Use It for Good – News and information from IPUMS

Studying Americans’ Social Lives Using IPUMS ATUS Data

November 11, 2026 by mpcblog

By R. Gordon Rinderknecht and Isabel Pastoor

Our sense of social connection is related to health and well-being in important ways, and Americans spend an increasing amount of time alone (Kannan & Veazie, 2023). In 2023, U.S. surgeon general Vivek Murthy declared loneliness to be an epidemic, sparking further media attention and scholarly research on the subject. Nationally representative data on how Americans spend their time is key to understanding the loneliness epidemic, sociability, and people’s social lives more broadly. In this blog post, we discuss the use of American Time Use Survey (ATUS) data from IPUMS to study these topics, including some important limitations for data users. This information was originally presented by R. Gordon Rinderknecht in a webinar supported by IPUMS, the Minnesota Population Center, the Life Course Center, and the Maryland Population Research Center.

IPUMS ATUS provides data from the American Time Use Survey, a nationally representative time diary survey of Americans, from 2003 to present. ATUS data are particularly well-suited for studying social interaction. ATUS respondents report, through an interview, information about their activities over a 24-hour period, including the types of activities they did, who they were with, and where they were. ATUS respondents are a subset of outgoing Current Population Survey (CPS) respondents, and data users can link these two data sources to access rich social, demographic, and economic information from the CPS to complement the time use data. Information on other household members is also available, both directly in the ATUS and from the CPS.

IPUMS Weights

November 2, 2026 by mpcblog

By Sarah Flood and Isabel Pastoor

Which weight should I use for my analysis?

One of the most common questions IPUMS users ask us is “which weight should I use for my analysis?” The real answer is “it depends.” Unfortunately, there is no hard and fast rule.

There is a lot of variability in the number of weights across IPUMS US Microdata collections. In some cases, there aren’t too many options. For example, in modern IPUMS USA data there are two weights – HHWT and PERWT – to choose from and which one you use depends on whether you’re doing a household-level analysis or a person-level analysis. In other cases, there are many more weights to choose from and recommending a weight requires additional information about what variables and datasets researchers are using to answer their questions. Consider the complexities of other US Microdata collections:

IPUMS CPS: The Current Population Survey (CPS) basic monthly survey has both a household, person, and earning weight. In addition, nearly every supplement fielded alongside the CPS has its own weight. Analyses that include variables collected as part of supplements should use the supplement weight because every CPS participant does not respond to every supplement.
IPUMS ATUS: In addition to the standard annual weight, every American Time Use Survey (ATUS) module has its own weight. Like the CPS, not every ATUS respondent participates in every module, which is why the module weight should be used.
IPUMS NHIS: In the National Health Interview Survey (NHIS), the appropriate weight depends on the specific NHIS source files of the variables you are analyzing and your focal population(e.g., adults or children). A major redesign in 2019 changed the appropriate weight for otherwise comparable variables, and many years include additional supplement weights.
IPUMS MEPS: Only person-level weights are available since the Medical Expenditure Panel Survey (MEPS) includes no household-level variables. A number of module- or questionnaire-specific weights are also available.

Measures of Depression and Anxiety in Adults, in IPUMS (U.S.) National Health Interview Survey and IPUMS Global Health

October 16, 2026 by mpcblog

By Miriam L. King, Anna Bolgrien, & Julia Rivera Drew

Mental health conditions, such as depression and generalized anxiety disorder, are an important aspect of population health around the world. Including measures of mental health conditions in population health surveys is important for elucidating overall wellbeing. For example, a 2025 IPUMS Best Student Paper winner used data from the IPUMS-harmonized National Health Interview Survey (NHIS) and Medical Expenditure Panel Survey (MEPS) to document increasingly poor self-rated health among young adults in the U.S., largely due to an increase in the prevalence of poor mental health.¹ While mental health measures have long been included in U.S. health surveys such as the NHIS, the Demographic and Health Surveys (DHS) and the UNICEF Multiple-Indicator Cluster Surveys (MICS) have recently incorporated measures of mental health comparable to those found in the NHIS.

This blog post will identify new opportunities for the comparative study of mental health by highlighting some of the widely available measures of depression and anxiety in the U.S. NHIS and in international population health data sources available from IPUMS. Which mental health measures to use will depend on the research question and the population of interest. Diagnosis-focused measures ask whether a person was (ever) told by a health professional that they have a certain mental health condition. Symptom-focused approaches ask about recent symptoms associated with mental health conditions, often using validated scales. Symptom-focused queries give a more accurate view of the prevalence of mental health problems, particularly in settings where medical and mental health resources are sparse, so few people are diagnosed.

Introducing: Variable-Based Sample Selection for IPUMS International

October 1, 2026 by mpcblog

By Derek Burk

When you’re building an IPUMS International extract, you almost always have a central topic or question in mind: How is children’s educational attainment influenced by their parents’ education levels? How have migration patterns in Latin America changed in the past few decades? How do age and disability status interact to influence employment?

In other words, you almost always know the main variables you want in your extract. To study education, you’ll need EDATTAIN. To study lifetime international migration, you’ll need BPLCOUNTRY. To study disability and employment, you’ll need DISABLED and EMPSTAT.

Wouldn’t it be great if there was a magic wand that could add all the samples with your variable(s) of interest to your extract with just a flick of the wrist?

