSCS is essentially unidimensional, with two strongly correlated facets that offer interpretive value. The scale can reliably differentiate students by their misconception severity and provides detailed diagnostic information at the item level. The DSCS thus serves as a powerful instrument for both researchers and educators in the effort to understand and address misconceptions in science education. In synthesizing the broader implications of our findings, the DSCS emerges not only as a test of student knowledge but also as a platform for improving science teaching, informing curriculum design, and guiding professional development. Continuing to refine the instrument (for example, by revising ambiguous items and adding more challenging ones), extending its use across different cultures and languages, and integrating it into adaptive digital learning systems will further amplify its impact. Ultimately, tools like the DSCS contribute to a more nuanced understanding of learning by making misconceptions visible. They help us see that misconceptions are not roadblocks but rather stepping‑stones on the path to deeper understanding: if assessments, instruction, and content are constructively aligned to confront and replace misconceptions, we can foster enduring conceptual change in our students. Declarations Ethics approval and consent to participate: The study protocol was reviewed and approved by the Erciyes University [ Non-Interventional Research Ethics Committee], Kayseri, Turkey, on [08/08/2013]. All procedures complied with the Declaration of Helsinki and relevant regulations. [ Written informed consent was obtained from all participants]. References Boone WJ, Scantlebury K (2006) The role of Rasch analysis in educational research. Res Sci Educ 36(4):287–298. https://doi.org/10.1007/s11165-006-9026-4 Bond TG, Fox CM (2015) Applying the Rasch model: Fundamental measurement in the human sciences (3rd ed.). Routledge. https://doi.org/10.4324/9781315814698 Chen W-H, Thissen D (1997) Local dependence indexes for item pairs using item response theory. J Educational Behav Stat 22(3):265–289. https://doi.org/10.3102/10769986022003265 Chandrasegaran AL, Treagust DF, Mocerino M (2007) The development of a two-tier multiple-choice diagnostic instrument for evaluating secondary school students’ ability to describe and explain chemical reactions using multiple levels of representation. Chem Educ Res Pract 8(3):293–307. https://doi.org/10.1039/B7RP90006F Costello AB, Osborne JW (2005) Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assess Res Evaluation 10(7):1–9. https://doi.org/10.7275/jyj1-4868 diSessa AA (1993) Toward an epistemology of physics. Cognition Instruction 10(2–3):105–225. https://doi.org/10.1080/07370008.1993.9649023 Dry HA (1998) Student misconceptions in science: The human digestive system (Master’s thesis, SUNY Brockport) Duit R, Treagust DF (2003) Conceptual change: A powerful framework for improving science teaching and learning. Int J Sci Educ 25(6):671–688. https://doi.org/10.1080/09500690305016 Fabrigar LR, Wegener DT (2011) Exploratory factor analysis. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199734185.001.0001 Flora DB, Curran PJ (2004) An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychol Methods 9(4):466–491. https://doi.org/10.1037/1082-989X.9.4.466 Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39–50. https://doi.org/10.1177/002224378101800104 Gil-Perez D, Carrascosa J (1990) What to do about science misconceptions. Sci Educ 74(5):531–540. https://doi.org/10.1002/sce.3730740504 Griffard PB, Wandersee JH (2001) The two-tier instrument on photosynthesis: What does it diagnose? Int J Sci Educ 23(10):1011–1031. https://doi.org/10.1080/09500690010025074 Gul S, Yilmaz M, Gul A (2024) Identification of the students’ misconceptions about the digestive system. Sci Insights Educ Front 21(2):3409–3434. https://doi.org/10.15354/sief.24.or561 Hammer D (2000) Student resources for learning introductory physics. Am J Phys 68(S1):52–S59. https://doi.org/10.1119/1.19520 Haslam F, Treagust D (1987) Diagnosing secondary students’ misconceptions of photosynthesis and respiration in plants using a two-tier multiple choice instrument. J Biol Educ 21(3):203–211. https://doi.org/10.1080/00219266.1987.9654897 Hewson MG, Hewson PW (1983) Effect of instruction using students’ prior knowledge and conceptual change strategies on science learning. J Res Sci Teach 20(8):731–743. https://doi.org/10.1002/tea.3660200804 Johnson RL, Morgan GB (2016) Survey scales: A guide to development, analysis, and reporting. Guilford Press Kaiser HF (1974) An index of factorial simplicity. Psychometrika 39(1):31–36. https://doi.org/10.1007/BF02291575 Klem L (2000) Structural equation modeling. In: Grimm L, Yarnold P (eds) Reading and understanding multivariate statistics. American Psychological Association, pp 227–286 Leonard MJ, Kalinowski ST, Andrews TC (2014) Misconceptions yesterday, today, and tomorrow. CBE—Life Sci Educ 13(2):179–186. https://doi.org/10.1187/cbe.13-12-0244 Levy Nahum T, Mamlok-Naaman R, Hofstein A, Taber KS (2010) Teaching and learning the concept of chemical bonding. Stud Sci Educ 46(2):179–207. https://doi.org/10.1080/03057267.2010.504548 Lin S-W (2004) Development and application of a two-tier diagnostic test for high school students’ understanding of flowering plant growth and development. Int J Sci Math Educ 2(2):175–199. https://doi.org/10.1007/s10763-004-6484-y Lin S, Chang Y, Liew K, Chu C (2016) Effects of concept map extraction and a test-based diagnostic environment on learning achievement. Br J Edu Technol 47(4):649–664. https://doi.org/10.1111/bjet.12250 Masters GN (1988) Item discrimination: When more is worse. J Educ Meas 25(1):15–29. https://doi.org/10.1111/j.1745-3984.1988.tb00288.x Padgett RN, Morgan GB (2020) Using the eRm package for Rasch modeling. Measurement: Interdisciplinary Res Perspect 18(3):163–176. https://doi.org/10.1080/15366367.2020.1732155 Posner GJ, Strike KA, Hewson PW, Gertzog WA (1982) Accommodation of a scientific conception: Toward a theory of conceptual change. Sci Educ 66(2):211–227. https://doi.org/10.1002/sce.3730660207 Pritchard A (2017) Ways of learning: Learning theories for the classroom (4th ed.). Routledge. https://doi.org/10.4324/9781315460611 Rasch G (1960) /1980 Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research. (Expanded English ed., 1980, University of Chicago Press). https://doi.org/10.7208/chicago/9780226706458.001.0001 Roswati N, Rustaman N, Nugraha I (2019) The development of science comic in human digestive system topic for junior high school students. Journal of Physics: Conference Series, 1318 (1), 012052. https://doi.org/10.1088/1742-6596/1318/1/012052 Sijtsma K (2009) On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika 74(1):107–120. https://doi.org/10.1007/s11336-008-9101-0 Smith JP, diSessa AA, Roschelle J (1994) Misconceptions reconceived: A constructivist analysis of knowledge in transition. J Learn Sci 3(2):115–163. https://doi.org/10.1207/s15327809jls0302_1 Suprapto N (2020) Do we experience misconceptions? An ontological review of misconceptions in science. Stud Philos Sci Educ 1(1):1–9. https://doi.org/10.33550/spsed.2020.005 Taber KS (2006) Beyond constructivism: The progressive research programme into learning science. Stud Sci Educ 42(1):125–184. https://doi.org/10.1080/03057260608560222 Tippett CD (2010) Refutation text in science education: A review of two decades of research. Int J Sci Math Educ 8(4):951–970. https://doi.org/10.1007/s10763-010-9203-x Treagust DF (1988) Development and use of diagnostic tests to evaluate students’ misconceptions in science. Int J Sci Educ 10(2):159–169. https://doi.org/10.1080/0950069880100204 Uğur UK (2010) The determination of the high school student’s misconceptions related with the digestive system by two tier test. Master thesis, Selçuk University, Konya Vosniadou S (2020) Students’ misconceptions and science education. In Oxford Research Encyclopedia of Education . https://doi.org/10.1093/acrefore/9780190264094.013.1166 Wright BD, Mok MMC (2004) An overview of the family of Rasch measurement models. In: Smith EV, Smith RM (eds) Introduction to Rasch measurement. JAM, pp 1–24 Additional Declarations The authors declare no competing interests. Supplementary Files analysis.qmd Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7707356","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":520221978,"identity":"ba471c49-092d-4146-99fe-780197d4b15d","order_by":0,"name":"Fatih 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13:03:11","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151019,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7707356/v1/93d7e0e80d5cf164cfbdb7c6.html"},{"id":92416335,"identity":"5c140808-f775-4495-9bd4-6b0da02ede3c","added_by":"auto","created_at":"2025-09-29 13:27:11","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":215916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eParallel analysis scree\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7707356/v1/32087778c4001a24ff2838f9.jpeg"},{"id":92413761,"identity":"8043d221-4353-4cca-b018-3d834ac9f284","added_by":"auto","created_at":"2025-09-29 13:03:11","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":751004,"visible":true,"origin":"","legend":"\u003cp\u003eTwo-factor CFA path diagram.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7707356/v1/24e3f4cf0a39b8dffbe37f37.jpeg"},{"id":92413751,"identity":"d809a491-f5b0-4e8d-a153-b52d0e9ee93c","added_by":"auto","created_at":"2025-09-29 13:03:11","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":443496,"visible":true,"origin":"","legend":"\u003cp\u003ePerson–item map from Rasch analysis.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7707356/v1/5e8fb17f34cc46a430150c6b.jpeg"},{"id":92413753,"identity":"7fa31be7-8777-4b90-a0ec-2ca8ea887a8a","added_by":"auto","created_at":"2025-09-29 13:03:11","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":187448,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRepresentative item characteristic curves (ICCs) for three items showing how the probability of a correct response varies across ability θ.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7707356/v1/c3492a696facad99f87ef089.jpeg"},{"id":92416616,"identity":"c901b6ba-5a5d-493b-9cc8-d2ad35365625","added_by":"auto","created_at":"2025-09-29 13:35:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2661120,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7707356/v1/453c9c10-206e-4b5b-984b-e74d74b17e62.pdf"},{"id":92414431,"identity":"2f069aab-436a-4618-a852-56187c68cc3f","added_by":"auto","created_at":"2025-09-29 13:11:11","extension":"qmd","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13887,"visible":true,"origin":"","legend":"","description":"","filename":"analysis.qmd","url":"https://assets-eu.researchsquare.com/files/rs-7707356/v1/59f4e415329e2de958f30f38.qmd"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eValidation of the Digestive System Concept Scale for 7th‑Grade Students: Integrating Factor Analysis and Rasch Modeling\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHumans construct knowledge by integrating new information with their existing cognitive structures (Pritchard, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). When a learner's prior conceptions clash with instructional materials, misconceptions arise, but resilient ideas that can obstruct understanding and hinder further learning (Duit \u0026amp; Treagust, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Misconceptions in science are well‑documented and notoriously persistent across age groups and domains (Smith et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Leonard et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For instance, in biology, students tend to think that plants absorb food in the soil or that a human being breathes in oxygen and breathes out only carbon dioxide. These types of myths do not only misrepresent a learner\u0026rsquo;s understanding of the phenomena of science, but also interfere with the learner\u0026rsquo;s ability to think in an integrated manner, thus, stifling learning (Vosniadou, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWithin the realm of human physiology, the digestive system is an area where misconceptions flourish. Previous studies have identified that middle‑school and high‑school students believe digestion occurs exclusively in the stomach, that chemical and physical digestion never occur concurrently, or that nutrients are completely absorbed in a single organ (Gul et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Uğur, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Educators occasionally reinforce analogous misconceptions when textbooks present vague explanations or when their own mastery of the subject matter is inadequate (Hewson \u0026amp; Hewson, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Such inaccuracies endure within undergraduate cohorts as well as among pre-service teachers, thereby revealing the institutional rather than the incidental character of the difficulty (Griffard \u0026amp; Wandersee, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Dry, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Recognizing and addressing students' misconceptions is therefore a crucial task for science educators (Gil‑Perez \u0026amp; Carrascosa, 1990; Suprapto, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To do so effectively requires reliable tools that can diagnose misconceptions and monitor conceptual change over time.