Spatial Linear Regression from Census Microdata: Combining Microdata and Small Area Data

2009 ◽  
Vol 41 (9) ◽  
pp. 2215-2231 ◽  
Author(s):  
Nicholas N Nagle
Keyword(s):  
Social Sciences ◽  
Regression Models ◽  
Small Area ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Geographic Scale ◽  
Density Maps ◽  
Neighborhood Scale ◽  
Source Of Information ◽  
Area Data

Census microdata have become an extremely valuable source of information in social sciences research. These data, however, must have very coarse geographic resolution in order to protect respondent anonymity. Thus the geographic scale of these microdata sources is drastically different from the scale of many spatial processes—particularly neighborhood-scale processes. It is suggested that this difference in geographic scales creates a problem of conclusion validity for regression models which use anonymized microdata: measures of statistical significance are biased in these models. A correction to this problem in which small area data and population-density maps are used to estimate the effects of spatial dependence is presented. Monte Carlo evidence is presented which demonstrates that the conclusion-validity problem may be severe in practice. Further, this evidence shows that the suggested correction with small area data restores conclusion validity to statistical tests.

scholarly journals Small-area data on socioeconomic status and immigrant groups for evaluating equity of early cancer detection and care

2021 ◽  
pp. 1-6
Author(s):  
Ulf Strömberg ◽  
Brandon L. Parkes ◽  
Amir Baigi ◽  
Carl Bonander ◽  
Anders Holmén ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Cancer Detection ◽  
Small Area ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Area Data

Use of Small-area Data to Support a Community Agenda in Boston Chinatown

2002 ◽  
Vol 7 (2) ◽  
pp. 203-219
Author(s):  
Doug Brugge ◽  
Martha Tai
Keyword(s):  
Small Area ◽  
Area Data

scholarly journals Use of a Simple Anthropometric Measurement to Identify Low-Birth-Weight Infants in Enugu, Nigeria

2018 ◽  
Vol 5 ◽  
pp. 2333794X1878817
Author(s):  
Ndubuisi Kennedy Chukwudi ◽  
Huldah Ijeoma Nwokeukwu ◽  
Gilbert Nwadiakanma Adimorah
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Linear Regression Analysis ◽  
Cutoff Point ◽  
Chest Circumference ◽  
Anthropometric Measurement ◽  
Suitable Alternative ◽  
Study Population

Background. This prospective study was conducted to identify a suitable alternative to birth weight and establish its cutoff point to facilitate the identification of low-birth-weight (LBW) infants in Enugu, Southeast Nigeria. Methods. The study involved newborn babies within the first 48 hours of life. Five anthropometric measurements (head, chest, mid-arm and calf circumferences, as well as abdominal girth) were taken using a tape measure while supine length was measured with an aluminum infantometer. Birth weight was also recorded. Linear regression analysis was done to identify the measurement with the highest coefficient of determination with birth weight while its cutoff point was defined using a receiver operating characteristic curve. Standard statistical tests were used to determine the statistical significance of the findings. Results. The LBW prevalence for the study population was 21.41%. Chest circumference had the highest R2 value of 0.83 for the general study population and 0.72 for the LBW infants. The identified cutoff point for chest circumference is ⩽30 cm. Conclusion. Chest circumference is the best alternative to birth weight in identifying LBW babies within the first 48 hours of life in this environment.

Customer experience management in medical tourism (case study: Iranian hospital’s medical tourists)

2021 ◽  
Vol 13 (1) ◽  
pp. 198-226
Author(s):  
Hamed Dabaghi ◽  
Saeid Saieda Ardakani ◽  
Seyed Mohammad Tabataba’i-Nasab
Keyword(s):  
Medical Tourism ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Data Gathering ◽  
Customer Experience ◽  
Research Literature ◽  
Content Type ◽  
Research Areas ◽  
Customer Experience Management ◽  
Data Saturation

Purpose The purpose of this paper is to focus on the emerging phenomenon of medical tourism in the context of Iran from a customer experience management perspective and benchmark of their judgment including positive or negative, of the experience they have achieved of the Iranian health (medical) experience (CE) and suggest scenarios for the improvement of the Iranian customer experience management (CEM). Design/methodology/approach The research methodologies and research methods that are used in this descriptive-analytical research are based on an inspection of the remarkable literature related to medical tourism and customer experience management. The data gathering instrument is a researcher-made questionnaire based on the variables in the conceptual model extracted from the research literature. The study was conducted from May to August 2019. The population cohort of this study was the foreign patients calling selected Iranian hospitals and the sampling method was a purposive and snowball sample of prospective medical tourists. As the study was conducted throughout Iran, some important hospitals in Iran were selected by stratified sampling Yang et al. (2020b). The sample size and data saturation were 500 participants Lv and Song (2019). The collected data using the questionnaire were analyzed by SPSS software and statistical tests. Findings According to the results, the customer experience management statistical significance in the task aspect is (p = 0.0523), in the mechanical aspect is (p = 0.0563), in the human aspect is (p = 0.0544). The study showed positive customer experience among the patients who had been treated in the Iranian hospitals. Originality/value There is a lack of study that focuses on medical tourism and customer experience management in Iran. Therefore, based on the results of this study, the experience of medical tourists in Iran proved to be positive and satisfying. As little research has been conducted in the area of customer experience management (CEM) in Iranian medical tourism, future researchers can use these valuable results precisely and in more detail to benchmark more accurately the customer experience in all areas of medical and health tourism and other research areas in different aspects of CEM in Iran.

