Nonresponse is a growing problem in surveys of college students and the general population. At present, we have a limited understanding of survey nonresponse in college student populations and therefore the extent to which survey results may be biased. The purpose of this dissertation is to explore three facets of nonresponse in surveys of college students in order to strengthen our empirical and conceptual understanding of this phenomenon. This dissertation seeks to contribute to our understanding of who participates in surveys and who does not, how students experience the process of being asked to complete surveys, and whether or not students' perspectives about surveys suggest that college student surveys should be conceptualized as organizational surveys. To begin to answer these questions, I conducted three studies -- a secondary data analysis that examines student characteristics associated with the odds of completing a survey, a "survey on surveys" study that asks students about their experiences with surveys, and a series of focus groups to understand how students made sense of surveys at their institutions. Taken together, these findings provide a basis for a more developed and nuanced understanding of nonresponse in student surveys.

The diploma thesis focuses on the youngest age cathegory of respondents in public opinion polls. The main goal is to examine character and quality of information about adolescent's attitudes and opinions obtained in public opinion polls that are held by The Public Opinion Research Centre. To achieve the main goal nonattitude is examined. The thesis will be divided into theoretical and practical part. Theoretical part stands on the basis of public opinion sociology and developmental psychology and the issue of attitude measurement is introduced along with adolescents developmental theory and characteristics. Practical part reflects the information summoned in theoretical part and test them on data collected by The Public Opinion Research Centre which were obtained in continuous research within project Our society. Analysis focuses on examination of nonresponse, don't know answers and neutral attitudes. Results are compared among all age groups.

調查研究被廣泛的使用於現今社會，而研究中出現頻繁的「無反應」問題卻常為研究者帶來統計分析過程中的困擾，基於預防重於治療的前提下，「無反應」因素的探究有其必要性。本研究的目的即在現有的理論基礎上，針對「敏感性」問題於「無反應」（收入及投票意向問題）中的情況，應用卡方檢定與logit模型驗証其於本研究的適切性，並進一步建立新的「無反應」問題解釋模型，以幫助我們瞭解此種「無反應」問題在現有台灣調查研究中可能存在的因素，進而減低對研究結果的影響。 影響「無反應」因素的驗證部份，大致上與過去相關的研究結果相同，只有少數的變項有所差異。另外，在模型配適的解釋部份，收入的研究結果中會影響收入「無反應」的因素有：受訪者年齡、受訪者職業、訪員性別、訪員經驗以及調查問題型式。投票意向的研究結果顯示，影響投票意向「無反應」的因素有：受訪者的性別、省籍、政治參與度、對統獨立場的看法、對族群認同的看法、受訪者的性別與對統獨立場看法的交互作用以及受訪者的省籍對族群認同看法的交互作用。本研究的研究結果主要適用於敏感性問題中影響收入與投票意向問題之「無反應」因素情況。 / Investigative process is widely used in our society today. However, statistical analysts are often bothered by the frequent occurrence of the nonresponse problem. Hence, it is important to investigate the cause of the nonresponse situation. The purpose of this research is to create a new model to explain the nonresponse problem in certain sensitive situations. There are two types of questions of sensitive nature; namely, income level and voting inclination, that lead to the nonresponse. We have explored two methodologies, Chi-square test and Logit model, for their applicability to the study. The study will help us to understand what are the factors influencing the nonresponse for investigative process conducted in Taiwan. These findings may be used to reduce the impact of the nonresponse in regards to how we can interpret the results. In terms of verifying influential factors of the nonresponse, there are only a few different outcomes in comparison with previous research results. To find factors in regard to income level, the following five elements are identified: the age of the interviewee, the occupation of the interviewee, the gender of the interviewer, the experience of the interviewer, and styles of the questionnaire. As for the voting inclination, we found the following factors: interviewee's gender, birth origin, degree of political participation, viewpoint toward the issue of unification/independence, and opinion of ethnic identity; interaction between interviewee's gender and his/her viewpoint toward the issue of unification/independence, and interaction between interviewee's birth origin and opinion of ethnic identity. The main application of this study is to identify the various factors causing nonresponse phenomenon in reaction to sensitive questions of income level and voting inclination during an investigative process.