\u003c/p\u003e\u003cp\u003eAssessing misconceptions poses several challenges. Misconceptions are often nuanced and context‑dependent; thus, instruments must be carefully designed to capture variations in understanding (Treagust, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Because correct responses may not always reflect deep conceptual understanding, some students may guess or memorize definitions, many researchers advocate for two‑tier or two‑stage diagnostic tests that require students not only to select an answer but also to justify their choice or to select a reasoning statement (Chandrasegaran et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lin, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The equal measure of content validity, diagnostic cognitive data, and psychometric rigor is non negotiable in every form of sound measurement. However, outlining the more recent misconception scales demonstrates the use of validity and reliability in the scales is fundamentally lacking. Even the scales of internal reliability, factor structure, and content and construct validity were found to be low and exceedingly poor affirming sets of deficiencies regarding validation (Johnson \u0026amp; Morgan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This study addresses these shortcomings by using modern psychometric techniques, factor analysis and Rasch modeling, to evaluate the DSCS as a diagnostic instrument.\u003c/p\u003e\n\u003ch3\u003eMisconceptions in Digestive System Education\u003c/h3\u003e\n\u003cp\u003eNumerous research have documented the misconceptions concerning the digestive system. Dry (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) showed that students do not see the contribution of the mouth as an organ of digestion, seeing the liver as a digestion organ. Gul et al. (2015) showed that even after being taught, a student may hold the misconception that the entire digestion process happens in the small intestine or may confuse the mechanical and chemical stages of digestion. Recent studies have pointed out the inability to explain the functions of an enzyme, the contraption of bile, and absorption tissue (Lin et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Roswati et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These issues may stem from a lack of appreciation of the banal day to day activities and an oversimplistic textbook portrayal. Digestion is often illustrated as a simplistic and unnecessarily simplistically routed through the stomach. Such misconceptions may arise from the cognitive load of complex phenomena such as the process of peristalsis, hydrolysis and the absorption of nutrients (Karpudewan et al., 2017). Such misconceptions necessitate tests that target poorly conceived notions instead of simply gauging lack of depth in ideas.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAssessing Misconceptions: Psychometric Considerations\u003c/h2\u003e\u003cp\u003eThe tools designed to assess a person\u0026rsquo;s misconceptions must satisfy certain requirements grouped under Content Validity \u0026ndash; reflecting the relevant areas (digestive systems, organs, or even digestive chemical processes). Construct Validity entails relevant scaling misconceptions appropriate to the concept (Padgett \u0026amp; Morgan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As reliability, which is often evaluated with the person separation reliability in Rasch analysis or in Cronbach\u0026rsquo;s alpha, She (reliability) tells us the accuracy and stability of the tool (Sijtsma \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In terms of reliability, α\u0026thinsp;\u0026ge;\u0026thinsp;0.70 is acceptable highly desirable, while almost perfect reliability α\u0026thinsp;\u0026gt;\u0026thinsp;0.90 is tells it is possible that we have too many items to measure the same thing (Kaiser \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). \u0026iquest;With the use of factor analysis, we can better understand the axes of a the provided set of values. EFA (exploratory factor analysis) is used to identify latent structures without pre-defined frameworks, while CFA (Confirmatory) is used to validate pre-determined propositions (Fabrigar \u0026amp; Wegener, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Klem, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In multi-valued or Likert\u0026rsquo;s scale survey tools, it is suggested that polychoric correlations have to be used with weighted least squares estimators to filter to the ordinal values (Flora \u0026amp; Curran, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In the case of a dichotomous variable, the use of tetrachoric correlations is suggested. Fit indices such as the comparative fit index (CFI), Tucker\u0026ndash;Lewis index (TLI), and root mean square error of approximation (RMSEA) provide general benchmarks for model fit: values of CFI/TLI\u0026thinsp;\u0026ge;\u0026thinsp;0.90 and RMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.08 are often considered indicators of good fit.\u003c/p\u003e\u003cp\u003eModern item response theory approaches, such as Rasch modeling, place persons and items on a common latent scale, enabling interval‑level measurement and providing item‑level diagnostics (Wright \u0026amp; Mok, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In the one‑parameter logistic model (1PL), all items share the same discrimination, and the only parameter estimated is item difficulty. Item fit is typically assessed with statistics such as Infit and Outfit mean squares; values between about 0.7 and 1.3 are generally considered acceptable (Bond \u0026amp; Fox, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Boone \u0026amp; Scantlebury, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The person reliabilities (similar to Cronbach's alpha) along with separation indices are results of Rasch analyses which indicate the degree to which the tool can differentiate between the various levels of the trait being measured (Padgett \u0026amp; Morgan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). One of the strongest forms of unidimensionality in scalogram analyses involves local independence: item scores must not show any unexplained correlation of association above and beyond the predicted correlations derived from the latent trait. Diagnostics such as Yen's Q3 statistic can detect local dependence; violations suggest that items may be redundant or share a common stimulus (Chen \u0026amp; Thissen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Rasch modeling can thus inform both the retention and revision of items during instrument development.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe Digestive System Concept Scale (DSCS)\u003c/h3\u003e\n\u003cp\u003eThe DSCS was developed to detect misconceptions among 7th‑grade students concerning the digestive system. It consists of 20 multiple‑choice items, each designed to probe a specific aspect of digestion (e.g., the sequence of digestion, the role of enzymes, the distinction between mechanical and chemical digestion, nutrient absorption, and organ functions). In this study, the items were assessed as correct, scored as \u0026lsquo;1\u0026rsquo;, or incorrect, scored as \u0026lsquo;0\u0026rsquo;. The items were taken from the 7th Grade Science Curriculum, and then the clarity and content of the items were reviewed the experts and pilot tested. While the DSCS originally contained fewer items, the current version expands coverage across the domain to improve reliability and to permit robust factor and Rasch analysis.\u003c/p\u003e\u003cp\u003eThe goals that we set for ourselves are two. First, we conduct an exploratory and confirmatory factor analysis of the DSCS\u0026rsquo;s factor structure to consider the possibility of one latent trait(s) (the severity of misconceptions) and how well one or more factors could structurally describe the phenomenon. Second, we use Rasch modeling to examine item functioning, person reliability, local item independence, and general scale reliability. All these approaches together contribute to adequate psychometric reasoning to interpret the DSCS scores and formulate the appropriate instructional strategies.\u003c/p\u003e\n\u003ch3\u003eTheoretical Framework: Conceptual Change in Science Education\u003c/h3\u003e\n\u003cp\u003eTo understand why misconceptions stick, one must study constructivist learning theory where knowledge is added to what is already known. Children, according to Piaget, add new knowledge to their mental structures as long as it is new until it is shown that they must change the structures in order to accommodate new knowledge. Posner and the rest built on this idea to conceptual change and argued that learners must become dissatisfied with their conceptions before they can change to more scientifically accurate ones (Posner et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). This dissatisfaction is more than often triggered by cognitive conflict situations where the current reality does not match what is believed. Cognitive conflict alone is insufficient. Therefore, learners have to deal with new explanations that are understandable, believable, and practical. The long-lasting presence of misconceptions is due to the absence of explanations, or the explanations that are provided do not fulfill requirements. (Taber, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In the case of the digestive system, \u0026ldquo;digestion happens where food is stored\u0026rdquo; and \u0026ldquo;enzymes get used up like fuel\u0026rdquo; are naive heuristics that children have as explanations, and are used because they are similar to real-life. Such intuitions are hard to change because they are practical in real life and scientific phenomena that are below the surface level of life are complex and invisible. According to diSessa's phenomenological primitives theory (diSessa, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), such fragments of knowledge (or \"p-prims\") are contextually activated and need not form coherent theories. Effective conceptual change thus requires not only introducing correct information but also restructuring underlying intuitions. Strategies such as refutation texts (Tippett, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which explicitly state a misconception and then refute it with evidence and other conceptual change texts can help in this process. Diagnostic tests like the DSCS provide teachers with the information necessary to tailor such interventions.\u003c/p\u003e\u003cp\u003eAnother perspective is the resources framework, which suggests that misconceptions are not monolithic ideas but rather consist of multiple semi‑independent resources that can be activated differently across contexts (Hammer, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). From this viewpoint, assessment instruments need to sample understanding in varied contexts to capture the variability of student thinking. The DSCS therefore includes items that probe the same underlying concept in different ways, for example, multiple items assess understanding of chemical digestion in both the mouth and the stomach to ensure that we capture stable misconceptions rather than isolated responses. This design helps reveal whether students hold consistent misconceptions across scenarios or whether their answers vary with surface features.