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Similar to IAOS 2018 - Teaching basic statistics using integrated global census and survey (IPUMS) data, L. Cleveland, K. Jeffers, M. King
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IAOS 2018 - Teaching basic statistics using integrated global census and survey (IPUMS) data, L. Cleveland, K. Jeffers, M. King
- 1. Teaching Basic Statistics Using Integrated Global Census and Survey (IPUMS) Data Lara Cleveland, Kristen Jeffers, Miriam King IPUMS University of Minnesota
- 3. 89 countries – 345 censuses – 988 million person records IPUMS-International microdata availability
- 4. • Individual-level microdata samples • Anonymized • Harmonized • Data extract system • Online data analysis • Free
- 5. Relation to head Marital status Literacy Occupation Population Microdata
- 6. • Access • Data are messy • Documentation may not be available • Universe of respondents often unclear • Changes in wording (and meaning) of questions • Variables coded differently • year to year • country to country Challenges of real-world data
- 7. Importance of data context Children ever born 0 1,082,616 1 38,609 2 35,372 3 32,257 4 29,260 5 24,474 6 21,558 7 16,020 8 12,988 9 9,728 10+ 19,097
- 8. Importance of data context Who was asked this question or included in this dataset? Does the subject population change over time or vary across samples? How can we deal with such inconsistencies? What was the wording of the question that people responded to? Did this change over time or vary across samples? Are these changes or differences likely to influence results? What proportion of people refused to answer or didn't know the answer to a question? What are the options for dealing with these missing data? What are we assuming when we just exclude the missing cases?
- 10. IPUMS variable harmonization Marital Status: MARST Bangladesh 2011 1 = Unmarried 2 = Married 3 = Widowed 4 = Divorced/separated Mexico 1970 1 = Married, civil & relig 2 = Married, civil 3 = Married, religious 4 = Consensual union 5 = Widowed 6 = Divorced 7 = Separated 8 = Single Kenya 1999 1 = Never married 2 = Monogamous 3 = Polygamous 4 = Widowed 5 = Divorced 6 = Separated
- 11. Input Bangladesh 2011 4 = Divrc or separated 1 = Unmarried 2 = Married 3 = Widowed Mexico 1970 1 = Married, civil & relig 2 = Married, civil 3 = Married, religious 4 = Consensual union 5 = Widowed 6 = Divorced 7 = Separated 8 = Single Kenya 1999 1 = Never married 2 = Monogamous 3 = Polygamous 4 = Widowed 5 = Divorced 6 = Separated
- 12. LabelCode Harmonized 1 = Never married1 = Married, civil & relig 4 = Divrc or separated 1 = Unmarried 2 = Married 3 = Widowed 2 = Married, civil 3 = Married, religious 4 = Consensual union 5 = Widowed 6 = Divorced 7 = Separated 8 = Single Single Married or in union Married, formally Civil Religious Civil and religious Monogamous Polygamous Consensual union Separated Divorced 2 = Monogamous 3 = Polygamous 4 = Widowed 5 = Divorced 6 = Separated 1 0 0 2 0 0 2 1 0 2 1 1 2 1 2 2 1 3 2 1 4 2 1 5 2 2 0 0 0 3 1 0 3 2 0 0 0 Mexico 1970 Input Bangladesh 2011 Kenya 1999 Divorced or separated3 Widowed4
- 14. Text of Census Questionnaire (Mexico 2000) 5. Number of Rooms How many rooms are used for sleeping without counting hallways? _____ Write the number Without counting the hallways or bathrooms how many total rooms are in this dwelling? Count the kitchen _____Write the number 6. Access to water Read all of the options until you get an affirmative answer. Circle only one answer 1 Running water inside the dwelling 2 Running water outside the dwelling but on the land 3 Running water from a public faucet or hydrant 4 Running water that is carried from another dwelling 5 Tanked in by truck 6 Water from a well, river, lake, stream or other Answers 3, 4, 5, 6 continue with number 8 7. Water supply How many days of the week is water available? Circle only one answer 1 Daily 2 Every third day 3 Twice a week 4 Once a week 5 Occasionally
- 22. Home Ownership Relation to Head Age Marital Status Occupation Customized data extract
- 23. 3. Submit extract Pooled data extract sample water sex education Argentina 2001 3.6 million Chile 2002 1.5 million Cuba 2002 1.1 million Extract Engine Argentina 2001 Chile 2002 Cuba 2002 Water supply Sex Education 1. Select samples 2. Select variables 1 dataset 3 censuses 4 variables 6.2 million records Harmonized codes
- 25. Advantages of IPUMS data • Data access • Documentation • Train students to deal with messy, missing, inconsistent data • Customizable, modifiable • Real-life relevance
- 26. IPUMS for Teaching Statistics Specific needs of statistics educators • Data analysis in Excel; R • Specific types of data suited to teaching particular techniques (e.g., continuous variables for OLS regression analysis) • Examples of how to use real-world population data to teach basic statistics concepts and techniques
