An improved version of systematic sampling design for use with linear trend data.

Muhammad Azeem, Sundus Hussain, Musarrat Ijaz, Najma Salahuddin, Abdul Salam

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In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

5 authors.

Muhammad AzeemDepartment of Statistics, University of Malakand, Chakdara, Khyber Pakhtunkhwa, Pakistan.

Sundus HussainDepartment of Statistics, Shaheed Benazir Bhutto Women University, Peshawar, Pakistan.

Musarrat IjazDepartment of Statistics, Rawalpindi Women University, Rawalpindi, Pakistan.

Najma SalahuddinDepartment of Statistics, Shaheed Benazir Bhutto Women University, Peshawar, Pakistan.

Abdul SalamDepartment of Statistics, University of Malakand, Chakdara, Khyber Pakhtunkhwa, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In survey sampling, systematic sampling design has attracted survey researchers in recent years due to its simplicity of use. We introduce a modified variant of systematic sampling scheme which improves the efficiency of a recently developed diagonal systematic sampling method. The suggested modification is also found to be more efficient than the other popular sampling designs in circumstances where the units of the population under consideration exhibit an increasing or decreasing perfect or near-perfect linear trend. Moreover, it is observed that the conditions for efficiency are mathematically strong and practically always hold, hence making the suggested sampling design preferable over the available sampling designs.

Indexed as

62D0565C05Diagonal systematic samplingEfficiencyLinear trendMathematics Subject Classification 2020Milk yield dataSample surveys

Identifiers

PMID37484426

DOI10.1016/j.heliyon.2023.e17121

PMCPMC10361301

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.