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and Data Collection\u003c/h2\u003e\u003cp\u003eParticipants were 412 7th‑grade students (mean age\u0026thinsp;=\u0026thinsp;12.8 years, SD\u0026thinsp;=\u0026thinsp;0.7) from nine public schools in a large metropolitan area in Turkey. The sample was roughly balanced in terms of gender (52% female). Parents agreed to the process and the students chose to take part in the process. Data collection took place during the normal lessons of science. The students DSCS, were supervised by their teachers who primarily were in charge of the class. The students were told to work alone and were told to use all their possible knowledge to complete the set in the time limit of 45 minutes. Teachers collected the answer sheets, which were later coded and double-entered into a database by trained researchers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eInstrument Development\u003c/h2\u003e\u003cp\u003eThe 20 items of the DSCS were developed through a multi‑step process. Researchers first reviewed the 7th‑grade science curriculum, textbooks, and relevant educational literature to identify key concepts in digestion and common misconceptions.Draft items were created as two-option multiple choice questions, including distractors that captured common inaccuracies (e.g., \"Physical digestion takes place only in the mouth of the mouth\" versus \"Chemical digestion occurs exclusively in the stomach\"). These items were piloted with a small group of 30 students and reviewed by three experienced science teachers and a university professor of biology education. The reviewers assessed clarity, appropriateness for 7th‑grade comprehension, and content coverage. Items were revised or discarded based on pilot performance and expert feedback, yielding a 20‑item instrument covering four conceptual domains (digestive organs; chemical digestion and enzyme action; carbohydrate and nutrient metabolism; and absorption).\u003c/p\u003e\u003cp\u003eAdditionally, during development we undertook cognitive interviews with a diverse subset of ten 7th‑grade students to ensure that item wording was interpreted as intended. In these think‑aloud sessions, some students misinterpreted certain phrases (for example, a few thought \"chemical digestion\" meant that chemicals are added to food). Based on their feedback, we refined the wording of items to highlight processes (e.g., enzyme action) rather than substances. We also asked students to explain their reasoning for each distractor, to verify that each incorrect option indeed reflected a common misconception. Items that did not elicit the expected misconceptions were revised or replaced. This iterative development process helped ensure that the DSCS not only aligned with curriculum standards but also resonated with students' actual thinking.\u003c/p\u003e\u003cp\u003eThe revised instrument was then pilot tested in two additional schools. We analyzed item difficulties and item-total correlations from this field test to fine‑tune the scale before full deployment. The development process pinpointed the weaknesses and gaps and thus confirmed the content validity of the DSCS. Item-level descriptives can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eExploratory Factor Analysis (minres, oblimin)\u0026mdash;Pattern Loadings (\u0026ge;\u0026thinsp;.30 Shown)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors/Items\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF1. Digestive Processes and Chemical Digestion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF2. Organs and Mechanisms\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex5. Nutrients are digested and excreted in the small intestine.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex6. Digestion begins in the mouth and ends in the liver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex7. The absorption of digested food and its passage into the blood occurs in the large intestine.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex12. Only chemical digestion takes place in the stomach.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex13. Digestive enzymes are effective only at a certain temperature.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex14. Since carbohydrates are digested in the mouth, they are not digested in the stomach.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex16. Only water is absorbed in the small intestine.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex17. Fat digestion does not occur in the mouth.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex18. Fat digestion takes place in the stomach.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex20.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex1. Physical digestion of food begins in the mouth, while chemical digestion does not occur.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex2. The digestive system is a filtering process by the stomach.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex3. Since water cannot be digested, it cannot be absorbed in the small intestine and is absorbed in the large intestine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex4. Only physical digestion takes place in the mouth.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex8. Physical digestion takes place in the esophagus.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex9. Increasing the amount of enzymes and adding gastric juice accelerates digestion in the stomach.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex11. The liver secretes bile and digests fats, and the pancreas secretes carbohydrates and proteins.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex15. A piece of meat swallowed without chewing is normally chemically digested in the stomach.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19. Digestion also has an absorption dimension.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote.\u003c/em\u003e Loadings\u0026thinsp;\u0026lt;\u0026thinsp;.30 suppressed. Tetrachoric correlation matrix used. SS loadings: PA1\u0026thinsp;=\u0026thinsp;2.60, PA2\u0026thinsp;=\u0026thinsp;2.08; cumulative variance\u0026thinsp;\u0026asymp;\u0026thinsp;.23.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eScoring and Data Preparation\u003c/h3\u003e\n\u003cp\u003eEach item was scored dichotomously: correct responses received a score of 1 and incorrect responses 0 (Hammond, 1994). Omitted answers were treated as incorrect. Thus, total raw scores ranged from 0 to 20. For factor analysis, we computed a tetrachoric correlation matrix among the items. Tetrachoric correlations estimate the relationships between the underlying continuous propensities that lead to the dichotomous item responses, under the assumption of an underlying bivariate normal distribution for each item pair. These correlations are appropriate for binary data and provide a foundation for factor extraction.\u003c/p\u003e\u003cp\u003eFor Rasch modeling, we constructed a person\u0026ndash;item response matrix (with rows as participants and columns as items). We examined the distribution of total scores and calculated the person separation reliability. To facilitate interpretation of Rasch results, we converted each student's logit ability estimate into a standardized T‑score (mean\u0026thinsp;=\u0026thinsp;50, SD\u0026thinsp;=\u0026thinsp;10). These transformed scores place students on a familiar scale while preserving the interval properties of the Rasch logit scale.\u003c/p\u003e\n\u003ch3\u003eAnalytic Strategy\u003c/h3\u003e\n\u003cp\u003eWe adopted a sequential analysis plan that combined classical test theory indices, factor analyses, and item response theory. All analyses were conducted using R (version 4.2.1) with relevant packages (e.g., psych, lavaan, eRm, and mirt).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDescriptive statistics and item analysis.\u003c/b\u003e We first examined classical item statistics. For each item, we computed the proportion of students answering correctly (item difficulty) and the corrected item\u0026ndash;total correlation (point-biserial correlation between the item and the total score excluding that item, often called item\u0026ndash;rest correlation). These indices helped identify any potentially problematic items, such as those that were extremely difficult or easy (difficulty\u0026thinsp;\u0026lt;\u0026thinsp;0.20 or \u0026gt;\u0026thinsp;0.80) or those that did not correlate with the overall test (item\u0026ndash;total correlation\u0026thinsp;\u0026lt;\u0026thinsp;0.20). Items showing extreme difficulty or poor discrimination were flagged for further scrutiny.\u003c/p\u003e\u003cp\u003eSuitability for factor analysis. To ensure that factor analysis was appropriate for our data, we calculated the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) measure of sampling adequacy and performed Bartlett's test of sphericity on the item correlation matrix. A KMO value\u0026thinsp;\u0026ge;\u0026thinsp;0.60 and a significant Bartlett's test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) would indicate that the data are factorable and justify proceeding with factor analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExploratory factor analysis (EFA).\u003c/b\u003e Using the tetrachoric correlation matrix, we conducted EFA to explore the underlying factor structure without imposing a predetermined model. We employed principal axis factoring (which does not assume multivariate normality and is well-suited to non-normal or binary data) and used oblimin rotation (an oblique rotation) because we expected any factors to be correlated (Costello \u0026amp; Osborne, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). To decide how many factors to retain, we triangulated multiple criteria: parallel analysis (which compares empirical eigenvalues to those from random data), the scree test (visual identification of an \"elbow\" in the plot of eigenvalues), and Velicer's Minimum Average Partial (MAP) test. Notably, parallel analysis can tend to over-extract factors with dichotomous data due to sampling error, so we cross-checked its suggestions with the MAP and the nScree method (which aggregates several criteria). In our case, the MAP test favored a single factor whereas parallel analysis suggested as many as eight factors, and the nScree indicated two factors. Based on these results and theoretical expectations (that a general misconception trait might have subdomains), we decided to examine both one-factor and two-factor solutions. We considered an item to load saliently on a factor if its loading was \u0026ge;\u0026thinsp;0.30 on that factor and it did not simultaneously load\u0026thinsp;\u0026gt;\u0026thinsp;0.20 on another factor. We interpreted any extracted factors in light of the content and conceptual focus of the items grouping on them.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConfirmatory factor analysis (CFA).\u003c/b\u003e We next tested the dimensionality indicated by EFA using CFA. We fit two CFA models with lavaan: a one-factor model (all 20 items loading on a single latent construct) and a two-factor model (items loading on two correlated latent factors, based on the EFA groupings). Given the dichotomous nature of the data, we treated items as ordered categorical variables and used the WLSMV estimator, which is robust for categorical outcomes. Factor variances were fixed to 1 for model identification. Model fit was evaluated with standard indices: CFI and TLI (values\u0026thinsp;\u0026ge;\u0026thinsp;0.90 or 0.95 for good or excellent fit) and RMSEA and SRMR (values\u0026thinsp;\u0026le;\u0026thinsp;0.08 for acceptable fit, with \u0026le;\u0026thinsp;0.05 indicating close fit). We compared the one- and two-factor models on these indices. We also checked modification indices for any local misfit (e.g., suggesting correlated residuals) and considered whether allowing any additional covariance or cross-loading was substantively justified. In addition to overall fit, we calculated composite reliability (CR) and average variance extracted (AVE) for each factor in the two-factor model to assess internal consistency and convergent validity of the factors. Generally, CR\u0026thinsp;\u0026gt;\u0026thinsp;0.70 and AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.50 are desired (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). We report these values and discuss whether the factors were sufficiently distinct (discriminant validity) or essentially measuring the same construct.