18054 research outputs found
Sort by
Special Cases in Estimating Multiple Missing Values in Linear Models
Get PDFMissing a single observation, or more, very commonly occurs in observational and designed studies. Estimating a single missing observation and analysis of these types of data is found in literature (Montgomery, 2020). But the estimation and analysis of data becomes more complicated when the study dataset becomes imbalanced due to multiple missing. Though the case of two missing values is the simplest case of multiple missing values, the analytic estimation and analysis will not be as straight forward as in the one missing value case, because two missing can occur in various ways. This thesis will be exploring mainly the idea of multiple missing values in two-way classified data. In Azadeh et al (2008) it is stated that missing values are incredibly common. In order to continue working with the dataset, those missing values must be estimated. There are two different types of missing values: missing at random (MAR) and missing not at random (MNAR) (Efromovich, 2018). This thesis will be focusing specifically on values that are MAR. Values that are MAR are relatively convenient to deal with because each value in the data set has the same probability of being missing. Also, in Gomer and Ke-Hai (2021) it is stated that the cause of missingness being unobserved makes MNAR valuables difficult to deal with, but it is found in literature (Efromovich, 2018). With a MAR in datasets, the missing value can be in any treatment and in any block. With two missing values, each missing value can appear in any treatment and any block which gives three separate cases that will be discussed later. This thesis will also be looking at multiple missing values instead of just one. Estimating for one missing value has been researched extensively, but estimating more than one still has plenty of room for exploration. In Tang \& Ishwaran (2017) and Montgomery (2020), the authors mentioned that their method for estimating missing values is iterative because the estimation of one missing value takes into account the value of the other missing value. This means for the first estimation one of the missing values is given a random number and that is used to compute the second missing value. That second missing value is then plugged in to find the estimation for the first missing value. This process is done over and over until the estimates have stabilized. The method proposed is an analytic method which will provide closed form solution for missing values and will not require the iterative process. Having the analytic solution will allow us to explore the inferential, statistical properties of the estimators
Aphasia-friendly reading comprehension training to enhance nutritional literacy
Get PDFThis study aimed to establish the impact of exposure to controlled, aphasia-friendly articles about dietary mindfulness on the nutritional literacy of people with aphasia. The student researcher evaluated five participants from the Butler Aphasia Community to determine their level of reading impairment, the severity of their aphasia, and baseline nutritional knowledge. Participants attended two pretest sessions, two reading sessions, and one post-test session. They were exposed to eight nutritional articles, four in an aphasia-friendly format and four in a traditional format, and evaluated on reading speed and comprehension. During their final session, the student researcher re-administered the assessment of nutritional literacy and interviewed the participants about their preferences regarding the two article formats: aphasia-friendly and traditional. Participants demonstrated statistically significant improvement in nutritional literacy but did not statistically significantly improve reading speed or comprehension. However, this study provides insight into the potential of bolstering reading enjoyment with aphasia-friendly textual modifications as participants reported more positive reading experiences when given aphasia-friendly adaptations
Profiles in Volunteerism: A Study on Contributing Factors to Time Spent in Service
Get PDFThe objective of this thesis is to identify a profile of volunteerism in the United States. Time spent in service of others serves as the dependent variable while explanatory variables include years of education, annual family income, sex, age, household size, racial identity, marital status, region, disability, veteran status, location in a metropolitan area, day of the week, employment status, and if the household has children present. By modeling volunteerism through an econometric lens, this thesis will analyze the concept of time spent in service and how it relates to the Midwestern identity among other attributes. Using data from the United States Bureau of Labor Statistics, the study will provide estimates on if residing in the Midwest impacts the hours of volunteerism or time spent in service while examining how this impact differs by education, income level, age, and gender