scholarly journals Latent Attitude Method for Trend Analysis with Pooled Survey Data

2021 ◽  
Author(s):  
Donghui Wang ◽  
Yu Xie ◽  
Junming Huang
Keyword(s):  
Survey Data ◽  
Latent Variable ◽  
Survey Design ◽  
Simulated Data ◽  
Likelihood Method ◽  
Survey Analysis ◽  
Measurement Scales ◽  
Attitude Research ◽  
Survey Instruments ◽  
Best Fit

Millions of people are surveyed every year regarding their attitudes toward various topics. Together these surveys have produced a large corps of data that document how people think collectively toward various aspects of contemporary social life.The wealth of the attitude surveys has promoted scholars to move beyond the single-survey analysis. However, the use of survey data for studying trends in attitudes is handicapped by a measurement difficulty: different surveys have used different survey instruments to measure the same attitude and thus have generated data that strictly non-comparable. We propose the Latent Attitude Method (LAM) to address this issue. Our method borrows strength from two research traditions: (1) the latent variable method in attitude research and (2) the comparable distribution condition in survey design and evaluation. The core of this method is that, when two or more surveys overlap in a given year, we assume that the same latent attitude is measured as if two measurement scales are randomly given to two independent samples drawn from the same population. Thus, we can assume the same statistical properties for the latent attitude. In so doing, we are able to reduce the number of unknowns to be less than the number of established equations and estimate the best-fit parameters with maximum likelihood method. We demonstrate the utility of the method with simulated data, and apply the method to an empirical example of estimating America’s attitude toward China from 1974 to 2019.

scholarly journals Hyperbolastic Models from a Stochastic Differential Equation Point of View

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1835
Author(s):  
Antonio Barrera ◽  
Patricia Román-Román ◽  
Francisco Torres-Ruiz
Keyword(s):  
Differential Equation ◽  
Simulated Data ◽  
Real Data ◽  
Point Of View ◽  
Likelihood Method ◽  
Numerical Resolution ◽  
The Family ◽  
The Common ◽  
Linear Differential ◽  
Mean Function

A joint and unified vision of stochastic diffusion models associated with the family of hyperbolastic curves is presented. The motivation behind this approach stems from the fact that all hyperbolastic curves verify a linear differential equation of the Malthusian type. By virtue of this, and by adding a multiplicative noise to said ordinary differential equation, a diffusion process may be associated with each curve whose mean function is said curve. The inference in the resulting processes is presented jointly, as well as the strategies developed to obtain the initial solutions necessary for the numerical resolution of the system of equations resulting from the application of the maximum likelihood method. The common perspective presented is especially useful for the implementation of the necessary procedures for fitting the models to real data. Some examples based on simulated data support the suitability of the development described in the present paper.

Restricted quasi-score estimating functions for sample survey data

2004 ◽  
Vol 41 (A) ◽  
pp. 119-130
Author(s):  
Y.-X. Lin ◽  
D. Steel ◽  
R. L Chambers
Keyword(s):  
Maximum Likelihood ◽  
Survey Data ◽  
Sample Survey ◽  
Likelihood Method ◽  
Sample Surveys ◽  
Estimating Functions ◽  
Limit Theory ◽  
Normal Probability ◽  
Probability Structure ◽  
Sample Survey Data

This paper applies the theory of the quasi-likelihood method to model-based inference for sample surveys. Currently, much of the theory related to sample surveys is based on the theory of maximum likelihood. The maximum likelihood approach is available only when the full probability structure of the survey data is known. However, this knowledge is rarely available in practice. Based on central limit theory, statisticians are often willing to accept the assumption that data have, say, a normal probability structure. However, such an assumption may not be reasonable in many situations in which sample surveys are used. We establish a framework for sample surveys which is less dependent on the exact underlying probability structure using the quasi-likelihood method.

