Background and aim: Following a correct biopsy protocol is crucial for accurate diagnosis of oral and maxillofacial lesions. Pre-analysis technical ERRORs during preparation and submission of biopsies can jeopardize the diagnostic process and the related treatment plan. The aim of the present study was to evaluate pre-analysis technical ERRORs in the biopsy samples submitted to an oral and maxillofacial pathology laboratory. Materials and methods: In this descriptive study, 166 biopsy samples submitted to the oral and maxillofacial pathology laboratory of the dental branch of Islamic Azad University of Tehran in 2013 were assessed. 13 samples were excluded due to incomplete information. 153 samples were evaluated and compared to the standard criteria with regard to four fields including: biopsy request form, storage solution, container, and quality. The frequency of ERRORs was reported with 95% confidence interval. Results: No ERRORs were detected in 19 samples (12. 4%); whereas, 123 samples (80. 4%) had incomplete biopsy request forms, 7 samples (4. 6%) had been kept in improper storage media, 2 samples (1. 3%) had unsuitable containers, and 2 biopsies (1. 3%) had poor quality. Conclusion: It seems that failure to submit a complete biopsy request form is the most common technical ERROR, which indicates the need for periodic retraining and updates regarding the biopsy protocols.

In this study, three classes of generalized and more efficient combined regression-cum-ratio estimators are presented to estimate the population mean of the study variable in stratified two-phase SAMPLING considering non-response and measurement ERROR are present jointly. The expressions for the bias and mean square ERROR of the three proposed generalized combined regression-cum-ratio estimators have been obtained. Optimal conditions which make the proposed generalized regression-cum-ratio estimators more efficient than modified combined regression estimator are discussed. The performance of the proposed generalized combined regression-cum-ratio estimators has been compared theoretically as well as empirically with various combined type estimators in stratified two-phase SAMPLING including usual combined ratio estimator, usual combined exponential ratio estimator, usual combined regression estimator, and modified combined regression estimator. An empirical study shows that the proposed generalized combined regression-cum-ratio estimators perform more efficiently than all combined type ratio, exponential ratio, and regression estimators discussed in the study.

Survey implementation is one of the common methods to data collection. Quality of survey results is of prime importance due to their applications in decision making and planning. To determine quality of survey results, it is necessary to estimate both SAMPLING and nonSAMPLING ERRORs. However, estimation of nonSAMPLING ERROR is difficult because of problem of quantifying other parts of nonSAMPLING ERROR that is nonresponse ERROR, Coverage ERROR and data processing ERROR.Response ERROR is a quantifiable part of nonSAMPLING ERROR. Therefore, modeling of the response ERROR has received a particular attention over years.In this paper, we consider some major statistical surveys which are conducted in a face to face interview and Sources of ERROR are determined, then a model of response ERROR in face to face surveys has been proposed Based on presentation of an experimental design.Furthermore, computation of precision in surveys with two stage SAMPLING designs is assessed and an estimator of precision is presented in this case. As an application.of proposed model, precision of survey estimators of interest is computed based on resulted estimator, for data of house hold in come-and expenditure survey for the year 1382.

Introduction: In qualitative research the SAMPLING process is usually determined by the methodology employed. However, this is not always evident in published qualitative research papers as many qualitative studies appear not to have a clearly defined methodological approach. Indeed, pragmatic researches focus on the need to adopt a flexible rather than rigid approach to application of qualitative methodologies. Therefore, this study was performed to review previous research to clarify qualitative SAMPLING. Valid articles and books were used in this review study.Method: The keywords qualitative research and SAMPLING were searched in Cumulative Index to Nursing and Allied Health Literature (CINAHL), ProQuest, PsycINFO, ScienceDirect, Scopus, and Medline databases.Results: Although SAMPLING methods in qualitative research are known as purposive SAMPLING, there is an extensive spectrum of SAMPLING methods such as quota, snowball, theoretical, critical cases, homogeneity, sequential, criterion, and combination SAMPLING.Conclusion: The purpose of SAMPLING in qualitative research is not to establish a random or representative sample draw from a population, but rather to identify specific groups of people, who either possess characteristics or live experiences relevant to the social phenomenon being studied. Informants are identified because they will enable the exploration of a particular aspect of behavior relevant to the research. The benefits of the qualitative approach to health care research are becoming increasingly recognized by both academics and clinicians. However, misunderstandings about the philosophical basis and the methodological approach remain. The process of SAMPLING is one of the principal areas of confusion. SAMPLING is a very complex issue in qualitative research. This is due to the many variations of qualitative SAMPLING described in the literature, and much confusion and overlapping of types of SAMPLING.