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Bryan Keller; Zach Branson – Asia Pacific Education Review, 2024
Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for…
Descriptors: Causal Models, Educational Research, Regression (Statistics), Probability
Rahman, Nazia – ProQuest LLC, 2013
Samejima hypothesized that non-monotonically increasing item response functions (IRFs) of ability might occur for multiple-choice items (referred to here as "Samejima items") if low ability test takers with some, though incomplete, knowledge or skill are drawn to a particularly attractive distractor, while very low ability test takers…
Descriptors: Multiple Choice Tests, Test Items, Item Response Theory, Probability
Stephens, Caroline E.; Newcomer, Robert; Blegen, Mary; Miller, Bruce; Harrington, Charlene – Gerontologist, 2012
Purpose: To examine the 1-year prevalence and risk of emergency department (ED) use and ambulatory care-sensitive (ACS) ED use by nursing home (NH) residents with different levels of severity of cognitive impairment (CI). Design and Methods: We used multinomial logistic regression to estimate the effect of CI severity on the odds of any ED visit…
Descriptors: Dementia, Hospitals, Nursing Homes, Health Facilities
Gruneir, Andrea; Miller, Susan C.; Intrator, Orna; Mor, Vincent – Gerontologist, 2007
Purpose: The purpose of this study was to quantify the effect of specific nursing home features and state Medicaid policies on the risk of hospitalization among cognitively impaired nursing home residents. Design and Methods: We used multilevel logistic regression to estimate the odds of hospitalization among long-stay (greater than 90 days)…
Descriptors: Dementia, Probability, Identification, Nursing Homes

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