This archive provides information describing the content, methodology and implementation of census and survey conducted by the NSO of Mongolia, as well as descriptions of micro data files with the variable documentation.

Home › MULTIPLE INDICATOR CLUSTER SURVEY › MICS 2016 (NALAIKH DISTRICT) › Data Dictionary › F2 › V105

Mongolia - MICS 2016 (Nalaikh District)

Reference ID	DDI-MNG-NSO-EN-MICS-2016-NA-v1.0
Year	2016
Country	Mongolia
Producer(s)	National Statistical Office - SGH
Sponsor(s)	United Nations Children's Fund - UNICEF - Funding of survey implementation United Nations Population Fund - UNFPA - Funding of survey implementation
Collection(s)	Multiple Indicator Cluster Survey
Metadata	Download DDI Download RDF

Created on	Dec 18, 2017
Last modified	Dec 18, 2017
Page views	7082661
Downloads	12790487

• Study Description
• Data Dictionary
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• Data Dictionary
  • bh
  • ch
  • hh
  • hl
  • mn
  • vc
  • wm
• Variable Groups
  • Group Interview of Household identification
  • Group Water and Sanitation
  • Group Household characteristics
  • Group Education
  • Group Child Labour
  • Group Child Displine
  • Group Hand Washing
  • Group Information Panel
  • Group Background
  • Group Access to mass media and use of information communication technology
  • Group Fertility
  • Group Birth History
  • Group Desire for Last birth
  • Group Material and New Born Health
  • Group Post-Natal Health Checks
  • Group Attitudes Towards Domestic Violence
  • Group Marriage/Union
  • Group Sexual Behaviour
  • Group Illness Symptoms
  • Group Contraception
  • Group HIV/ AIDS
  • Group Unmet Need
  • Group Tobacco and Alcohol Use
  • Group Life Satisfaction
  • Group Age
  • Group Birth Registration
  • Group Early Childhood Development
  • Group Breastfeeding
  • Group Immunization
  • Group Care of Illness
  • Group Child FUNCTIONING
  • Group Anthropometry

CAN (name) RECOGNIZE SIMPLE SHAPES SUCH AS TRIANGLES, RECTANGLES AND CIRCLES? (EC7M)
File: ch

Overview

Type: Discrete Format: numeric Width: 1 Decimals: 0 Range: 1-9

Valid cases: 155 Invalid: 224

Categories

Value	Category	Cases
1	Yes	104	67.1%
2	No	47	30.3%
8	DK	4	2.6%
9	Missing	0	0.0%
Sysmiss		224

Warning: these figures indicate the number of cases found in the data file. They cannot be interpreted as summary statistics of the population of interest.