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Reaching people with disabilities to learn about their experiences of social connection and loneliness
Principal Investigator(s): View help for Principal Investigator(s) Catherine Ipsen, Rural Institute For Inclusive Communities at the University of Montana
Version: View help for Version V1
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Project Citation:
Ipsen, Catherine. Reaching people with disabilities to learn about their experiences of social connection and loneliness. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-08-30. https://doi.org/10.3886/E208782V1
Project Description
Summary:
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Background: People with disabilities
experience higher rates of social isolation and loneliness than people
without disabilities, but there is limited information about how these
conditions are manifested in people with specific types of
disabilities.
Objectives: Using data collected as part of the second administration of the National Survey on Health and Disability (n = 2,132), our objectives were to determine if disability type and recruitment method added explanatory power to observed levels of social connectedness, after controlling for socio-demographic and environmental indicators.
Methods: We used hierarchical regression to evaluate how socio-demographic, disability, environmental, and recruitment type explained four outcome variables for different dimensions of social connectedness, including satisfaction with social activity, quantity of social connections, quality of social connections, and loneliness.
Results: Across all models, employment status (not employed), having mental illness/psychiatric disability, lack of transportation for social needs, and recruitment type (MTurk) significantly predicted lower levels of social connectedness.
Conclusions: The study provides evidence that using alternative methods, such as MTurk, to complement conventional recruitment strategies can improve understanding of social isolation and feelings of loneliness among people with disabilities, particularly those with mental illnesses and/or psychiatric disorders who are less likely to be accessed through group affiliations, but who are at greater risk for experiencing social isolation and feelings of loneliness.
Objectives: Using data collected as part of the second administration of the National Survey on Health and Disability (n = 2,132), our objectives were to determine if disability type and recruitment method added explanatory power to observed levels of social connectedness, after controlling for socio-demographic and environmental indicators.
Methods: We used hierarchical regression to evaluate how socio-demographic, disability, environmental, and recruitment type explained four outcome variables for different dimensions of social connectedness, including satisfaction with social activity, quantity of social connections, quality of social connections, and loneliness.
Results: Across all models, employment status (not employed), having mental illness/psychiatric disability, lack of transportation for social needs, and recruitment type (MTurk) significantly predicted lower levels of social connectedness.
Conclusions: The study provides evidence that using alternative methods, such as MTurk, to complement conventional recruitment strategies can improve understanding of social isolation and feelings of loneliness among people with disabilities, particularly those with mental illnesses and/or psychiatric disorders who are less likely to be accessed through group affiliations, but who are at greater risk for experiencing social isolation and feelings of loneliness.
Funding Sources:
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National Institute on Disability, Independent Living, and Rehabilitation Research (90RTCP0002)
Scope of Project
Subject Terms:
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Disability;
loneliness;
recruitment;
rural;
social connection
Geographic Coverage:
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United States
Time Period(s):
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10/2019 – 1/2020 (Data collection occurred between October 2019 and January 2020, Questions asked about current experiences of experiences within the last year.)
Universe:
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Adults
over 18 years old living in the U.S. with disabilities.
Data Type(s):
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survey data
Collection Notes:
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Information
related to disability type, health status, transportation, housing, community
participation, personal assistance services, public benefits, employment,
insurance coverage, unmet health care needs, income, and demographics.
Methodology
Sampling:
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Sample Size: 2,132 respondents
(1,374 recruited through conventional methods and 758 through Amazon
Mechanical Turk (MTurk)).
Inclusion Criteria: Adults aged 18 and older living in the United States who self-identified as having a physical condition, mental illness, impairment, disability, or chronic health condition affecting daily activities.
Inclusion Criteria: Adults aged 18 and older living in the United States who self-identified as having a physical condition, mental illness, impairment, disability, or chronic health condition affecting daily activities.
Data Source:
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National
Survey on Health and Disability (Wave 2), ACS (American Community Survey),
WG-SS (Washington Group Short Set)
Collection Mode(s):
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mail questionnaire;
web-based survey
Scales:
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Social Connectedness:
- Satisfaction with social activity (scale from 0 to 4).
- Quantity of social connections (0 to 9).
- Quality of social connections (0 to 5).
- Loneliness (measured using the three-item UCLA Loneliness Scale).
Disability Measurement: Self-identified disability type and responses to disability screening questions.
Socio-demographic Characteristics: Various categorical scales (age, gender, race/ethnicity, etc.).
- Satisfaction with social activity (scale from 0 to 4).
- Quantity of social connections (0 to 9).
- Quality of social connections (0 to 5).
- Loneliness (measured using the three-item UCLA Loneliness Scale).
Disability Measurement: Self-identified disability type and responses to disability screening questions.
Socio-demographic Characteristics: Various categorical scales (age, gender, race/ethnicity, etc.).
Unit(s) of Observation:
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Individual respondents (adults aged 18 and older with disabilities).
Geographic Unit:
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United States (Based on county of residence matched to Federal Information Processing Standards (FIPS) codes, categorized using the Office of Management and Budget's (OMB) rural-urban classification scheme (metropolitan, micropolitan, or non-core/rural).)
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