Development and standardization of a cognitive-domain knowledge instrument for wool and pelt production

Introduction

Sheep rearing, as a component of small ruminant farming, is deeply embedded in the rural livelihood systems of India and serves as a vital income source for resource-poor farmers, especially in semi-arid and hilly regions such as Jammu Division (Kumar et al., 2010) [6] . The sector contributes not only to meat and milk production but also yields wool and pelts, which are economically significant by-products supporting both cottage and industrial enterprises (DAHD, 2021). Among livestock products, wool has particular importance in temperate regions and contributes to rural employment through its involvement in shearing, sorting, grading, and processing (Singh et al., 2015) [9] . Despite its potential, the wool and pelt sub-sector in Jammu Division remains underdeveloped. Traditional practices continue to dominate sheep husbandry, and the adoption of scientific methods remains limited due to low awareness and knowledge among sheep farmers (Sharma et al., 2018). Scientific practices such as proper timing and method of shearing, fleece grading, hygienic storage, pelt curing, and ectoparasite control have been shown to significantly enhance the quality and market value of wool and pelts (Bhat and Mir, 2017). However, these benefits can only be realized if farmers possess adequate knowledge and are trained in these techniques. The successful transfer and adoption of such technologies depend largely on understanding the existing knowledge levels and attitudes of the farmers (Reddy, 2003) [7] . Therefore, there is a need to develop a standardized tool that can objectively measure farmers' knowledge about scientific wool and pelt production, considering various aspects of sheep husbandry viz. housing, breeding, feeding, health care, government schemes, training and capacity building, marketing, ITKs and pelt production.

Materials and Methods Collection and edition of items

The knowledge test was developed following the procedure laid out by Edwards (1957) [3] . A detailed review was done from diverse sources such as reference books, field-level extension personnel, subject matter experts, scientists, professional colleagues, and the researcher's own practical experience in scientific sheep husbandry. Initially, 158 statements were developed encompassing key elements of scientific wool and pelt production. After preliminary screening and expert consultation, 91 statements were retained according to criteria given by Edwards (1957) [3] .

Results and Discussion

The relevancy testing of items was done by the method described by Verma et al (2025) [12] . Items with a mean www.extensionjournal.com relevancy score of 2.44 or higher and a relevancy weightage greater than 0.80 were retained for item analysis (Table 1).

In this way, a total 64 items were found to be most relevant by the experts and selected for further analysis.

Relevancy Testing of Items

Relevance refers to the extent to which test items effectively measure the intended knowledge domain, ensuring their suitability and significance in line with the test's objectives. The set of 91 statements were submitted to 150 subject matter experts via Google Forms and personal communication. These experts included scientists and researchers from ICAR institutes, SAUs, and other relevant research institutions. A total of 72 complete responses were received, with 18 offline and 54 online submissions. They rated each item as 'most relevant,' 'relevant,' or 'not relevant,' assigning scores of 3, 2, and 1, respectively (Subrahmanyeswari and Chander, 2008) [10] . Based on their feedback, three metrics were computed for each item: Relevancy Weightage (RW), Relevancy Percentage (RP) and Mean Relevancy Score (MRS), using the formulas below: Where, MRS = Mean Relevancy Score Finally, items with a mean relevancy score of 2.44 or higher and a relevancy weightage greater than 0.80 were retained for item analysis (Verma et al., 2025) [12] . In this way, a total 64 items were found to be most relevant by the experts and selected for further analysis.

Item Analysis

Based on the relevancy scores, 64 items were considered appropriate for field testing. The selected 64 statements were administered to 30 sheep rearers across villages of different districts. Responses were scored dichotomously (1 = correct, 0 = incorrect), and each respondent's total knowledge score was calculated as the sum of correct responses. Thus, the range of obtainable score was 0-64.

Difficulty Index and Discrimination Index

Item analysis was performed using both the difficulty index and the discrimination index. These indices provide valuable insights into each item's effectiveness: the difficulty index reflects how challenging an item is, while the discrimination index indicates how well an item distinguishes between more knowledgeable and less knowledgeable farmers. After computing the scores for all 30 respondents, their scores were ranked in descending order and grouped into six equal categories (G1 to G6), with five respondents in each group. To calculate the discrimination index, only the extreme groups, G1 and G2 (high scorers) and G5 and G6 (low scorers) were used, while the middle two groups (G3 and G4) were excluded from the analysis.