<p>Longitudinal twin data provide important information for exploring sources of variation in human traits. In statistical models for twin data, unobserved genetic and environmental factors influencing the trait are represented by latent variables. In this way, trait variation can be decomposed into genetic and environmental components. With repeated measurements on twins, latent variables can be used to describe individual trajectories, and the genetic and environmental variance components are assessed as functions of age. This thesis contributes to statistical methodology for analysing longitudinal twin data by (i) exploring the use of random change point models for modelling variance as a function of age, (ii) assessing how nonresponse in twin studies may affect estimates of genetic and environmental influences, and (iii) providing a method for hypothesis testing of genetic and environmental variance components. The random change point model, in contrast to linear and quadratic random effects models, is shown to be very flexible in capturing variability as a function of age. Approximate maximum likelihood inference through first-order linearization of the random change point model is contrasted with Bayesian inference based on Markov chain Monte Carlo simulation. In a set of simulations based on a twin model for informative nonresponse, it is demonstrated how the effect of nonresponse on estimates of genetic and environmental variance components depends on the underlying nonresponse mechanism. This thesis also reveals that the standard procedure for testing variance components is inadequate, since the null hypothesis places the variance components on the boundary of the parameter space. The asymptotic distribution of the likelihood ratio statistic for testing variance components in classical twin models is derived, resulting in a mixture of chi-square distributions. Statistical methodology is illustrated with applications to empirical data on cognitive function from a longitudinal twin study of aging. </p>

Longitudinal twin data provide important information for exploring sources of variation in human traits. In statistical models for twin data, unobserved genetic and environmental factors influencing the trait are represented by latent variables. In this way, trait variation can be decomposed into genetic and environmental components. With repeated measurements on twins, latent variables can be used to describe individual trajectories, and the genetic and environmental variance components are assessed as functions of age. This thesis contributes to statistical methodology for analysing longitudinal twin data by (i) exploring the use of random change point models for modelling variance as a function of age, (ii) assessing how nonresponse in twin studies may affect estimates of genetic and environmental influences, and (iii) providing a method for hypothesis testing of genetic and environmental variance components. The random change point model, in contrast to linear and quadratic random effects models, is shown to be very flexible in capturing variability as a function of age. Approximate maximum likelihood inference through first-order linearization of the random change point model is contrasted with Bayesian inference based on Markov chain Monte Carlo simulation. In a set of simulations based on a twin model for informative nonresponse, it is demonstrated how the effect of nonresponse on estimates of genetic and environmental variance components depends on the underlying nonresponse mechanism. This thesis also reveals that the standard procedure for testing variance components is inadequate, since the null hypothesis places the variance components on the boundary of the parameter space. The asymptotic distribution of the likelihood ratio statistic for testing variance components in classical twin models is derived, resulting in a mixture of chi-square distributions. Statistical methodology is illustrated with applications to empirical data on cognitive function from a longitudinal twin study of aging.

Thesis (Ph.D.)--James Madison University, 2009. / Includes bibliographical references.

Em pesquisas da área da saúde é muito comum que o pesquisador defronte-se com o problema de dados faltantes. Nessa situação, é freqüente que a decisão do pesquisador seja desconsiderar os sujeitos que tenham não-resposta em alguma ou algumas das variáveis, pois muitas das técnicas estatísticas foram desenvolvidas para analisar dados completos. Entretanto, essa exclusão de sujeitos pode gerar inferências que não são válidas, principalmente se os indivíduos que permanecem na análise são diferentes daqueles que foram excluídos. Nas duas últimas décadas, métodos de imputação de dados foram desenvolvidos com a intenção de se encontrar solução para esse problema. Esses métodos usam como base a idéia de preencher os dados faltantes com valores plausíveis. O método mais complexo de imputação é a chamada imputação múltipla. Essa tese tem por objetivo divulgar o método de imputação múltipla e através de dois artigos procura atingir esse objetivo. O primeiro artigo descreve duas técnicas de imputação múltipla e as aplica a um conjunto de dados reais. O segundo artigo faz a comparação do método de imputação múltipla com duas técnicas de imputação única através de uma aplicação a um modelo de risco para mortalidade cirúrgica. Para as aplicações foram usados dados secundários já utilizados por Klück (2004). / Missing data in health research is a very common problem. The most direct way of dealing with missing data is to exclude observations with missing data, probably because the traditional statistical methods have been developed for complete data sets. However, this decision may give biased results, mainly if the subjects considered in the analysis are different of those who have been excluded. In the last two decades, imputation methods were developed to solve this problem. The idea of the imputation is to fill in the missing data with reasonable values. The multiple imputation is the most complex method. The objective of this dissertation is to divulge the multiple imputation method through two papers. The first one describes two different types of multiple imputation and it shows an application to real data. The second paper shows a comparison among the multiple imputation and two single imputations applied to a risk model for surgical mortality. The used data sets were secondary data used by Klück (2004).