Emotional talk about robotic technologies on Reddit: Sentiment analysis of life domains, motives, and temporal themes

2021 ◽  
pp. 146144482110672
Author(s):  
Nina Savela ◽  
David Garcia ◽  
Max Pellert ◽  
Atte Oksanen
Keyword(s):  
Social Sciences ◽  
Social Media ◽  
Text Analysis ◽  
Regression Models ◽  
Life Domains ◽  
Robotic Technology ◽  
Logistic Regression Models ◽  
Negative Comments ◽  
Media Platform ◽  
Robotic Technologies

This study grounded on computational social sciences and social psychology investigated sentiment and life domains, motivational, and temporal themes in social media discussions about robotic technologies. We retrieved text comments from the Reddit social media platform in March 2019 based on the following six robotic technology concepts: robot ( N = 3,433,554), AI ( N = 2,821,614), automation ( N = 879,092), bot ( N = 21,559,939), intelligent agent ( N = 15,119), and software agent ( N = 18,324). The comments were processed using VADER and LIWC text analysis tools and analyzed further with logistic regression models. Compared to the other four concepts, robot and AI were used less often in positive context. Comments addressing themes of leisure, money, and future were associated with positive and home, power, and past with negative comments. The results show how the context and terminology affect the emotionality in robotic technology conversations.

Prediction of Bubbles, Crashes, and Antibubbles

2017 ◽  
Author(s):  
Didier Sornette
Keyword(s):  
Stock Market ◽  
Case Studies ◽  
Power Law ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Large Market ◽  
Simple Power ◽  
Market Crashes ◽  
Successes And Failures

This chapter examines how to predict stock market crashes and other large market events as well as the limitations of forecasting, in particular in terms of the horizon of visibility and expected precision. Several case studies are presented in detail, with a careful count of successes and failures. After providing an overview of the nature of predictions, the chapter explains how to develop and interpret statistical tests of log-periodicity. It then considers the concept of an “antibubble,” using as an example the Japanese collapse from the beginning of 1990 to the present. It also describes the first guidelines for prediction, a hierarchy of prediction schemes that includes the simple power law, and the statistical significance of the forward predictions.

Statistical Tests of Type I Error

2021 ◽  
pp. 90-120
Author(s):  
Charles Auerbach
Keyword(s):  
Control Charts ◽  
Sampling Error ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Data Sets ◽  
Chi Square ◽  
Statistical Process ◽  
Incorrect Decision ◽  
Type 1 Error ◽  
Statistical Process Control Charts

This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of sampling error or chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis, that there is no difference between compared phases, and accepting the alternative one, that true differences exist. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed, and examples are provided to illustrate each. The method for transforming autocorrelated data and merging data sets is discussed further in the context of utilizing transformed data sets to test of Type 1 error.

Evaluating the Statistical Significance of Models Developed by Stepwise Regression

1983 ◽  
Vol 20 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Shelby H. McIntyre ◽  
David B. Montgomery ◽  
V. Srinivasan ◽  
Barton A. Weitz
Keyword(s):  
Monte Carlo ◽  
Null Hypothesis ◽  
Regression Models ◽  
Stepwise Regression ◽  
Statistical Significance ◽  
Selection Procedure ◽  
Coefficient Of Determination ◽  
Forward Selection ◽  
Monte Carlo Simulation Study ◽  
Independent Variables

Information for evaluating the statistical significance of stepwise regression models developed with a forward selection procedure is presented. Cumulative distributions of the adjusted coefficient of determination ([Formula: see text]) under the null hypothesis of no relationship between the dependent variable and m potential independent variables are derived from a Monté Carlo simulation study. The study design included sample sizes of 25, 50, and 100, available independent variables of 10, 20, and 40, and three criteria for including variables in the regression model. The results reveal that the biases involved in testing statistical significance by two well-known rules are very large, thus demonstrating the desirability of using the Monté Carlo cumulative [Formula: see text] distributions developed by the authors. Although the results were derived under the assumption of uncorrelated predictors, the authors show that the results continue to be useful for the correlated predictor case.

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