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRasch modeling.\u003c/b\u003e Finally, we performed Rasch analysis by fitting a one-parameter logistic (1PL) model using the eRm package. This yielded estimates of item difficulty (the location of each item on the latent scale) and person ability for each student. All items were constrained to equal discrimination (slope) in this model. We examined item fit via Infit and Outfit mean square statistics; we considered values in the range 0.70\u0026ndash;1.30 as indicative of acceptable fit. Items with fit statistics outside this range or with standardized fit residuals |z| \u0026ge; 2.0 were flagged for review. (We note that with N\u0026thinsp;=\u0026thinsp;412, the standardized fit statistic can be sensitive, so we emphasize mean-square fit and substantive content review of flagged items rather than rigidly dropping items on the basis of z alone.) We also examined person fit to identify any students with aberrant response patterns (e.g., high ability students getting many easy items wrong, or vice versa). Person separation reliability and the separation index were computed to evaluate the precision of measurement on the logit scale. We generated a Wright map (person\u0026ndash;item map) to visualize the distribution of student abilities against item difficulties; this helps identify whether the test items appropriately target the range of student misconceptions (e.g., whether very high or very low ability ranges are underrepresented by items). Finally, we assessed local independence by computing Yen's Q3 statistic for residual correlations between item pairs. Item pairs with Q3\u0026thinsp;\u0026gt;\u0026thinsp;0.20 would suggest local dependence (Yen, 1984), potentially indicating redundant items or shared stimuli, but as reported below we found no such violations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eItem Analysis and Descriptive Statistics\u003c/h2\u003e\u003cp\u003eItem difficulty (proportion of students answering correctly) ranged from 0.32 to 0.86. No item was extremely difficult or extremely easy by our criteria, indicating that the test included a mix of items of varying difficulty without floor or ceiling effects. The corrected item\u0026ndash;total correlations (item discrimination indices) varied between 0.19 and 0.50, with a median of 0.33, suggesting that most items had satisfactory discrimination and were contributing positively to the total score. Two items (labeled x09 and x20) showed the lowest item\u0026ndash;total correlations (approximately 0.19 and 0.16, respectively). Increased enzymes accelerate digestion was stated as item x09 (the misconception being that digestion can be sped up infinitely with the addition of enzyme). Item x20 dealt with the secretion of bile. However, despite their relatively low discrimination, no item had an item\u0026ndash;total correlation below 0.15, and all items still demonstrated at least a moderate correlation with overall test performance. We therefore retained all 20 items for further analyses. The Cronbach's alpha for the 20 items was 0.77, which indicates acceptable internal consistency for a concept diagnostic scale. The Johnson \u0026amp; Morgan (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) study also shows how this value yields confidence that the DSCS provides consistent outcomes across individual items since \u0026lsquo;the commonly recommended reliability bounds for classroom assessments\u0026rsquo; suggests that the primary value should fall between \u0026lsquo;0.70 and 0.95\u0026rsquo; (recall: group-level use).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSuitability for Factor Analysis\u003c/h2\u003e\u003cp\u003eThe DSCS data met key assumptions for factor analysis. The Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) measure of sampling adequacy was 0.86, which is well above the recommended minimum of 0.60 for a satisfactory factor analysis. Similarly, Bartlett's test of sphericity was highly significant (χ\u0026sup2; = 2344.19, df\u0026thinsp;=\u0026thinsp;171, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the item correlation matrix was not an identity matrix and contained sufficient structure for factor extraction. Together, these diagnostics confirmed that it was appropriate to proceed with exploring the factor structure of the DSCS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eExploratory Factor Analysis (EFA)\u003c/h2\u003e\u003cp\u003eUsing principal axis factoring on the tetrachoric correlation matrix, we conducted an EFA to probe the latent structure. The initial unrotated factor solution showed that the first factor had an eigenvalue substantially larger than subsequent factors. The first factor alone accounted for 37.5% of the variance, while the second factor accounted for 8.4%. A parallel analysis (random eigenvalue comparison) suggested that more than one factor could be retained, but the results were inconclusive due to the tendency of parallel analysis to overestimate factors with dichotomous data. The scree plot revealed a noticeable elbow after the first factor, with a much smaller drop to the second, suggesting a dominant first factor. We also computed Velicer's MAP test, which indicated a single underlying factor. Balancing this evidence, we examined both a one-factor and a two-factor solution. Parallel analysis on tetrachoric correlations guided factor retention; the scree comparison is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the one-factor EFA solution, all 20 items loaded positively on the general factor, with factor loadings ranging from 0.29 to 0.78. This single factor solution explained 45.9% of the total variance in item responses. In the two-factor solution (using oblimin rotation to allow correlation between factors), the cumulative variance explained increased to 55.3%. The two factors were interpretable and aligned with content themes. Factor 1, which we might label Digestive Processes and Chemical Digestion, included items emphasizing biochemical aspects of digestion (e.g., the role of enzymes, understanding of chemical vs. mechanical digestion, and nutrient absorption processes). For instance, item x13 (misconception about enzyme function) and item x16 (misconception about where digestion can occur simultaneously) loaded strongly on Factor 1 (loadings 0.74 and 0.62, respectively) and had minimal cross-loading on the other factor. Factor 2, which we might label Organs and Mechanisms, comprised items focusing on anatomical sequence and organ-specific functions (e.g., which organs perform which digestive roles, where digestion begins, how food moves). Items x05 (belief that most nutrient digestion happens only in the small intestine) and x06 (belief that digestion begins only in the stomach rather than the mouth) were among those loading on Factor 2. Cross-loadings between the two factors were generally low; no item had a secondary loading greater than 0.20. This suggests that, while a single overall misconception trait underlies performance, there are two meaningful clusters of items that provide a richer, more nuanced view of students' misconceptions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eConfirmatory Factor Analysis (CFA)\u003c/h2\u003e\u003cp\u003eWe next conducted CFA to compare a unidimensional model against the two-factor model indicated by the EFA. In the one-factor CFA, all 20 items were specified to load on a single latent factor representing overall misconception severity. This model achieved an acceptable fit to the data: CFI\u0026thinsp;=\u0026thinsp;0.978, TLI\u0026thinsp;=\u0026thinsp;0.975, RMSEA\u0026thinsp;=\u0026thinsp;0.024, SRMR\u0026thinsp;=\u0026thinsp;0.076. All factor loadings in this model were significant and positive, ranging from about 0.35 up to 0.75. However, the modification indices for the one-factor model suggested that a few pairs of items with very similar content (for example, item x10 about enzyme reuse and item x11 about enzyme function) had correlated residuals, hinting that a secondary grouping might improve model fit.\u003c/p\u003e\u003cp\u003eWe then specified a two-factor CFA model based on the EFA groupings: Factor 1 included the items related to digestive processes and enzymes, and Factor 2 included items related to organ functions and mechanisms. We allowed the two factors to correlate, as suggested by theory and the EFA oblimin rotation. The two-factor model provided an excellent fit: CFI\u0026thinsp;=\u0026thinsp;0.997, TLI\u0026thinsp;=\u0026thinsp;0.996, RMSEA\u0026thinsp;=\u0026thinsp;0.009, SRMR\u0026thinsp;=\u0026thinsp;0.070. These fit indices were substantially better than those of the one-factor model, indicating that modeling two latent factors improved the representation of the data. Factor loadings in the two-factor CFA ranged from 0.36 up to 0.74, with a median loading around 0.53. Each item loaded most strongly on its intended factor, and no cross-loadings were specified in the CFA. The two factors were moderately to strongly correlated (r\u0026thinsp;=\u0026thinsp;0.704), which is consistent with the idea that while the DSCS can be viewed as tapping a single broad misconception construct, the two subdimensions are closely related. Given this high inter-factor correlation, the DSCS is essentially unidimensional in terms of measurement, though the two-factor solution offers some pedagogical interpretability (as discussed below). The two-factor model fit better than the one-factor alternative (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Standardized loadings for the retained two-factor solution appear in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and the path diagram is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The latent factors were strongly correlated (r\u0026thinsp;\u0026asymp;\u0026thinsp;.70), supporting a single predominant construct with two interpretable facets.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eConfirmatory Factor Analysis\u0026mdash;Model Fit Indices (WLSMV)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRMSEA 90% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne-factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e211.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.011\u0026ndash;.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo-factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e179.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.000\u0026ndash;.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote.\u003c/em\u003e Robust (scaled) indices were more conservative (two-factor robust CFI\u0026thinsp;=\u0026thinsp;.844; robust RMSEA\u0026thinsp;=\u0026thinsp;.062 [.044\u0026ndash;.078]).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eTwo-Factor CFA: Standardized Loadings\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. loading\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.524\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.596\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote.\u003c/em\u003e Latent correlation r(F1, F2)\u0026thinsp;=\u0026thinsp;.73.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further evaluate the two-factor solution, we calculated the composite reliability (CR) for each factor, finding CR\u0026thinsp;\u0026asymp;\u0026thinsp;0.85 for Factor 1 and 0.81 for Factor 2. These values indicate good internal consistency for the items grouped in each factor. Factor 1 was 0.49 and Factor 2 was 0.45, with an average variance extracted (AVE) of two, which still is lower than the generally agreed upon 0.50 limit for convergent validity (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). This means that despite the fact that there is a considerable amount of variance in the captured items corresponding to each factor, almost half of the variance is not captured and that the remaining variance is a result of either error or uniqueness. However, given the relatively high correlation between the factors (and the modest number of items per factor), the somewhat lower AVE is not surprising. We considered whether any low-loading items could be removed to raise the AVE, but doing so would have reduced content coverage of the scale. On balance, we retained all items to preserve the diagnostic breadth of the DSCS. We conclude that the CFA provided strong support for a two-factor structure, while also affirming that a single overarching trait largely governs student performance.