- 27. IPUMS for Teaching Statistics
- 28. IPUMS-based exercises for teaching basic statistics 9 exercises follow topics covered in 3rd edition of Statistics: The Art and Science of Learning From Data by Alan Agresti and Christine Franklin
- 29. IPUMS-based exercises for teaching basic statistics Topics covered: • Exploring Data (frequency distributions; graphs) • Probability • Probability Distributions • Confidence Intervals • Hypothesis Testing • Comparing Two Groups • Association between Categorical Variables • Regression Analysis • Multiple Regression
Editor's Notes
- Describe the IPUMS database Discuss the advantages of using real-world data from IPUMS in the classroom Talk about exercises we have developed to support teaching basic statistics with IPUMS data
- IPUMS is a collection of 9 data projects housed at the University of Minnesota that provide census and survey data from around the world. The projects are funded by federal research grants from the NSF and NIH and all the data are available for free online. The two projects most relevant for international audiences are…IPUMS-International—which provide harmonized census microdata from nearly 100 countries around the world, and IPUMS Global Health, which provides health survey data for Africa and Asia from the demographic and health surveys and a relatively new survey called PMA 2020.
- Map indicates the data available from IPUMS-International. IPUMS global health covers a subset of African and Asian countries.
- Number of common features shared across all IPUMS projects
- To be clear, IPUMS disseminates population microdata where each case represents an individual, and each column or group of columns represents that individual’s response to a census or survey questions. Most IPUMS data is organized into households, so individuals can be analyzed in the context of their families and households.
- Value of using real-world data in the classroom in universally acknowledged, but there are significant challenges associated with doing so. Access—identifying and accessing appropriate datasets can be difficult and time-consuming The data are often messy and students are rarely equipped to deal with messy data on their own Documentation about data collection and data treatment may be unavailable or insufficient From one census to the next or from one survey to the next, there are often changes in data collection practices or question wording that make it difficult to make accurate comparisons across time and place Similarly, data producers are often inconsistent in the coding schema they apply to the data presenting another challenge for comparative analysis
- Despite these challenges, it’s extremely important to expose students to the complexity of real-world data and to train them to think critically about the meaning of underlying data. Take this example from the 2010 census of Zambia. Here is a frequency distribution for the children-ever-born variable as we received it from the Central Statistical Office. We can train students to calculate summary statistics for this variable, but unless they consider the real meaning of these data, their results will be inaccurate. And why is that, you may wonder. If we consults the documentation that provides context to these data—in this case the census questionnaire—we learn that only women age 12 and older were asked this question. But the frequency distribution adds up to the total number of cases in the dataset—so where are the males and young females? Who was asked this question or included in this dataset? Does the subject population change over time or vary across samples? How can we deal with such inconsistencies? What was the wording of the question that people responded to? Did this change over time or vary across samples? Are these changes or differences likely to influence results? What proportion of people refused to answer or didn't know the answer to a question? What are the options for dealing with these missing data? What are we assuming when we just exclude the missing cases?
- So despites its challenges, we contend that it is better to train students with real-world, imperfect data, to prepare them to answer such questions as: Who was asked this question or included in this dataset? Does the subject population change over time or vary across samples? How can we deal with such inconsistencies? What was the wording of the question that people responded to? Did this change over time or vary across samples? Are these changes or differences likely to influence results? What proportion of people refused to answer or didn't know the answer to a question? What are the options for dealing with these missing data? What are we assuming when we just exclude the missing cases?
