Concept-wise Practice

disaggregated data MCQ Questions for Class 12

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Practice Questions

6 questions tagged with disaggregated data.

यदि बालिका शिक्षा पर data केवल कुल संख्या में है और caste income disability के अनुसार नहीं है तो क्या छूट सकता है?

If data on girls education is only in total numbers and not by caste income or disability what may be missed?

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A. अंदरूनी असमानताओं की पहचानIdentification of internal inequalities

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disaggregated data कमजोर समूहों की समस्या दिखाता है। परीक्षा में inclusive monitoring याद रखें। / Disaggregated data shows problems of vulnerable groups. In exams remember inclusive monitoring.

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यदि gender audit केवल महिलाओं की संख्या गिनता है और वेतन पद सुरक्षा तथा contract type नहीं देखता तो क्या कमी है?

If a gender audit only counts the number of women and ignores wage position safety and contract type what is the gap?

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A. गहन असमानताओं का विश्लेषण छूट जाता हैAnalysis of deeper inequalities is missed

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केवल संख्या equality का पूरा चित्र नहीं देती। परीक्षा में disaggregated data याद रखें। / Numbers alone do not give the full picture of equality. In exams remember disaggregated data.

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यदि किसी संस्था में gender audit केवल कुल पुरुष महिला संख्या देखता है तो कौन सी कमी रह जाती है?

If a gender audit checks only total male female numbers what gap remains?

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A. अनुभव आयु पद और वेतन स्तरों का सूक्ष्म विश्लेषण छूट जाता हैDetailed analysis of experience age position and pay levels is missed

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कुल संख्या hidden inequality छिपा सकती है। परीक्षा में disaggregated data का महत्व याद रखें। / Total numbers can hide inequalities. In exams remember the importance of disaggregated data.

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किस स्थिति में gender issues को समझने के लिए disaggregated data उपयोगी होगा?

In which situation will disaggregated data help understand gender issues?

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A. जब training participation को gender और job category के अनुसार देखना होWhen training participation must be seen by gender and job category

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अलग data hidden gaps दिखाता है। परीक्षा में data analysis को policy सुधार से जोड़ें। / Separate data shows hidden gaps. In exams connect data analysis with policy correction.

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यदि workplace data में कुल महिला संख्या ठीक है पर older women की संख्या training में बहुत कम है तो क्या करना चाहिए?

If workplace data shows overall women numbers are fine but older women are very low in training what should be done?

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A. age और gender के संयुक्त data की समीक्षाReview combined data by age and gender

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कुल data hidden gaps छिपा सकता है। परीक्षा में disaggregated data को policy tool मानें। / Overall data can hide gaps. In exams treat disaggregated data as a policy tool.

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यदि older workers और women workers दोनों को digital training में कम भागीदारी मिलती है तो data analysis में क्या करना चाहिए?

If older workers and women workers both have low participation in digital training what should be done in data analysis?

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A. age और gender के अनुसार अलग तथा संयुक्त data देखनाCheck data separately and jointly by age and gender

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अलग और संयुक्त data hidden gaps दिखाते हैं। परीक्षा में disaggregated data को policy tool मानें। / Separate and joint data show hidden gaps. In exams treat disaggregated data as a policy tool.

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