\u003c/p\u003e\u003cp\u003eImportantly, removing the two previously flagged items (x09 and x20) and re-running the CFA did not substantially improve model fit; it actually slightly worsened some indices and left certain content areas underrepresented. Therefore, all 20 items were retained in the final instrument for subsequent Rasch analysis and interpretation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eRasch Modeling\u003c/h2\u003e\u003cp\u003eWe fit a one-parameter Rasch model (1PL) to the 20-item dataset to further assess the measurement properties of the DSCS. The item difficulty estimates (denoted δ or β) ranged from \u0026minus;\u0026thinsp;0.85 logits (for item x09, which turned out to be one of the easiest items) to +\u0026thinsp;0.78 logits (for item x19, one of the most difficult items). This range of approximately 1.6 logits indicates a reasonable spread of item difficulty across the continuum of misconception severity. It was reasonable to arrange the items in levels of difficulty. The easier ones often related to more fundamental or widely taught ideas like pinpointing the beginning stages of digestion, while the harder ones were aimed at more subtle misunderstandings like the enzymes that get reused and the action of bile. Under a Rasch (1-PL) model, item difficulties and fit indices (infit/outfit mean squares and standardized z) are reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The person\u0026ndash;item (Wright) map is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and item characteristic curves (ICCs) for representative items are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eRasch (1PL) Item Difficulty and Fit\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDifficulty (logit)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfit MSQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOutfit MSQ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e Difficulties are mean centered. Reference MSQ range .70\u0026ndash;1.30.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe person reliability in the Rasch analysis was 0.706. This reliability is analogous to Cronbach's alpha in classical test theory and suggests that the DSCS can reliably distinguish at least two to three broad levels of student ability (misconception severity) in our sample. The separation index, calculated as SD(θ)/SEM on the logit scale and related to reliability, was about 1.55, indicating that the test separates students into approximately 1.552\u0026thinsp;\u0026asymp;\u0026thinsp;2 strata of ability. A person reliability above 0.70 is acceptable for educational assessments of this type.\u003c/p\u003e\u003cp\u003eAll items showed good fit to the Rasch model. The Infit mean square statistics ranged from 0.85 to 1.19, and the Outfit mean squares were in a similar range, all well within the 0.7\u0026ndash;1.3 guideline for acceptable fit. Thus, none of the items exhibited aberrant response patterns inconsistent with the expectations of the model. We have standardized fit residuals (z-statistics) that indicated a severe misfit with six items (x07, x10, x11, x12, x17, x20) with |z|\u0026ge; 2. However, statistically significant misfit should be use with caution. In this case, it is warranted because our sample is considerable (N\u0026thinsp;\u0026gt;\u0026thinsp;400) and it is known these sample sizes influence Bond and Fox (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) significant z values with minor deviations. Crucially, all six of these flagged items had mean-square fit statistics very close to 1 (well within the acceptable range), indicating that the magnitude of misfit was small in practical terms. Upon examining the content of these items, we found plausible reasons for their slight misfit: for example, item x12 (\"Only chemical digestion takes place in the stomach\") may have confused some students who did not clearly distinguish chemical from mechanical digestion, and item x10 (\"Enzymes are lost after digestion\") touches on knowledge (enzyme reuse) that might not be well emphasized in the curriculum. Such items might behave a bit unpredictably (leading to more variability in responses), but this does not invalidate them. Rather than removing these items, we decided to keep them in the scale but note in our recommendations that they could be rephrased in future revisions to reduce ambiguity. Overall, misfit in this context does not necessarily imply the item is useless; it may still target a valuable misconception concept (Sijtsma, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe also checked the assumption of local independence among items using Yen's Q3 residual correlations. The largest observed residual correlation between any pair of items was 0.18, and no item pair exceeded the 0.20 threshold for concern. This indicates that after accounting for the primary Rasch trait, item responses were essentially independent \u0026ndash; in other words, there was no evidence that any two items shared additional common factors or that answering one item influenced responses to another. The DSCS items each appear to contribute unique information about the underlying misconception trait.\u003c/p\u003e\u003cp\u003eIn terms of person fit, about 4.2% of students had outfit or infit statistics beyond the usual cut-off (e.g., outfit\u0026thinsp;\u0026gt;\u0026thinsp;1.5), suggesting their response patterns were somewhat unusual. On inspecting these cases, we found they corresponded to instances where students might have been randomly guessing or not fully engaged (for example, some of these students had nearly equal numbers of correct and incorrect answers in a sporadic pattern or left multiple items blank). These few cases did not substantially affect the overall analysis and were not removed, but they underscore the importance of ensuring student motivation and understanding of instructions when administering the test.\u003c/p\u003e\u003cp\u003eThe Wright map (person\u0026ndash;item map) produced by the Rasch analysis provides a visualization of the scale's targeting. We observed that the bulk of student ability estimates clustered slightly above the mean item difficulty. In other words, on average, students in our sample found the test slightly easy (many students scored in the upper half of the raw score range). The items tended to target low to moderate levels of misconception severity; fewer items were located at the very high end of the ability scale. This results in fewer very difficult items to challenge the most knowledgeable students (those with the least misconceptions). Consequently, high-performing students (with very few misconceptions) may achieve near-perfect scores, making it harder to differentiate among them based on this test. This ceiling effect suggests that adding a few more challenging items, perhaps items that probe deeper biochemical or physiological details of digestion, could improve the test's ability to measure the upper end of understanding and thereby increase person reliability.\u003c/p\u003e\u003cp\u003eGiven that the Rasch analysis supported treating the DSCS as essentially unidimensional (one dominant trait underlying performance) and the person reliability was adequate, we proceeded to use the total Rasch score (ability estimate) for each student as the primary measure of misconception severity. For practical use in classrooms, we also converted the continuous Rasch ability estimates into more interpretable metrics. As mentioned, we standardized the logits into T‑scores (mean\u0026thinsp;=\u0026thinsp;50, SD\u0026thinsp;=\u0026thinsp;10). An average student in our sample thus would have a T-score around 50; higher T-scores indicate more severe misconceptions (lower understanding), and lower T-scores indicate fewer misconceptions (higher understanding).\u003c/p\u003e\u003cp\u003eAdditionally, we established performance bands based on percentile cut-offs to facilitate interpretation by teachers. For example, we defined approximate score bands as follows: raw scores of 0\u0026ndash;4 correct (out of 20) as Very Low understanding (indicating pervasive misconceptions), 5\u0026ndash;7 correct as Low, 8\u0026ndash;11 as Average, 12\u0026ndash;14 as High, and 15\u0026ndash;20 as Very High. These categories correspond roughly to certain regions on the ability scale (e.g., the Very Low band roughly corresponds to ability estimates below about \u0026minus;\u0026thinsp;1.5 logits, and the Very High band corresponds to abilities above +\u0026thinsp;1.5 logits, based on our data distribution). A teacher can use these bands to get a general sense of a student's level: for instance, a student in the Very Low band likely holds many fundamental misconceptions about digestion and may need intensive review of basic concepts, whereas a student in the Very High band has mastered most concepts and might benefit from enrichment activities.\u003c/p\u003e\u003cp\u003eIt must be stressed that these support bands are meant to be descriptive and not to stigmatize students. In this case support bands must be used along with other analysis at the item level. For example, a student in the Average band (around 8 to 11 correct) could still hold certain misconceptions (based on the particular items they got wrong) that need to be fixed. Conversely, a student in the \"High\" or \"Very High\" band might still have one or two stubborn misconceptions that could be uncovered through class discussion or open-ended questions. We advise educators to interpret the total score as a starting point and then delve into which items were missed to target instruction appropriately. Because the two factors identified by CFA were so highly correlated and the subscale reliability for each was low (Cronbach's α approximately 0.50 for Factor 1 and 0.32 for Factor 2), we do not recommend reporting separate subscale scores for each factor. Instead, teachers and researchers should interpret the DSCS as yielding a single overall misconceptions score, supplemented by an item-level profile to indicate whether a student's misconceptions tend to cluster more in process-related or anatomy-related areas.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study sought to validate the 20‑item DSCS using robust psychometric methods. As a whole, the DSCS showed strong technical attributes for a concept inventory. The instrument showed sufficient reliability (Cronbach's α\u0026thinsp;=\u0026thinsp;0.77; Rasch person reliability\u0026thinsp;=\u0026thinsp;0.706) and exhibited range for student understanding mastery. Both EFA and CFA indicated that student responses were driven primarily by a single latent trait (general understanding of digestion concepts vs. misconceptions). However, a two‑factor model provided a marginally better fit in CFA and offered pedagogical insight by grouping misconceptions into two interpretable facets (digestive processes vs. organ functions). Rasch modeling confirmed that items functioned appropriately (no significant misfit after accounting for content considerations) and that local item independence was satisfied. The person\u0026ndash;item map revealed that the scale was somewhat easier for our sample than expected, suggesting the need for a few more difficult items to better challenge high-understanding students. Taken together, these results support using the DSCS as a valid and reliable instrument to diagnose misconceptions about digestion among 7th‑grade students.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eInterpretation of Factor Structure\u003c/h2\u003e\u003cp\u003eAlthough the two‑factor CFA model provided the best fit statistically, the high correlation between factors (approximately 0.70) suggests that the DSCS largely measures a single underlying construct: the overall severity of a student's misconceptions about the digestive system. The two factors can be thought of as emphasizing different aspects of this construct. One factor highlights process-related misconceptions (e.g., misunderstanding chemical vs. mechanical digestion, enzyme action, and how nutrients are broken down), while the other centers on structure-related misconceptions (e.g., confusion about which organs perform certain functions, or the path food takes through the digestive system).