• The Mathematical Education
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• v.56 no.1
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• pp.19-39
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• 2017

Taking samples of data and using samples to make inferences about unknown populations are at the core of statistical investigations. So, an understanding of the nature of sample as statistical thinking is involved in the area of statistical literacy, since the process of a statistical investigation can turn out to be totally useless if we don't appreciate the part sampling plays. However, the conception of sampling is a scheme of interrelated ideas entailing many statistical notions such as repeatability, representativeness, randomness, variability, and distribution. This complexity makes many people, teachers as well as students, reason about statistical inference relying on their incorrect intuitions without understanding sample comprehensively. Some research investigated how the concept of a sample is understood by not only students but also teachers or preservice teachers, but we want to identify preservice secondary mathematics teachers' understanding of sample as the statistical literacy by a qualitative analysis. We designed four items which asked preservice teachers to write their understanding for sampling tasks including representativeness and variability. Then, we categorized the similar responses and compared these categories with Watson's statistical literacy hierarchy. As a result, many preservice teachers turned out to be lie in the low level of statistical literacy as they ignore contexts and critical thinking, expecially about sampling variability rather than sample representativeness. Moreover, the experience of taking statistics courses in university did not seem to make a contribution to development of their statistical literacy. These findings should be considered when design preservice teacher education program to promote statistics education.

• School Mathematics
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• v.17 no.3
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• pp.515-530
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• 2015

The concepts of sample and sampling are central to make a statistically correct decision, so we need to be emphasized their importance in the statistics education. Nevertheless, there were not enough studies which discuss how to teach the concepts of sample and sampling. In this study, teaching sample and sampling is addressed by foreign curricula and cases of instruction in order to obtain suggestions for teaching sample and sampling. In particular, the curricular of Australia, New Zealand, England and the United States are analyzed, considering the sample representativeness and the sampling variability; the two elements in the concept of sample. Also foreign textbooks and cases of instruction when it comes to teach sample are analyzed. The results say that with respect to teach sample can be divided into four suggestions: first, sample was taught in the process of statistical inquiry such as data collection, analysis, and results. Second, sample was introduced earlier than Korea curriculum. Third, when it comes to teach sample, sample variability, as well as sample representativeness was considered. Fourth, technological tools were used to enhance understanding sample.

• School Mathematics
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• v.16 no.4
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• pp.727-743
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• 2014

The concepts of sample and sampling are central to the statistical thinking and foundations of the statistical literacy, so we need to be emphasized their importance in the statistics education. However, many researches which dealt with samples only analyze textbooks or students' responses. In this study, the concept of sample is addressed by a historical consideration which is one aspect of the didactical analysis. Moreover, developing concept of sample is analyzed from the preceding studies about the statistical literacy, considering the sample representativeness and the sampling variability. The results say that the historical process of developing the concept of sample can be divided into three step: understanding the sample representativeness; appearing the sample variance; recognizing the sampling variability. Above all, it is important to aware and control the sampling variability, but many related researches might not consider sample variability. Therefore, it implies that the awareness and control of sampling variability are needed to reflect to the teaching-learing of sample for developing the students' statistical literacy.

• Journal of the Korean association of regional geographers
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• v.2 no.2
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• pp.49-73
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• 1996

This study produces quantitatively weather entropy and information ratio using information theory about frequency in the appearance of precipitation phenomenon and monthly change, and then applies them to observation of the change of their space scale by time. As a result of these, this study defines Pusan, Chongju and Kwangju's weather representativeness and then establishes the range of weather representativeness. Based on weather entropy (statistical parameter)-the amount of average weather information-and information ratio, we can define each area's weather representativeness, which can show us more constant form included topographical, geographical factors and season change. The data used for this study are the daily precipitotion and cloudiness during the recent five years($1990{\sim}1994$) at the 69 stations in Korea. It is divided into class of no precipitation, that of precipitation. The results of this study can be summarized as follows: (1) The four season's mean value of information ratio is the highest value. as 0.641, on the basis of Chongju. It is the lowest as 0.572, on the basis of Pusan. On a seasonal basis, the highest mean value of information rate is April's (spring) in Chongju, and the lowest is October's(fall) in Pusan. Accordingly weather representativeness has the highest in Chongju and the lowest in Pusan. (2) To synthesize information ratio of decaying tendancy and half-decay distance, Chonju's weather representativeness has the highest in April, July and October. And kwangju has the highest value in January and the lowest in April and July. Pusan's weather representativeness is not high, that of Pusan's October is the lowest in the year. (3) If we establish the weather representative character on the basis of Chongju-Pusan, the domain of Chongju area is larger than that of Pusan area in October, July and April in order. But Pusan's is larger than Chongju's in January. In the case of Chongju and Kwangju, the domain of Chongju area is larger than that of Kwangju in October, July and April in order, but it is less than that of Kwangju area in January. In the case of Kwangju-Pusan, the domain of Kwangju is larger than that of Pusan in October, July in order. But in April it is less than Pusan's.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.47-64
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• 2008

It is well known that consumer's quality perception is strongly affected by price. Higher priced products tend to be perceived to have better quality than lower priced products although the objective product quality is the same. However, it is less known the process through which quality perception is affected by price cues. The existing literature suggests three potential hypotheses (i.e., the selectiveprocessing hypothesis, the selective interpretation hypothesis, and the representativeness heuristic hypothesis) that explain the underlying processes of the price-induced quality perception. The current research tests among the three competing hypotheses and also examines the role of consumer knowledge as a moderating factor. An experiment was conducted to test the moderating role of the knowledge in the price-quality relationship and to investigate the underlying process. The results indicate that theinfluence of price on perceived quality differs between novices and experts. Expert consumers' quality perception is not significantly influenced by price and this finding is consistent with the extant literature. On the other hand, novice consumers' quality perception is affected by price and the tests on the underlying process support for the representativeness heuristic hypothesis. Novice consumers assess that a high-priced brand should have good product quality due to the belief that high (low) price brands represent better (worse) brand quality and such a representativeness heuristic occurs without involving selective attention or selective interpretation price-consistent information.