Likelihood-Based Inference for Multi-Color Optical Mapping

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Liping Tong ◽  
Laurens Mets ◽  
Mary Sara McPeek
Keyword(s):  
Latent Variable ◽  
Optical Mapping ◽  
Physical Map ◽  
Profile Likelihood ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Error Rates ◽  
Mapping Procedure ◽  
Recognition Sites ◽  
Estimate Error

Multi-color optical mapping is a new technique being developed to obtain detailed physical maps (indicating relative positions of various recognition sites) of DNA molecules. We consider a study design in which the data consist of noisy observations of multiple copies of a DNA molecule marked with colors at recognition sites. The primary goal is to estimate a physical map. A secondary goal is to estimate error rates associated with the experiment, which are potentially useful for analysis and refinement of the biochemical steps in the mapping procedure. We propose statistical models for various sources of error and use maximum likelihood estimation (MLE) to construct a physical map and estimate error rates. To overcome difficulties arising in the maximization process, a latent-variable Markov chain version of the model is proposed, and the EM algorithm is used for maximization. In addition, a simulated annealing procedure is applied to maximize the profile likelihood over the discrete space of sequences of colors. We apply the methods to simulated data on the bacteriophage lambda genome.

Best Practice in Online Survey Research with Sensitive Topics

2013 ◽  
pp. 157-168 ◽  
Author(s):  
Kristina M. Kays ◽  
Tashina L. Keith ◽  
Michael T. Broughal
Keyword(s):  
Gender Differences ◽  
Survey Research ◽  
Survey Data ◽  
Best Practice ◽  
Online Survey ◽  
Survey Design ◽  
Sensitive Material ◽  
Sensitive Topics ◽  
Need To Evaluate

This chapter addresses the main considerations in online survey research with sensitive topics. Advances in technology have allowed numerous options in addressing survey design, and thus created a need to evaluate and consider best approaches when using online survey research. This chapter identifies subjects such as item non-response in online survey research. In addition, this chapter includes a description of the differences in researching non-sensitive topics versus sensitive topics, and then lists a number of best practice strategies to reduce item non-response and improve the quality of survey data obtained. Included are specific considerations for defining sensitive topics and addressing gender differences when surveying more sensitive material. Additional resources in online survey research design are recommended.

Interpersonal Perceptions and Interviewer Effects in Face-to-Face Surveys

2020 ◽  
pp. 004912412092621
Author(s):  
Simon Kühne
Keyword(s):  
Survey Data ◽  
Large Scale ◽  
Survey Design ◽  
Political Issues ◽  
Interpersonal Perceptions ◽  
Face To Face ◽  
Interviewer Effects ◽  
Self Reports ◽  
And Training

Survey interviewers can negatively affect survey data by introducing variance and bias into estimates. When investigating these interviewer effects, research typically focuses on interviewer sociodemographics with only a few studies examining the effects of characteristics that are not directly visible such as interviewer attitudes, opinions, and personality. For the study at hand, self-reports of 1,212 respondents and 116 interviewers, as well as their interpersonal perceptions of each other, were collected in a large-scale, face-to-face survey of households in Germany. Respondents and interviewers were presented with the same questions regarding their opinions and mutual perceptions toward social and political issues in Germany. Analyses show that interviewer effects can be largely explained by how an interviewer is seen by respondents. This indicates that some respondents adjust their answers toward anticipated interviewer opinions. Survey practitioners ought to acknowledge this in their survey design and training of interviewers.

Industry Software Reviews Survey Design

2011 ◽  
pp. 156-195
Author(s):  
Yuk Kuen Wong
Keyword(s):  
Data Collection ◽  
Research Design ◽  
Research Methodology ◽  
Survey Design ◽  
Measurement Scales ◽  
Cross Sectional ◽  
Collection Method ◽  
Software Reviews ◽  
Industry Survey ◽  
Analyse Data

Chapters five and six described the theoretical EIIO model; this chapter mainly focuses on industry survey design. The first section describes the research methodology and survey used to gather, collate, and analyse data for the study. After presenting the rationale for the research design, including the questionnaire design, measurement scales, and models. The chapter explores issues of validation and reliability, such as cross sectional research and construct operationalisation. The chapter concludes with a discussion of the data collection method and the analytical procedures used in the study.