\u003c/p\u003e\u003cp\u003eFrom an instructional perspective, these facets are useful. Teachers may find it valuable to know, for example, if a class as a whole is weaker in understanding digestive processes as opposed to anatomy. In our data, many students showed consistent errors on the enzyme-related items (Factor 1), indicating a potential curriculum gap in teaching the role of enzymes or the chemical nature of digestion. If a teacher administers the DSCS and notices widespread difficulties on those items, they could plan additional lessons or demonstrations focusing on enzyme function (e.g., an experiment showing how enzymes break down food or the effect of pH on enzyme activity). Conversely, if students struggled more with items like X05 and X06 on Factor 2 (organ functions and sequences), a teacher might spend time having students construct annotated digestive system diagrams or perform a simulation of the digestive process, to reinforce what happens in each organ.\u003c/p\u003e\u003cp\u003eThat said, because each subscale (Factor 1 and Factor 2) was short (approximately 10 items each) and their internal consistencies were quite low (α\u0026thinsp;~\u0026thinsp;0.50 or below), we caution against reporting separate scores for the two factors. Subscale scores at that reliability would be unstable and potentially misleading (Nunnally, 1978). The safer approach is to use the total score as the primary metric of misconceptions and then use the pattern of item responses to glean insights into the types of misconceptions. In practice, an educator can review which specific questions a student or class answered incorrectly to diagnose whether those misconceptions relate more to processes or to structures, without needing to formally compute two subscale totals. This approach balances psychometric rigor (by using a well-defined unidimensional score) with pedagogical utility (by interpreting item patterns).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eRasch Interpretation and Item Functioning\u003c/h2\u003e\u003cp\u003eRasch analysis offered several valuable perspectives on the performance of items and the test. To begin with the spread of the item difficulties suggest that the DSCS covers narrow and moderate levels of comprehension. There were items that most students found easy (indicating very common misconceptions that almost everyone held) and items that were difficult (misconceptions that only the least-informed students held). However, the lack of extremely difficult items suggests that our instrument might not be sufficiently challenging for the top quartile of students who have very few misconceptions. In a class of mostly high-achieving students, the DSCS could yield many high scores with little variation. Adding 2\u0026ndash;3 more difficult items that target subtle or higher-order misconceptions (for instance, items about hormonal regulation of digestion or integration of digestion with other body systems like circulatory or endocrine) could improve the instrument's ability to discriminate among students at the upper end of understanding. Increasing the spread of item difficulties in this way would likely also raise the person reliability, as more information is gathered about high-performing students.\u003c/p\u003e\u003cp\u003eSecond, while all items were within acceptable fit ranges, the fact that six items triggered the statistical misfit criterion (|z| \u0026ge; 2) invites reflection. After examining these items qualitatively, we suspect that the wording or cognitive demand of some items could be refined. For instance, students who fail to notice that mechanical digestion (churning) also happens in the stomach may get confused with the statement in item x12 (\"Only chemical digestion takes place in the stomach\"). Perhaps, for the sake of accuracy, focusing the misconception would be better if it was clarified to \u0026ldquo;No mechanical digestion occurs in the stomach.\u0026rdquo; Item x10 (\"Enzymes are lost after digestion\") touches on the concept of enzyme reusability, a topic that might not be fully covered in 7th grade, so some students might have answered incorrectly simply due to lack of instruction rather than a misconception per se. In future revisions of the DSCS, we recommend revisiting such items. The target audience should be students who are prepared to face the misconceptions the curriculum defines. The items should be retained rather than removed because they do target important misconceptions. Little changes could enhance their performance without compromising their content.\u003c/p\u003e\u003cp\u003eThird, the Rasch analysis affirmed that each item contributes unique information (local independence) and that a single score is defensible. We found no evidence of item clustering effects or multi-dimensionality in the residuals. This justifies the practice of reporting a total score (or Rasch measure) for each student. It also supports using the Rasch difficulty measures for each item to understand which misconceptions are easier or harder for students to avoid. For instance, the easiest item (x09) was one that nearly all students got right, suggesting that the misconception it tests (possibly a trivial or less common one) is not widespread. In contrast, the hardest item (x19) points to a misconception that even many high-performing students held, identifying a particularly problematic area of understanding.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eComparison with Previous Research\u003c/h2\u003e\u003cp\u003eOur findings align with earlier studies on science misconceptions that have often found a largely unidimensional structure underlying students' incorrect belief. For example, instruments measuring misconceptions in other domains like photosynthesis or genetics have similarly found that while multiple factors can sometimes be identified, those factors tend to be highly correlated, reflecting one general proficiency (or lack thereof) in the domain (Haslam \u0026amp; Treagust, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Levy Nahum et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In the context of digestive system misconceptions, relatively few published scales exist. One prior study in Turkey by Uğur (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) developed a two-tier test for high school students on the digestive system and reported a Cronbach's alpha of 0.71 for their 16-item test, which is comparable to the reliability of 0.77 we obtained for the DSCS. This suggests that the DSCS's reliability is on par with similar instruments, despite our inclusion of more items and more advanced psychometric analysis.\u003c/p\u003e\u003cp\u003eNotably, many earlier misconception tests have relied solely on classical test theory or exploratory factor analysis, without the additional confirmation and item-level scrutiny that Rasch modeling provides. This sometimes led to ambiguity in the interpretation of dimensions or inflated views of reliability. By employing both factor analysis and Rasch modeling, our study demonstrates the value of a mixed psychometric approach. The factor analyses allowed us to see overarching patterns and potential subdimensions, while the Rasch analysis confirmed the essential unidimensionality and helped identify specific items that could be improved. We believe this dual approach provides a more nuanced validation than either method alone, a practice that could be beneficially applied in validating concept inventories in other science domains.\u003c/p\u003e\u003cp\u003eIn terms of magnitude of misconceptions, the average score in our sample and the person ability distribution indicate that misconceptions about digestion are still prevalent among 7th graders, despite digestion being a standard topic in the curriculum. This echoes the observations of other researchers who have found that significant proportions of students harbor misconceptions even after instruction (\u0026Ouml;zkan, 2017). Our high factor correlation (r\u0026thinsp;~\u0026thinsp;0.70) also mirrors findings from concept inventories in other areas of biology and chemistry where factors were identified but were not truly independent (e.g., different facets of understanding in chemical bonding were found to correlate strongly: Levy Nahum et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In practice, many researchers in science education end up reporting a single overall score on such inventories (even if a factor analysis suggests multiple factors) because that overall score tends to be the most reliable and interpretable measure of student understanding (or misunderstanding). Our results support a similar approach for the DSCS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eImplications for Research and Practice\u003c/h2\u003e\u003cp\u003eThe DSCS provides teachers and researchers with a psychometrically sound tool for diagnosing students' misconceptions about digestion and tailoring instruction accordingly. For classroom practitioners, the test covers multiple aspects of digestion, allowing them to pinpoint whether student misunderstandings cluster around certain themes. For instance, if a teacher finds that many students missed questions related to enzymes and chemical reactions (Factor 1 items), this would signal a need to reinforce lessons on how enzymes work (perhaps through interactive labs or analogies that make the invisible process more concrete). On the other hand, if students struggled more with items about the path of food or organ functions (Factor 2 items), the teacher might use more physical models or ask students to build a flowchart of the digestive process to solidify the sequence and roles of each organ.\u003c/p\u003e\u003cp\u003eThe derived score bands and T-scores can help educators interpret individual and class performance in an intuitive way. A teacher might say, \"Most of my class scored in the Average band, with a few in Low and a few in High,\" suggesting the needs of the class rather quickly. However, we stress the need to go deeper than the total score. We ask that teachers analyze the results multiple items and, whenever possible, address specific items with the class after the test to remediate the specific errors made. Research on conceptual change suggests that explicitly addressing misconceptions (for example, using refutation texts or class discussions where misconceptions are brought to light and examined) is necessary for many students to change their thinking (Duit \u0026amp; Treagust, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Tippett, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The DSCS can act as the diagnostic step in this process: it identifies the misconceptions, which the teacher can then deliberately refute and replace with correct conceptions through instruction.\u003c/p\u003e\u003cp\u003eFor researchers and test developers, our study illustrates a holistic approach to instrument validation. The use of item-total correlations, factor analyses (EFA/CFA), and Rasch modeling of These elements were synthesised together. Multiple approaches provides more complete proof. Subsequent researchers creating partial understanding scales in science education may adopt a similar multi-pronged strategy to evaluate construct validity, check for dimensionality, and ensure the scale\u0026rsquo;s grade. The use of EFA, CFA, and Rasch in this work illustrates how EFA proposed a structural framework, CFA evaluated the framework using stringent fit standards, and Rasch provided item-level diagnostics and an IRT-based reliability and measurement invariance framework. This kind of thorough validation is especially important if such instruments are to be used for research or high-stakes decisions.\u003c/p\u003e\u003cp\u003eOur findings also underscore the importance of item difficulty distribution. Many diagnostic tests in science either lack enough easy items (leading to floor effects where many students score zero or very low) or lack enough hard items (leading to ceiling effects). The DSCS was designed to include a range, but as discussed, it could benefit from a few more difficult items. Achieving a balanced item set that measures across the spectrum of understanding enables better discrimination among all students, not just those in the middle. This has implications for test design: during development, one should aim to include some items that even the top students will struggle with (to probe the limits of their understanding) and some items that even the lowest students will get right (to capture partial understanding and engage those students).