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Dataset
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Learn to Test for Heteroscedasticity in SPSS With Data From the China Health and Nutrition Survey (2006)

By: The Odum Institute Published: 2015 | Product: SAGE Research Methods Datasets

This dataset is designed for teaching the test for heteroscedasticity following a linear regression analysis. The dataset is a subset of data derived from the 2006 China Health and Nutrition Survey (CHNS) survey of adults. The example estimates a simple regression model of whether systolic blood pressure is a linear function of a person’s age. After performing the regression, it shows how to examine the results for evidence of heteroscedasticity. The dataset file is accompanied by a teaching guide, a student guide, and a how-to guide for SPSS.

Data Type: Survey
Software Guide: SPSS
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You can preview and download the dataset from this tab. The dataset is available in multiple file formats, compatible with most common software packages. You can also view and download the Codebook, which provides information on the structure, contents, and layout of the dataset.

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In this tab you will find guides on using this dataset. The Teaching Guide is designed for faculty who are teaching research methods and statistics, with suggestions on how to use the dataset in lab exercises, in homework assignments and as exam questions. The Student Guide introduces the method for students, and can be used in teaching to provide students with an introductory overview of the method or test. The How-to Guide shows how to perform the technique or test using data analysis software.

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About This Dataset
Data Source Citation

Carolina Population Center. (2006). China Health and Nutrition Survey [Data file]. Available from http://www.cpc.unc.edu/projects/china

Full title of originating dataset

China Health and Nutrition Survey

Data author(s) and affiliations

Carolina Population Center, University of North Carolina at Chapel Hill

National Institute of Nutrition and Food Safety, Chinese Center for Disease Control and Prevention

Dataset source website address

http://www.cpc.unc.edu/projects/china

Data Universe

Adults 18 and older

Funding sources/suppliers
  • National Institutes of Health (R01-HD30880, DK056350, and R01-HD38700)
  • National Institute of Nutrition and Food Safety (INFS)
  • Chinese Center for Disease Control and Prevention (CCDC)
  • Ford Foundation
  • National Science Foundation (INT-9215399)

“This research uses data from China Health and Nutrition Survey (CHNS). We thank the National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention, Carolina Population Center, the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, and R01-HD38700) and the Fogarty International Center, NIH for financial support for the CHNS data collection and analysis files from 1989 to 2006 and both parties plus the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009 and future surveys.”

Sample/sampling procedures

Data collection took place over a 3-day period using a multistage, random cluster process to draw a sample of approximately 4,400 households with a total of 26,000 individuals in nine provinces that vary in geography, economic development, public resources, and health indicators.

Data collection dates

2006

Time frame of analysis

2006

Unit of analysis

Individual

Location covered by data

China

Provinces: Guangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Liaoning, Shandong

Links to SRM content
  • Sweeney, K. (2004). Heteroskedasticity. In M. S. Lewis-Beck, A. Bryman, & T. Futing Liao (Eds.), The SAGE encyclopedia of social science research methods (pp. 459–460). Thousand Oaks, CA: Sage Publications, Inc. doi: http://dx.doi.org/10.4135/9781412950589.n392
  • Kaufman, R.L. (2013). Heteroskedasticity in regression: Detection and correction. Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org/10.4135/9781452270128
List of variables

age

AGE (YEARS)

gender

gender

systolic

SYSTOLIC BLOOD PRESSURE 1

diastolic

DIASTOLIC BLOOD PRESSURE 1

weight

WEIGHT (KG)

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