- Data we receive from data producers to make data more user-friendly
- Zambia 2010 Equally as important for students to now how to calculate and interpret summary statistics as understand the meaning of the underlying data-- If you don’t understand the way the data were collected and treated, what they are measuring, you will always risk inaccurate results/conclusions. Working with real world data exposes teaches students to consider these things as part of responsible statistical analysis. Who was asked this question or included in this dataset? Does the subject population change over time or vary across samples? How can we deal with such inconsistencies? What was the wording of the question that people responded to? Did this change over time or vary across samples? Are these changes or differences likely to influence results? What proportion of people refused to answer or didn't know the answer to a question? What are the options for dealing with these missing data? What are we assuming when we just exclude the missing cases?
- Majority of IPUMS data users are demographer, economists, sociologists using data for substantive research. During the last several years, IPUMS staff have engaged with statistics educators as a new audience for these free online data from around the world. Beginning with an IPUMS workshop at the ICOTS 9 conference, and continuing with participation in meetings of the International Association for Statistics Education, Discussions with statistics educators were eye-opening for the IPUMS staff members, who are primarily trained in the social sciences. We learned the following: While social and health scientists use statistical packages such as Stata, SAS, and SPSS, statistics educators tend to use Excel and the open-source R programming language for analysis; While social and health scientists are interested in particular substantive topics, statistics educators need specific types of data suited to teaching particular techniques (e.g., continuous variables for OLS regression analysis); Statistics educators welcome concrete examples of how to use real-world data to teach basic statistics concepts and techniques.
- Teachers of statistics classes are ideally preparing their students to use statistical methods after they finish with the class, in jobs and in further studies requiring analysis of real data and "big data." Our conversations with both students and teachers of statistics suggest that teaching materials that accompany statistics textbooks are all too often unlike real-world data. For example, the number of cases may be very small, and there may be no missing or inconsistent data. In real-world data, from censuses, surveys, corporate or government records, or other sources, the number of cases may be very large, and missing and inconsistent data are a given.
- In addition to the enhancements IPUMS makes to the data that make them easier to use for all data users, we have also begun to develop materials to support teaching basic statistics with IPUMs data. Beginning with an IPUMS workshop at the ICOTS 9 conference, and continuing with participation in meetings of the International Association for Statistics Education, While social and health scientists use statistical packages such as Stata, SAS, and SPSS, statistics educators tend to use Excel and the open-source R programming language for analysis; That statistics educators need help identifying among the thousands of variables available from IPUMS which are suited to teaching particular techniques (e.g., continuous variables for OLS regression analysis); Statistics educators welcome concrete examples of how to use real-world data to teach basic statistics concepts and techniques.
- In addition to building R package that reads ipums microdata and metadata Available on CRAN
- Feedback from ICOTS 9, IASE satellite meetings Make use of the unique quality, complexity and breadth of the IPUMS-International and IPUMS-DHS datasets for teaching purposes Extend the reach of the IPUMS datasets to a broader audience of both statistics teachers and students from around the world Expose teachers and students from the field of statistics to the complexity and messiness of real-life data In response to these special needs of statistics educators, IPUMS hired two Fellows to create exercises for teaching basic statistics using IPUMS-International and IPUMS-DHS data. Erez Garnai, a doctoral student in Sociology with experience teaching statistics to undergraduate Sociology majors, and Stephanie Chen, an undergraduate majoring in Social Statistics, followed the topics covered in a basic statistics textbook to create a series of 9 exercises based on IPUMS-International census data and 9 exercises based on IPUMS-DHS health survey data. Each exercise noted the statistics topics covered, specified the variables used, recommended country- and year-specific samples to use, supplied R programming code, and included answers and interpretive questions to test students' understanding of the topic. Specifically, these exercises covered:
- Feedback from ICOTS 9, IASE satellite meetings Make use of the unique quality, complexity and breadth of the IPUMS-International and IPUMS-DHS datasets for teaching purposes Extend the reach of the IPUMS datasets to a broader audience of both statistics teachers and students from around the world Expose teachers and students from the field of statistics to the complexity and messiness of real-life data
- Notes the statistics topics covered Specifies the variables used Recommends country- and year-specific samples to use Supplies R programming code Includes answers and interpretive questions to test students' understanding of the topic Such exercises can be modified to fit the needs of teachers or serve as examples that spur the instructor's own creativity in creating new material using census and survey data from nearly 100 countries.
- Understand implications of dropping missing values
- Rest of exercises available soon Feedback, input