• ETRI Journal
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• v.22 no.2
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• pp.20-31
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• 2000

Much research on natural language processing (NLP), computational linguistics and lexicography has relied and depended on linguistic corpora. In recent years, many organizations around the world have been constructing their own large corporal to achieve corpus representativeness and/or linguistic comprehensiveness. However, there is no reliable guideline as to how large machine readable corpus resources should be compiled to develop practical NLP software and/or complete dictionaries for humans and computational use. In order to shed some new light on this issue, we shall reveal the flaws of several previous researches aiming to predict corpus size, especially those using pure regression or curve-fitting methods. To overcome these flaws, we shall contrive a new mathematical tool: a piecewise curve-fitting algorithm, and next, suggest how to determine the tolerance error of the algorithm for good prediction, using a specific corpus. Finally, we shall illustrate experimentally that the algorithm presented is valid, accurate and very reliable. We are confident that this study can contribute to solving some inherent problems of corpus linguistics, such as corpus predictability, compiling methodology, corpus representativeness and linguistic comprehensiveness.

• Journal of Information Management
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• v.23 no.4
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• pp.1-20
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• 1992

In this study, titles, abstracts, and tables of content were evaluated and compared. The evaluation of the representativeness of surrogates and the evaluation through retrieval experiments were carried out in this study. Through the two experiments it was proved that titles are the best surrogate to condense information but they can't contain sufficient information because of the degree of condensation. Abstracts and tables of content are more effective than titles, while tables of content are the most effective.

• Language and Information
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• v.7 no.2
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• pp.103-115
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• 2003

In recent years, many organizations have been constructing their own large corpora to achieve corpus representativeness. However, there is no reliable guideline as to how large corpus resources should be compiled, especially for Korean corpora. In this study, we have contrived a new statistical model, ARIMA (Autoregressive Integrated Moving Average), for predicting the relationship between linguistic items (the number of types) and corpus size (the number of tokens), overcoming the major flaws of several previous researches on this issue. Finally, we shall illustrate that the ARIMA model presented is valid, accurate and very reliable. We are confident that this study can contribute to solving some inherent problems of corpus linguistics, such as corpus predictability, corpus representativeness and linguistic comprehensiveness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.91-97
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• 2016

This study is concerned about the process capability index in single process. Previous process capability indices have been developed for the consistency with the nonconforming rate due to the process target value and skewness. These indices calculate the process capability by measuring one spot in an item. But the only one datum in an item reduces the representativeness of the item. In addition to the lack of representativeness, there are many cases that the uniformity of the item such as flatness of panel is absolutely important. In these cases, we have to measure several spots in an item. Also the nonconforming judgment to an item is mainly due to the range not due to the standard variation or the shift from the specifications. To imply the uniformity concept to the process capability index, we should consider only the variation in an item. It is the within subgroup variation. When the universe is composed of several subgroups, the sample standard deviation is the sum of the within subgroup variation and the between subgroup variation. So the range R which represents only the within subgroup variation is the much better measure than that of the sample standard deviation. In general, a subgroup contains a couple of individual items. But in our cases, a subgroup is an item and R is the difference between the maximum and the minimum among the measured data in an item. Even though our object is a single process index, causing by the subgroups, its analytic structure looks like a system process capability index. In this paper we propose a new process capability index considering the representativeness and uniformity.

• Asian Journal for Public Opinion Research
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• v.9 no.2
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• pp.188-211
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• 2021

Decreasing response rates to traditional survey methods, like face-to-face and telephone interviews, have led survey practitioners around the world to seek new ways of conducting surveys in recent years." The COVID-19 pandemic exacerbated this problem because it made conducting face-to-face interviews even more difficult than before. For example, it made conducting face-to-face surveys infeasible in 2020 in South Korea, and so the Korean Academic Multimode Open Survey (KAMOS) was unable to conduct a planned face-to-face survey to recruit new panel members. The entire 8,514-member panel, established via two-stage probability-based sampling from 2016 to 2019, was invited to take three online/telephone surveys in 2020. Of these panel members, 1,352 responded to at least one survey in 2020. To test to what extent the panel remained representative of the adult South Korean population, we compared the two groups of panel members: those who responded to at least one survey in 2020 and those who did not. After weighting both groups on the basis of age, sex, and geographical area, we analyzed their responses to some of the questions that were asked during multiple rounds of the face-to-face panel-recruiting interviews. Using Cohen's d for survey items that could be analyzed numerically and Cramér's V for categorical items, we were able to conclude that the respondents to the 2020 surveys were equivalent to the non-respondents in terms of both demographics and in the answers they originally gave to substantive questions on a variety of topics related to social science or public opinion research, including questions about quality of life, societal issue, and politics (Cohen's d items <0.2, 95% CI; Cramér's V items <0.1, 95% CI). This analysis may provide a model for others who wish to test the continued representativeness of their panel or who would like to use a different survey mode or change some other aspect of their methodology and test whether it is equivalent to their former methodology. Our success in building a panel that retained its representativeness may be useful to those in other countries where face-to-face surveys had previously been the norm but are becoming increasingly difficult to conduct.