An Empirical Investigation of Sampling Errors In Educational Survey Research

1979 ◽  
Vol 4 (1) ◽  
pp. 24-40 ◽  
Author(s):  
Kenneth N. Ross
Keyword(s):  
Survey Research ◽  
Error Estimation ◽  
Multivariate Statistics ◽  
Empirical Investigation ◽  
Sampling Technique ◽  
Sampling Errors ◽  
Sample Design ◽  
Estimation Techniques ◽  
Sampling Distributions ◽  
Complex Sample

This investigation examines the influence of sample design on the sampling errors of several multivariate statistics which are frequently used in educational survey research. Student’s empirical sampling technique is used to generate sampling distributions for several complex sample designs which are often used to sample schools, classrooms and students. Some results are presented for two error estimation techniques: “Jackknifing” and “Balanced Repeated Replication”.

scholarly journals Kontribusi Marketing Syariah dan Citra Lembaga Dalam Mempengaruhi Keputusan Nasabah Menabung

2020 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Nur Anim Jauhariyah ◽  
Kiki Fitria ◽  
Mahmudah Mahmudah
Keyword(s):  
Survey Research ◽  
Random Sampling ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Quantitative Approach ◽  
Institutional Image ◽  
The Public ◽  
Simultaneous Influence

The purpose of this study 1) Knowing the influence of sharia marketing (X1) on customer decisions (Y) saving; 2) Determine the magnitude of the influence of the image of the institution (X2) on the customer's decision (Y) to save; 3) Knowing the simultaneous influence between sharia marketing (X1) and institutional image (X2) on Customer Decision (Y) saving. In this study using a quantitative approach to the type of survey research. Determination of the research sample using simple random sampling technique with 30 respondents. Research conclusions 1) Sharia marketing Bank Syariah Mandiri KC Genteng Banyuwangi Regency is one of the factors that influence customers' decision to save. The better the marketing of sharia is carried out, the more interested the public will be in saving at Syariah Syariah Bank KC Genteng Banyuwangi Regency; 2) the image of the institution of Bank Syariah Mandiri KC Genteng Banyuwangi Regency is one of the factors that influence the interests of customers to save. The better the image in the minds of the public, the more interested people will be saving at the Syariah Mandiri Bank KC Genteng Banyuwangi Regency; 3) sharia marketing (X1) and institutional image (X2) are the dominant variables on customer decisions (Y) saving at Bank Syariah Mandiri KC Genteng Banyuwangi Regency.

Sampling in Research

2016 ◽  
pp. 107-121 ◽  
Author(s):  
Never Mujere
Keyword(s):  
Sample Size ◽  
Sampling Technique ◽  
Sample Size Determination ◽  
Size Determination ◽  
Practical Application ◽  
Sampling Errors ◽  
Advantages And Disadvantages ◽  
Needed Information ◽  
Different Types

Research is aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts or practical application of such new or revised theories or laws. A sample provides needed information about the population quickly. However, there is no guarantee that any sample will be precisely representative of the population from which it comes. It is cheaper to observe a part rather than the whole. This chapter is a discussion on sampling in research and it is mainly designed to equip researchers with knowledge of the general issues to consider when sampling. The purpose of sampling in research, dangers of sampling and how to minimize them, types of sampling and guides for deciding the sample size are discussed. For a clear flow of ideas, a few definitions of the terms used are given. They highlight the types and methods of sampling, sampling errors and discusses techniques of sample size determination. Different types of sampling technique, how to carry them out, and their advantages and disadvantages are also introduced.

How to use replicate weights in health survey analysis using the National Nutrition and Physical Activity Survey as an example

2019 ◽  
Vol 22 (18) ◽  
pp. 3315-3326
Author(s):  
Carole L Birrell ◽  
David G Steel ◽  
Marijka J Batterham ◽  
Ankur Arya
Keyword(s):  
Physical Activity ◽  
Health Survey ◽  
Large Scale ◽  
Sample Design ◽  
Sampling Weights ◽  
Complex Sample ◽  
Unbiased Estimates ◽  
Nutrition And Physical Activity ◽  
Activity Survey ◽  
Complex Sample Design