Difficulty index was measured in terms of percentage of correct responses obtained for a particular question. This was calculated by using the formula:

Where, Pi = Difficulty index in percentage of the i th item ni = Number of respondents giving correct answer, to i th item Ni = Total number of respondents to whom i th item was administered i.e. 30 in the present study. Discrimination index(E 1/3 ) indicates how effectively an item can distinguish between high and low performers on the overall test. It is typically calculated by comparing the performance of the upper and lower groups of respondents. It was calculated following Mehta (1958), using responses from only the top and bottom 25% of respondents. It was calculated by the following formula:

Where, S1, S2, S5 and S6 = Frequencies of correct answers in G1, G2, G5 and G6 groups respectively N = Total number of respondents in the sample of item analysis Based on the results of difficulty and discrimination index, final selection of items was done. Two criteria viz. item difficulty (30-80) and item discrimination index (0.30-0.55) had been considered for selection of items in the final format of the knowledge test as described by Mehta (1958). A total of. Table 2 reveals the selected and eliminated statements based on difficulty index and discrimination index. Table 3 thus reveals the final 47 items were included in the final format of the knowledge test. Therefore, the overall possible knowledge score of the individual respondent wool and pelt production ranges from 0 to 47. A red-hot iron brought near the nose and feeding boiled long grasses to treat dysentery in animals I.

Breed characteristics is the main genetic factor influencing pelt quality 42.

Winter is best for obtaining high-quality pelts in temperate regions 43. 1-2 days is the minimum duration for salting a pelt before further processing to preserve it by preventing bacterial growth. 44.

5-10% is the ideal moisture content of pelts after drying 45.

Removal of excess fat and meat is the first step immediately after skinning the sheep 46.

Pelts come from young animals, while hides come from older ones. Yes/No 47.

Pelts from younger lambs usually more valuable softer and finer. Yes/no

Reliability and Internal Consistency

The reliability of the knowledge test was assessed using multiple methods to ensure the consistency and dependability of the instrument. The split-half method was used to evaluate the internal consistency of the test. All 47 items were administered to a sample of 30 sheep rearers. The items were divided into two set odd-numbered and even-numbered items and the scores from both sets were correlated. The reliability coefficient obtained through this method was 0.826, indicating a high level of internal consistency. To further enhance the accuracy of the reliability estimate, the Spearman-Brown Prophecy Formula was applied to the split-half correlation. The adjusted reliability coefficient was calculated to be 0.903, which confirms that the test possesses a high degree of reliability. The internal consistency of the test was also assessed using Cronbach's alpha (𝛼). The value was found to be 0.88, which is considered excellent, and was statistically significant at the p < 0.05 level. This further supports the consistency and reliability of the knowledge test. Although Cronbach's Alpha is commonly used for continuous or Likert-type scales, it can be approximated for dichotomous items like those in this test. The general rule of thumb is that a Cronbach's alpha of 0.70 and above is good, 0.80 and above is better, and 0.90 and above is best (Kumar et al., 2021) [5] . The high Spearman-Brown value suggests that Cronbach's alpha would likely fall between 0.88-0.91, also indicating excellent internal consistency (Tavakol & Dennick, 2011) [11] .

Content Validity

The content validity of the knowledge test was ensured through careful development and expert evaluation of the items. The test items were constructed based on relevant literature and practices related to scientific wool and pelt production. Inputs from subject-matter experts and experienced professionals were incorporated to ensure comprehensive coverage of all major knowledge areas. As the items were reviewed and approved by experts in the field, it is assumed that the test effectively measures the knowledge level of sheep rearers as intended.

Conflict of interest

Authors have no conflict of interest in this study.

Conclusion

The knowledge test developed through a systematic and rigorous process has proven to be a standardized, valid, and reliable tool specifically designed for assessing the knowledge level of sheep rearers in the context of wool and pelt production in the Jammu Division.

References

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