\u003c/p\u003e\u003cp\u003eAnother implication pertains to formative use of such assessments. The DSCS is not merely a research tool; it can be used formatively in the classroom. For example, a teacher might administer the DSCS as a pre-test at the start of a unit on human biology to gauge what misconceptions exist, then tailor teaching strategies accordingly, and perhaps administer it again as a post-test to measure conceptual change. Because the DSCS is multiple-choice, it can be graded quickly (or even electronically), and because it's relatively short (20 items), it can fit into a single class period. The use of learning analytics or even simple item analysis can provide immediate feedback. If integrated into a digital platform, a teacher could see which misconceptions are most common in a class and even provide individual students with targeted follow-up materials addressing the specific misconceptions they showed.\u003c/p\u003e\u003cp\u003eFor curriculum designers and education policy makers, the DSCS results across many classrooms could highlight systemic areas of weakness. For instance, if data from many schools reveal that the majority of students misunderstand the role of enzymes or have the misconception that \"digestion occurs only in the stomach,\" this might indicate that the standard curriculum or textbooks are not effectively dispelling those notions. Curriculum developers could respond by incorporating more explicit content or activities about these trouble spots. On a larger scale, education reform efforts that emphasize inquiry and conceptual understanding might use instruments like the DSCS to measure whether reforms are reducing misconception prevalence. If, say, a new inquiry-based unit on digestion is implemented, the DSCS could serve as an evaluative tool to see if students emerging from that unit hold fewer misconceptions than students taught with a traditional approach.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\u003cp\u003eSeveral limitations of this study should be acknowledged. First, our sample was drawn from a single metropolitan region in Turkey, which may limit the generalizability of the findings. Education systems, teaching approaches, and prior knowledge can vary significantly across regions and countries. What holds true for our population may not exactly replicate elsewhere. We suggest that future research replicate the validation of the DSCS with diverse samples, including students from different countries, rural areas, or educational systems. Such studies would strengthen the external validity of the scale. They could also examine whether the factor structure remains the same across contexts or whether, for instance, different misconceptions are prominent in different cultures.\u003c/p\u003e\u003cp\u003eSecond, we administered the DSCS as a paper\u0026ndash;pencil test under teacher supervision. While this approach reflects typical classroom practice (enhancing ecological validity), it does not leverage the possibilities of technology. An interesting future direction would be to develop a computer-based or online version of the DSCS. A digital format could provide immediate feedback to students and teachers and potentially adapt to student responses. In fact, there is growing interest in computerized adaptive testing in education. An adaptive version of the DSCS could present students with items matched to their estimated misconception level, challenging stronger students with harder items and giving struggling students more basic ones, hus maintaining engagement and efficiency. Such an adaptive DSCS would require a larger item pool and careful calibration, but it could shorten test time while preserving measurement precision (by not asking students questions that are far too easy or far too hard for them). Moreover, a digital DSCS could easily be integrated into learning management systems, allowing teachers to track conceptual change over time for each student and providing rich data for researchers on how misconceptions evolve with successive learning interventions.\u003c/p\u003e\u003cp\u003eThird, we did not formally examine Differential Item Functioning (DIF) in this study. Although our Rasch analysis did not find obvious overall ability differences by gender (and an informal analysis using Rasch trees did not split by gender), we cannot rule out that some items might function differently for boys versus girls, or for other subgroups (e.g., students of different socioeconomic status or different science achievement levels). For example, certain misconceptions might be more common among one gender due to differences in interest or attention in biology. Future research using methods like the Mantel\u0026ndash;Haenszel procedure, logistic regression DIF analysis, or multigroup IRT could investigate whether any DSCS items show bias. If any DIF is detected (say, an item is systematically easier for boys than girls despite equal overall understanding, or vice versa), that item could be revised or used with caution. So far, we have no specific indication of bias, but a thorough check would be prudent, especially if the test is used in high-stakes contexts.\u003c/p\u003e\u003cp\u003eFourth, as noted in the results, some items were flagged for potential misfit. While we have rationales for keeping them, it remains that these items could be improved. Cognitive interviews or think-aloud protocols with students focusing on these specific items would be a useful next step. By asking students to explain their thought process on, say, item x12 or x10, we could learn whether the phrasing is misleading or if students are interpreting the question in unintended ways. This qualitative feedback could directly inform how to rewrite those items. Even a well-validated instrument can be refined further, and our analysis points to a few candidates for improvement.\u003c/p\u003e\u003cp\u003eLastly, the DSCS in its current form lacks items tapping the very high end of understanding (the most advanced content or the most subtle misconceptions). As discussed, adding items in that range would likely improve the instrument's discrimination among top students and raise the ceiling. In practice, these could be items that incorporate minor details or exceptions that only students with a truly robust understanding would know. For example, an item might test understanding of how the nervous system regulates digestion or a misconception about how different macronutrients are processed differently. We foresee that including a handful of such items could extend the scale's utility for advanced or older students, such as 8th or 9th graders, while still being answerable by well-taught 7th graders.\u003c/p\u003e\u003cp\u003eIn terms of future research avenues, beyond validation and improvement of this instrument, there is the broader question of how using the DSCS (or similar tools) influences teaching and learning. One could design a study where some teachers use the DSCS to inform their teaching (essentially employing it as a formative assessment tool) and compare those classes to classes that did not use the diagnostic test. Would explicit diagnosis and remediation of misconceptions lead to significantly greater learning gains? Intuition and prior conceptual change literature suggest yes (because teaching would be more targeted), but empirical evidence would be valuable. Moreover, researchers might explore longitudinal use of the DSCS: for instance, using it at the beginning of the school year and at the end to measure how students' misconceptions shift after a year of instruction not just in digestion but in related topics like metabolism. A related idea is developing parallel forms of the DSCS to allow repeated testing without practice effects, given that repeating identical items can cue students to correct answers if they remember the test.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides robust evidence for the validity and reliability of the 20‑item Digestive System Concept Scale as a diagnostic tool for student misconceptions. Through rigorous analyses that combined factor analysis and Rasch modeling, we demonstrated that the DSCS is essentially unidimensional, with two strongly correlated facets that offer interpretive value. The scale can reliably differentiate students by their misconception severity and provides detailed diagnostic information at the item level. The DSCS thus serves as a powerful instrument for both researchers and educators in the effort to understand and address misconceptions in science education.\u003c/p\u003e\u003cp\u003eIn synthesizing the broader implications of our findings, the DSCS emerges not only as a test of student knowledge but also as a platform for improving science teaching, informing curriculum design, and guiding professional development. Continuing to refine the instrument (for example, by revising ambiguous items and adding more challenging ones), extending its use across different cultures and languages, and integrating it into adaptive digital learning systems will further amplify its impact. Ultimately, tools like the DSCS contribute to a more nuanced understanding of learning by making misconceptions visible. They help us see that misconceptions are not roadblocks but rather stepping‑stones on the path to deeper understanding: if assessments, instruction, and content are constructively aligned to confront and replace misconceptions, we can foster enduring conceptual change in our students.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: The study protocol was reviewed and approved by the Erciyes University [ Non-Interventional Research Ethics Committee], Kayseri, Turkey, on [08/08/2013]. All procedures complied with the Declaration of Helsinki and relevant regulations. [ Written informed consent was obtained from all participants].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoone WJ, Scantlebury K (2006) The role of Rasch analysis in educational research. Res Sci Educ 36(4):287\u0026ndash;298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11165-006-9026-4\u003c/span\u003e\u003cspan address=\"10.1007/s11165-006-9026-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBond TG, Fox CM (2015) \u003cem\u003eApplying the Rasch model: Fundamental measurement in the human sciences\u003c/em\u003e (3rd ed.). Routledge. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9781315814698\u003c/span\u003e\u003cspan address=\"10.4324/9781315814698\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen W-H, Thissen D (1997) Local dependence indexes for item pairs using item response theory. J Educational Behav Stat 22(3):265\u0026ndash;289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3102/10769986022003265\u003c/span\u003e\u003cspan address=\"10.3102/10769986022003265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChandrasegaran AL, Treagust DF, Mocerino M (2007) The development of a two-tier multiple-choice diagnostic instrument for evaluating secondary school students\u0026rsquo; ability to describe and explain chemical reactions using multiple levels of representation. Chem Educ Res Pract 8(3):293\u0026ndash;307. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/B7RP90006F\u003c/span\u003e\u003cspan address=\"10.1039/B7RP90006F\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCostello AB, Osborne JW (2005) Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assess Res Evaluation 10(7):1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7275/jyj1-4868\u003c/span\u003e\u003cspan address=\"10.7275/jyj1-4868\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ediSessa AA (1993) Toward an epistemology of physics. Cognition Instruction 10(2\u0026ndash;3):105\u0026ndash;225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07370008.1993.9649023\u003c/span\u003e\u003cspan address=\"10.1080/07370008.1993.9649023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDry HA (1998) \u003cem\u003eStudent misconceptions in science: The human digestive system\u003c/em\u003e (Master\u0026rsquo;s thesis, SUNY Brockport)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuit R, Treagust DF (2003) Conceptual change: A powerful framework for improving science teaching and learning. Int J Sci Educ 25(6):671\u0026ndash;688. