UTILISATION OF RASCH MODEL FOR THE ANALYSIS OF AN INSTRUMENT DEVELOPED BY MAPPING ITEMS TO COGNITIVE LEVELS OF MARZANO TAXONOMY

Oct 15, 2026

The article develops and validates an assessment instrument for Chemistry students’ ability in the topic of “physical bonding.” The study constructs 24 test items by mapping item content to cognitive levels defined by Marzano’s taxonomy, then evaluates the instrument with 73 students. Four items with misfit indicated by MNSQ values greater than 1.3 are removed. Final item difficulty, item separation, and reliability remain within normal ranges, while person separation and reliability suggest expanding the number of items. Results support the successful use of Marzano’s taxonomy in item development.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

1. Introduction

Item Response Theory (IRT)

1PL vs 2PL and links to Rasch

Rasch model parameters and fit statistics (Infit/Outfit)

Separation, reliability, and Wright map

Practical considerations for sample size vs parameters

UTILISATION OF RASCH MODEL FOR THE ANALYSIS OF AN INSTRUMENT DEVELOPED BY MAPPING ITEMS TO COGNITIVE LEVELS OF MARZANO TAXONOMY Roxana S. Timofte, Laura Siminiciuc Abstract: The scope this article was to develop an instrument to measure Chemistry students’ ability regarding‘physical bonding’ and to validate it. A number of 24 items were developed by mapping items to cognitive levels described by the Marzano taxonomy. A number of N=73 students were evaluated. Four items exhibited a MNSQ >1.3 and were eliminated from the final data analysis. At the final data analysis, the item difficulty measures were in normal ranges, as well as item separation and item reliability values. Person separation and person reliability values were showing that the number of items must be increased, since the instrument may not be sensitive enough to differentiate between low and high performers. Nevertheless, it was proven that the utilisation of Marzano’s taxonomy in the development of items was successful in the sense that the items had difficulty measures in normal ranges and that the items had different levels of difficulty. Key words: Rasch model, Marzano taxonomy, assessment, physical bonding, Chemistry students 1. Introduction Item Response Theory (IRT) is used for the validation of instruments and to provide data regarding the item difficulty and person ability. Many studies focused on the comparison between Classical Test Theory and Item Response Theory (for example, Hambleton & Jones, 1993; Wiberg, 2004) . The two most used IRT models are the 1-and 2-parameter logistic (1PL & 2 PL) . The probability for student i to respond correct at question / item j is calculated after the following equation (Equation 1): Equation 1. Probability for student i to respond correct to to item j-The 1PL Model Where is student’s ability and bj is the difficulty of task j (Rasch, 1960, Boone et al, 2014) . High values for indicate a high ability level and high values for b indicate difficult items. The values of item difficulty or person ability are normally in the range [-3, 3] logit. Values outside this range show that there is a problem with the measured items. Information regarding how well a particular item discriminates between students with different abilities can be also obtained. The discrimination parameter, aj, could be added, forming the 2PL model (Equation 2): Equation 2. Probability for student i to respond correct to to item j-The 2PL Model where and bj have the same meaning like in the 1PL model, and aj represents discrimination of item j. The values for aj are normally in the range [0, 2], and the higher values indicate items which differentiate better students’ ability (Sudol & Struder, 2010) . Altrough the 1PL model is sometimes associated with the Rasch model, there are differences between the Rasch model and the 1PL model (Linacre, 2005) . The Rasch model was elaborated by the Danish mathematician George Rasch in 1960. This model was developed in order to overcome the problems which appear when using classical test theory in analysis of instruments (Boone, 2016, Jackson et al, 2002) . More exactly, because the items have different difficulty levels, the raw scores obtained by summing up the correct answers can not be used to compare students ‘ability. Furthermore, Rasch technique can be used to transform the non-linear raw data in linear scales, which can then be evaluated by the utilization of statistical parametric tests. Some of the parameters considered when analyzing an instrument using the Rasch model are: item fit, item separation and reliability, person separation and reliability, Wright map, discrimination (Linacre & Wright, 2000) . Assessment of item fit is reported in two ways: INFIT and OUTFIT (Linacre & Wright, 2000, Jackson et al, 2002) . INFIT shows unexpected behavior observed at the responses near the person’s ability level. OUTFIT shows if unexpected responses or outliers are found, taken in

FAQs

What purpose does the instrument serve in the study?

It is developed to measure Chemistry students’ ability regarding “physical bonding” and to validate that instrument using the Rasch model.

How were the items constructed for the instrument?

The study created 24 items by mapping item content to cognitive levels from Marzano’s taxonomy.

Why were some items excluded from the final analysis?

Four items showed misfit with MNSQ values greater than 1.3, so they were eliminated from the final data analysis.

Oct 15, 2026

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