Em pesquisas da área da saúde é muito comum que o pesquisador defronte-se com o problema de dados faltantes. Nessa situação, é freqüente que a decisão do pesquisador seja desconsiderar os sujeitos que tenham não-resposta em alguma ou algumas das variáveis, pois muitas das técnicas estatísticas foram desenvolvidas para analisar dados completos. Entretanto, essa exclusão de sujeitos pode gerar inferências que não são válidas, principalmente se os indivíduos que permanecem na análise são diferentes daqueles que foram excluídos. Nas duas últimas décadas, métodos de imputação de dados foram desenvolvidos com a intenção de se encontrar solução para esse problema. Esses métodos usam como base a idéia de preencher os dados faltantes com valores plausíveis. O método mais complexo de imputação é a chamada imputação múltipla. Essa tese tem por objetivo divulgar o método de imputação múltipla e através de dois artigos procura atingir esse objetivo. O primeiro artigo descreve duas técnicas de imputação múltipla e as aplica a um conjunto de dados reais. O segundo artigo faz a comparação do método de imputação múltipla com duas técnicas de imputação única através de uma aplicação a um modelo de risco para mortalidade cirúrgica. Para as aplicações foram usados dados secundários já utilizados por Klück (2004). / Missing data in health research is a very common problem. The most direct way of dealing with missing data is to exclude observations with missing data, probably because the traditional statistical methods have been developed for complete data sets. However, this decision may give biased results, mainly if the subjects considered in the analysis are different of those who have been excluded. In the last two decades, imputation methods were developed to solve this problem. The idea of the imputation is to fill in the missing data with reasonable values. The multiple imputation is the most complex method. The objective of this dissertation is to divulge the multiple imputation method through two papers. The first one describes two different types of multiple imputation and it shows an application to real data. The second paper shows a comparison among the multiple imputation and two single imputations applied to a risk model for surgical mortality. The used data sets were secondary data used by Klück (2004).

Em pesquisas da área da saúde é muito comum que o pesquisador defronte-se com o problema de dados faltantes. Nessa situação, é freqüente que a decisão do pesquisador seja desconsiderar os sujeitos que tenham não-resposta em alguma ou algumas das variáveis, pois muitas das técnicas estatísticas foram desenvolvidas para analisar dados completos. Entretanto, essa exclusão de sujeitos pode gerar inferências que não são válidas, principalmente se os indivíduos que permanecem na análise são diferentes daqueles que foram excluídos. Nas duas últimas décadas, métodos de imputação de dados foram desenvolvidos com a intenção de se encontrar solução para esse problema. Esses métodos usam como base a idéia de preencher os dados faltantes com valores plausíveis. O método mais complexo de imputação é a chamada imputação múltipla. Essa tese tem por objetivo divulgar o método de imputação múltipla e através de dois artigos procura atingir esse objetivo. O primeiro artigo descreve duas técnicas de imputação múltipla e as aplica a um conjunto de dados reais. O segundo artigo faz a comparação do método de imputação múltipla com duas técnicas de imputação única através de uma aplicação a um modelo de risco para mortalidade cirúrgica. Para as aplicações foram usados dados secundários já utilizados por Klück (2004). / Missing data in health research is a very common problem. The most direct way of dealing with missing data is to exclude observations with missing data, probably because the traditional statistical methods have been developed for complete data sets. However, this decision may give biased results, mainly if the subjects considered in the analysis are different of those who have been excluded. In the last two decades, imputation methods were developed to solve this problem. The idea of the imputation is to fill in the missing data with reasonable values. The multiple imputation is the most complex method. The objective of this dissertation is to divulge the multiple imputation method through two papers. The first one describes two different types of multiple imputation and it shows an application to real data. The second paper shows a comparison among the multiple imputation and two single imputations applied to a risk model for surgical mortality. The used data sets were secondary data used by Klück (2004).

在問卷調查中未作答（nonresponse ）的情況可能有兩種形式：一種情況為整份問卷 都未作答（total nonresponse ）；另一種情況則是部份問項未作答（item nonres- ponse ），包括答不知道或無意見等。前者常是學者研究的重要課題，而後者則直到 近一、二十年來，因學者懷疑其可能會產生系統性誤差而受到重視。又國內從事民意 調查的機構也常碰到此問題，即在某些問題上，填不知道者之比率其高，不知是否會 影響實質意見之代表性。但國內有關這方面的研究幾已達闕如之地步，本論乃為此需 要而撰寫。 本論文共一冊，約三到四萬字左右。文分四章，其主要內容如下： 第一章緒論共分三節，旨在說明研究動機、研究目的；探討相關文獻；提出研究架構 ；並根據文獻擬出研究假設。 第二章研究方法共分三節，旨在說明抽樣方法；研究工具及資料處理與統計分析。本 論文乃以國立政治大學大學部學生為研究母體；採系統統性抽樣；樣本數約一千三百 名左右。研究工具乃由九種量表組成。用SPSS及BMDP兩種套裝程式來處理資料。 第三章結果的分析與討論，共分四節。包括問項中填不知道者在社會學變項及心理學 變項上之特性分析；問題型式與填不知道選項多寡之分析；問題內容與填不知道選項 多寡之分析；在不同實驗操弄下，填不知道選項多寡與實質意見分佈狀況之分析。 第四章結論與建議，共分兩節。包括結論、檢討與建議。