AbstractObjective:To conduct nutrition-related analyses on large-scale health surveys, two aspects of the survey must be incorporated into the analysis: the sampling weights and the sample design; a practice which is not always observed. The present paper compares three analyses: (1) unweighted; (2) weighted but not accounting for the complex sample design; and (3) weighted and accounting for the complex design using replicate weights.Design:Descriptive statistics are computed and a logistic regression investigation of being overweight/obese is conducted using Stata.Setting:Cross-sectional health survey with complex sample design where replicate weights are supplied rather than the variables containing sample design information.Participants:Responding adults from the National Nutrition and Physical Activity Survey (NNPAS) part of the Australian Health Survey (2011–2013).Results:Unweighted analysis produces biased estimates and incorrect estimates of se. Adjusting for the sampling weights gives unbiased estimates but incorrect se estimates. Incorporating both the sampling weights and the sample design results in unbiased estimates and the correct se estimates. This can affect interpretation; for example, the incorrect estimate of the OR for being a current smoker in the unweighted analysis was 1·20 (95 % CI 1·06, 1·37), t= 2·89, P = 0·004, suggesting a statistically significant relationship with being overweight/obese. When the sampling weights and complex sample design are correctly incorporated, the results are no longer statistically significant: OR = 1·06 (95 % CI 0·89, 1·27), t = 0·71, P = 0·480.Conclusions:Correct incorporation of the sampling weights and sample design is crucial for valid inference from survey data.

An Empirical Comparison of Two Telephone Sample Designs

1978 ◽  
Vol 15 (4) ◽  
pp. 622-631 ◽  
Author(s):  
Robert M. Groves
Keyword(s):  
Sampling Errors ◽  
Empirical Comparison ◽  
Sample Design ◽  
Variance Model ◽  
Design Effects ◽  
Cost Variance

The clustered telephone sample design described by Waksberg is compared with a design randomly generating four digit numbers within working prefixes. The clustered sample is found to increase the proportion of working household numbers selected from about 22% to over 55%, but sampling errors and design effects of the two sample designs show some loss of precision in the clustered design. A cost-variance model is constructed which provides estimates of desirable cluster sizes given varying amounts of intracluster homogeneity.

scholarly journals Synthetic Multiple-Imputation Procedure for Multistage Complex Samples

2016 ◽  
Vol 32 (1) ◽  
pp. 231-256 ◽  
Author(s):  
Hanzhi Zhou ◽  
Michael R. Elliott ◽  
Trivellore E. Raghunathan
Keyword(s):  
Multiple Imputation ◽  
Survey Design ◽  
Efficient Estimation ◽  
Sample Design ◽  
Sampling Weights ◽  
Design Data ◽  
Item Level ◽  
Complex Sample ◽  
Analytic Strategy ◽  
Modeling Strategy

Abstract Multiple imputation (MI) is commonly used when item-level missing data are present. However, MI requires that survey design information be built into the imputation models. For multistage stratified clustered designs, this requires dummy variables to represent strata as well as primary sampling units (PSUs) nested within each stratum in the imputation model. Such a modeling strategy is not only operationally burdensome but also inferentially inefficient when there are many strata in the sample design. Complexity only increases when sampling weights need to be modeled. This article develops a generalpurpose analytic strategy for population inference from complex sample designs with item-level missingness. In a simulation study, the proposed procedures demonstrate efficient estimation and good coverage properties. We also consider an application to accommodate missing body mass index (BMI) data in the analysis of BMI percentiles using National Health and Nutrition Examination Survey (NHANES) III data. We argue that the proposed methods offer an easy-to-implement solution to problems that are not well-handled by current MI techniques. Note that, while the proposed method borrows from the MI framework to develop its inferential methods, it is not designed as an alternative strategy to release multiply imputed datasets for complex sample design data, but rather as an analytic strategy in and of itself.

ENSURING QUALITY IN SURVEY RESEARCH

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Gopi K Khanal
Keyword(s):  
Survey Research ◽  
Survey Data ◽  
Reliability And Validity ◽  
Total Quality ◽  
Sampling Errors ◽  
Research Survey ◽  
The Social ◽  
Validity Tests ◽  
Survey Approach

This descripto-analytical paper on ensuring quality in survey research discusses the management of errors in administering survey. This paper aims to help the social science researchers to ensure the quality in the process and outcomes of survey research. It begins with the brief conceptual underpinnings of survey research, discusses about reliability and validity tests in survey, elaborates the notion of total survey error approach, and suggests some measures on handling survey errors. Given the wider applications and substantial costs associated with survey research, the issues of sampling and non-sampling errors have always been major concerns in the quality of survey research. Survey research can be instrumental in generating knowledge provided survey errors are handled properly. Though a variety of measures are in practices to ensure quality of survey data, this paper gives importance on total survey approach that gives emphasis on total quality management in the collection, analysis, and interpretation of data. Dealing survey data from the perspective of total survey approach would yield fruitful results from survey research.

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