If you’re nodding your head in agreement right now, we think you’ll love our new feature: variable based sample selection! It doesn’t involve a wand, but it is a magical way to select samples based on the variables you need.

Enabling variable based sample selection

Variable based sample selection is not enabled by default, so to use it, you’ll first need to turn it on. From the top level Select Variables page on the IPUMS International website, click on the Display Options button:

Screenshot of the Select Variables page on the IPUMS International website, with a red box drawn around the Display Options button.

This will call up the Display Options menu. The variable based sample selection feature is listed at the very top of the menu. To turn the feature on, click the radio button next to “Enabled”, then click the Apply Selections button at the bottom.

Continuity and Change in the 2025 NHIS Data

September 17, 2026September 15, 2026 by mpcblog

By Julia A. Rivera Drew

Collected annually since 1957, the National Health Interview Survey (NHIS) is the longest running nationally-representative health survey in the world. The NHIS questionnaire and data collection approaches have changed considerably over time, both to better capture emerging public health topics as well as to adapt the study design, questionnaire, and data collection operations to maintain a high-quality data product. The objective of this blog post is understanding the factors that contribute to year-to-year change in variables available from the NHIS with a particular focus on the 2025 NHIS data.

How is the NHIS structured?

Since 2019, the NHIS questionnaire has been a combination of (1) NHIS “core” content, collected every year; (2) NHIS “rotating core” content, collected on some predetermined occasional schedule; (3) NHIS “emerging public health” topical content, which is generally temporary and may be collected for a few consecutive years, collected occasionally, or collected once and then not again; and (4) NHIS “sponsored” content, with funding provided by other federal agencies. Refer to our user note on changes to the NHIS questionnaire implemented with the 2019 redesign for more information.

Comparing Census Bureau and IPUMS USA Inflation Adjustments to American Community Survey Income Variables

August 18, 2026 by mpcblog

By Kari Williams & Isabel Pastoor

Researchers working with variables that report income must account for the changing value of a dollar across time. IPUMS USA, and other IPUMS data collections, include resources that streamline the process of adjusting monetary variables into constant dollars to facilitate comparison. These tools are very useful for researchers who are initiating research projects and exclusively using IPUMS data; however, these tools can make it difficult to directly compare, for example, IPUMS and the original U.S. Census Bureau versions of public use microdata sample (PUMS) data. This blog post provides an overview of how the Census Bureau reports income values in the American Community Survey (ACS) PUMS data, the adjustment factors provided by the Census Bureau, and how those map onto variables and ACS microdata extracts from IPUMS USA.

Want to skip the gory details? The key points are summarized in the next paragraph and in Table 1.

TL;DR

The Census Bureau does not automatically adjust for inflation in the PUMS files. They provide an adjustment factor, available as the variable ADJINC in the original Census Bureau PUMS files, that users can apply manually. This adjustment factor is intended to account for the differential reference period of respondents based on the rolling sample design of the ACS and, in multi-year files, to account for inflation across the five-year (or three-year) period. In contrast, IPUMS USA does automatically adjust for inflation across multi-year periods. However, IPUMS does not automatically apply or recommend using the within-year reference period adjustment because it does not adequately account for the differences in reference periods between respondents within a single year of the ACS. Users who wish to apply this adjustment can use the IPUMS USA variable ADJUST, which provides the within-year adjustment factor only (i.e., it does not include the multi-year inflation adjustment as that is automatically applied by IPUMS to multi-year ACS PUMS samples).

Table 1: Comparing U.S. Census Bureau and IPUMS USA Adjustment of Income Variables, 1-Year and 5-Year ACS PUMS

	Census Bureau	IPUMS
Inflation Across Years	1-Year: N/A	1-Year: N/A
Inflation Across Years	5-Year: Apply manually, included in ADJINC variable	5-Year: Applied automatically
Within-Year Reference Period Adjustment	1-Year: Apply manually, entirety of ADJINC variable	1-Year: Not recommended; apply manually using ADJUST variable
Within-Year Reference Period Adjustment	5-Year: Apply manually, included in ADJINC variable	5-Year: Not recommended; apply manually using ADJUST variable

IPUMS Time Use Leadership Receives Ellen Galinsky Generative Researcher Award

July 17, 2026July 16, 2026 by mpcblog

By Kari Williams & Stacy Nordstrom

Drs. Sarah Flood, Liana Sayer, and Melissa Milkie presented with the Ellen Galinsky Generative Researcher Award
Since 1993, IPUMS has worked to preserve and harmonize population data and make them freely accessible to researchers. This includes time diary data from the American Time Use Survey (ATUS) as well as international and historical time diary data. Last month, Drs. Sarah Flood and Liana Sayer, co-PIs of IPUMS Time Use, along with their long-time collaborator Dr. Melissa Milkie, received the Ellen Galinsky Generative Researcher Award, presented by the Work and Family Researchers Network.

The award recognizes work-family researchers who have contributed breakthrough thinking to the work-family field via theory, measures, and/or data sets that led to expansive application, innovation, and diffusion, including the sharing of research opportunities in the spirit of open science. The award committee highlighted Flood’s foundational contributions to public data infrastructure and long history of demystifying complex time diary data, as well as Sayer’s innovative scholarship on gender and social class inequalities in time use over the life course.