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09500690305016\u003c/span\u003e\u003cspan address=\"10.1080/09500690305016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFabrigar LR, Wegener DT (2011) Exploratory factor analysis. Oxford University Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/acprof:oso/9780199734185.001.0001\u003c/span\u003e\u003cspan address=\"10.1093/acprof:oso/9780199734185.001.0001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlora DB, Curran PJ (2004) An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychol Methods 9(4):466\u0026ndash;491. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/1082-989X.9.4.466\u003c/span\u003e\u003cspan address=\"10.1037/1082-989X.9.4.466\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/002224378101800104\u003c/span\u003e\u003cspan address=\"10.1177/002224378101800104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGil-Perez D, Carrascosa J (1990) What to do about science misconceptions. Sci Educ 74(5):531\u0026ndash;540. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/sce.3730740504\u003c/span\u003e\u003cspan address=\"10.1002/sce.3730740504\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGriffard PB, Wandersee JH (2001) The two-tier instrument on photosynthesis: What does it diagnose? Int J Sci Educ 23(10):1011\u0026ndash;1031. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09500690010025074\u003c/span\u003e\u003cspan address=\"10.1080/09500690010025074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGul S, Yilmaz M, Gul A (2024) Identification of the students\u0026rsquo; misconceptions about the digestive system. Sci Insights Educ Front 21(2):3409\u0026ndash;3434. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15354/sief.24.or561\u003c/span\u003e\u003cspan address=\"10.15354/sief.24.or561\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHammer D (2000) Student resources for learning introductory physics. Am J Phys 68(S1):52\u0026ndash;S59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1119/1.19520\u003c/span\u003e\u003cspan address=\"10.1119/1.19520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaslam F, Treagust D (1987) Diagnosing secondary students\u0026rsquo; misconceptions of photosynthesis and respiration in plants using a two-tier multiple choice instrument. J Biol Educ 21(3):203\u0026ndash;211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00219266.1987.9654897\u003c/span\u003e\u003cspan address=\"10.1080/00219266.1987.9654897\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHewson MG, Hewson PW (1983) Effect of instruction using students\u0026rsquo; prior knowledge and conceptual change strategies on science learning. J Res Sci Teach 20(8):731\u0026ndash;743. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/tea.3660200804\u003c/span\u003e\u003cspan address=\"10.1002/tea.3660200804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson RL, Morgan GB (2016) Survey scales: A guide to development, analysis, and reporting. Guilford Press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaiser HF (1974) An index of factorial simplicity. Psychometrika 39(1):31\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02291575\u003c/span\u003e\u003cspan address=\"10.1007/BF02291575\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlem L (2000) Structural equation modeling. In: Grimm L, Yarnold P (eds) Reading and understanding multivariate statistics. American Psychological Association, pp 227\u0026ndash;286\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeonard MJ, Kalinowski ST, Andrews TC (2014) Misconceptions yesterday, today, and tomorrow. CBE\u0026mdash;Life Sci Educ 13(2):179\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1187/cbe.13-12-0244\u003c/span\u003e\u003cspan address=\"10.1187/cbe.13-12-0244\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevy Nahum T, Mamlok-Naaman R, Hofstein A, Taber KS (2010) Teaching and learning the concept of chemical bonding. Stud Sci Educ 46(2):179\u0026ndash;207. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03057267.2010.504548\u003c/span\u003e\u003cspan address=\"10.1080/03057267.2010.504548\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin S-W (2004) Development and application of a two-tier diagnostic test for high school students\u0026rsquo; understanding of flowering plant growth and development. Int J Sci Math Educ 2(2):175\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10763-004-6484-y\u003c/span\u003e\u003cspan address=\"10.1007/s10763-004-6484-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin S, Chang Y, Liew K, Chu C (2016) Effects of concept map extraction and a test-based diagnostic environment on learning achievement. Br J Edu Technol 47(4):649\u0026ndash;664. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/bjet.12250\u003c/span\u003e\u003cspan address=\"10.1111/bjet.12250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMasters GN (1988) Item discrimination: When more is worse. J Educ Meas 25(1):15\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1745-3984.1988.tb00288.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1745-3984.1988.tb00288.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePadgett RN, Morgan GB (2020) Using the eRm package for Rasch modeling. Measurement: Interdisciplinary Res Perspect 18(3):163\u0026ndash;176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15366367.2020.1732155\u003c/span\u003e\u003cspan address=\"10.1080/15366367.2020.1732155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePosner GJ, Strike KA, Hewson PW, Gertzog WA (1982) Accommodation of a scientific conception: Toward a theory of conceptual change. Sci Educ 66(2):211\u0026ndash;227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/sce.3730660207\u003c/span\u003e\u003cspan address=\"10.1002/sce.3730660207\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePritchard A (2017) \u003cem\u003eWays of learning: Learning theories for the classroom\u003c/em\u003e (4th ed.). Routledge. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9781315460611\u003c/span\u003e\u003cspan address=\"10.4324/9781315460611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRasch G (1960) /1980 \u003cem\u003eProbabilistic models for some intelligence and attainment tests.\u003c/em\u003e Copenhagen: Danish Institute for Educational Research. (Expanded English ed., 1980, University of Chicago Press). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7208/chicago/9780226706458.001.0001\u003c/span\u003e\u003cspan address=\"10.7208/chicago/9780226706458.001.0001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoswati N, Rustaman N, Nugraha I (2019) The development of science comic in human digestive system topic for junior high school students. \u003cem\u003eJournal of Physics: Conference Series, 1318\u003c/em\u003e(1), 012052. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1742-6596/1318/1/012052\u003c/span\u003e\u003cspan address=\"10.1088/1742-6596/1318/1/012052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSijtsma K (2009) On the use, the misuse, and the very limited usefulness of Cronbach\u0026rsquo;s alpha. Psychometrika 74(1):107\u0026ndash;120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11336-008-9101-0\u003c/span\u003e\u003cspan address=\"10.1007/s11336-008-9101-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith JP, diSessa AA, Roschelle J (1994) Misconceptions reconceived: A constructivist analysis of knowledge in transition. J Learn Sci 3(2):115\u0026ndash;163. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1207/s15327809jls0302_1\u003c/span\u003e\u003cspan address=\"10.1207/s15327809jls0302_1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuprapto N (2020) Do we experience misconceptions? An ontological review of misconceptions in science. Stud Philos Sci Educ 1(1):1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.33550/spsed.2020.005\u003c/span\u003e\u003cspan address=\"10.33550/spsed.2020.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaber KS (2006) Beyond constructivism: The progressive research programme into learning science. Stud Sci Educ 42(1):125\u0026ndash;184. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03057260608560222\u003c/span\u003e\u003cspan address=\"10.1080/03057260608560222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTippett CD (2010) Refutation text in science education: A review of two decades of research. Int J Sci Math Educ 8(4):951\u0026ndash;970. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10763-010-9203-x\u003c/span\u003e\u003cspan address=\"10.1007/s10763-010-9203-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTreagust DF (1988) Development and use of diagnostic tests to evaluate students\u0026rsquo; misconceptions in science. Int J Sci Educ 10(2):159\u0026ndash;169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/0950069880100204\u003c/span\u003e\u003cspan address=\"10.1080/0950069880100204\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUğur UK (2010) The determination of the high school student\u0026rsquo;s misconceptions related with the digestive system by two tier test. Master thesis, Sel\u0026ccedil;uk University, Konya\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVosniadou S (2020) Students\u0026rsquo; misconceptions and science education. In \u003cem\u003eOxford Research Encyclopedia of Education\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/acrefore/9780190264094.013.1166\u003c/span\u003e\u003cspan address=\"10.1093/acrefore/9780190264094.013.1166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright BD, Mok MMC (2004) An overview of the family of Rasch measurement models. In: Smith EV, Smith RM (eds) Introduction to Rasch measurement. JAM, pp 1\u0026ndash;24\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Erciyes University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"science education, misconceptions, diagnostic assessment, factor analysis, Rasch modeling","lastPublishedDoi":"10.21203/rs.3.rs-7707356/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7707356/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding students' misconceptions in science requires instruments that are both pedagogically meaningful and psychometrically sound. This study presents a comprehensive validation of the 20‑item Digestive System Concept Scale (DSCS) administered to 7th‑grade students in Turkey. The scale was designed to identify persistent misconceptions about digestive processes, combining content validity from curriculum and expert review with two‑stage multiple‑choice items scored dichotomously. Data from 412 students were analyzed. Exploratory factor analysis (EFA) on tetrachoric correlations suggested one to two latent factors. Confirmatory factor analysis (CFA) using weighted least squares means and variance adjusted (WLSMV) estimation favored a two‑factor model (CFI\u0026thinsp;=\u0026thinsp;0.997, TLI\u0026thinsp;=\u0026thinsp;0.996, RMSEA\u0026thinsp;=\u0026thinsp;0.009, SRMR\u0026thinsp;=\u0026thinsp;0.070) over a one‑factor alternative (CFI\u0026thinsp;=\u0026thinsp;0.978, TLI\u0026thinsp;=\u0026thinsp;0.975, RMSEA\u0026thinsp;=\u0026thinsp;0.024, SRMR\u0026thinsp;=\u0026thinsp;0.076). Rasch modeling suggested unidimensionality (nonvital person left out reliabilities), and unidimension person reliabilities equals to 0. 706 while span for item difficulties within the range of -0.85 and 0.786 logits. The correlation between factors was high, suggesting that student responses are governed by a single underlying proficiency with two interpretable facets.. Six items exceeded standardized fit thresholds, but their mean‑square statistics remained acceptable; no pair of items showed residual correlations above 0.20. Together, the findings support reporting a single Rasch total score while retaining the two‑factor structure for interpretive purposes. We discuss implications for instrument design, instructional practice, and future research.\u003c/p\u003e","manuscriptTitle":"Validation of the Digestive System Concept Scale for 7th‑Grade Students: Integrating Factor Analysis and Rasch Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 13:03:06","doi":"10.21203/rs.3.rs-7707356/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ffd10313-fe3e-499b-9c0f-bb4b590af8c0","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T13:03:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 13:03:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7707356","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7707356","identity":"rs-7707356","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}