Changepoint Analysis of Binary and Ordinal Probit Models: An Application to Bank Rate Policy Under the Interwar Gold Standard

2011 ◽  
Vol 19 (2) ◽  
pp. 188-204 ◽  
Author(s):  
Jong Hee Park
Keyword(s):  
Time Series ◽  
Gold Standard ◽  
Time Series Data ◽  
Likelihood Function ◽  
Marginal Likelihood ◽  
Estimation Method ◽  
Simulated Data ◽  
Likelihood Method ◽  
Series Data ◽  
Ordinal Probit

In this paper, I introduce changepoint models for binary and ordered time series data based on Chib's hidden Markov model. The extension of the changepoint model to a binary probit model is straightforward in a Bayesian setting. However, detecting parameter breaks from ordered regression models is difficult because ordered time series data often have clustering along the break points. To address this issue, I propose an estimation method that uses the linear regression likelihood function for the sampling of hidden states of the ordinal probit changepoint model. The marginal likelihood method is used to detect the number of hidden regimes. I evaluate the performance of the introduced methods using simulated data and apply the ordinal probit changepoint model to the study of Eichengreen, Watson, and Grossman on violations of the “rules of the game” of the gold standard by the Bank of England during the interwar period.

A Bayesian nonparametric semi-supervised model for integration of multiple single-cell experiments

2020 ◽  
Author(s):  
Archit Verma ◽  
Barbara Engelhardt
Keyword(s):  
Single Cell ◽  
Latent Variable ◽  
Environmental Variability ◽  
Simulated Data ◽  
Joint Analysis ◽  
Variable Model ◽  
Manifold Alignment ◽  
Multiple Data Sets ◽  
Sequencing Platforms ◽  
Low Dimensional

Joint analysis of multiple single cell RNA-sequencing (scRNA-seq) data is confounded by technical batch effects across experiments, biological or environmental variability across cells, and different capture processes across sequencing platforms. Manifold alignment is a principled, effective tool for integrating multiple data sets and controlling for confounding factors. We demonstrate that the semi-supervised t-distributed Gaussian process latent variable model (sstGPLVM), which projects the data onto a mixture of fixed and latent dimensions, can learn a unified low-dimensional embedding for multiple single cell experiments with minimal assumptions. We show the efficacy of the model as compared with state-of-the-art methods for single cell data integration on simulated data, pancreas cells from four sequencing technologies, induced pluripotent stem cells from male and female donors, and mouse brain cells from both spatial seqFISH+ and traditional scRNA-seq.Code and data is available at https://github.com/architverma1/sc-manifold-alignment

scholarly journals Using Analytic Tools with California School Library Survey Data

2015 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Lesley Farmer ◽  
Alan Safer ◽  
Joanna Leack
Keyword(s):  
Survey Data ◽  
School Library ◽  
School Libraries ◽  
State Standards ◽  
Funding Sources ◽  
Library Resources ◽  
Data Points ◽  
Cart Algorithm ◽  
Best Fit ◽  
Standard Library

Abstract Objective — California school libraries have new state standards, which can serve to guide their programs. Based on pre-standard and post-standard library survey data, this research compares California school library programs to determine the variables that can potentially help a school library reach the state standards, and to develop a predictive model of those variables. Methods – Variations of decision trees and logistic regression statistical techniques were applied to the library survey data in order to create the best-fit model. Results – Best models were chosen within each technique, and then compared, concluding that the decision tree using the CART algorithm had the most accurate results. Numerous variables came up as important across different models, including: funding sources, collection size, and access to online subscriptions. Conclusion – School library metrics can help both librarians and the educational community analyze school library programs closely and determine effective ways to maximize the school library’s impact on student learning. More generally, library resources and services can be measured as data points, and then modeling statistics can be applied in order to optimize library operations.

scholarly journals Integrating Probability and Nonprobability Samples for Survey Inference

2020 ◽  
Vol 8 (1) ◽  
pp. 120-147 ◽  
Author(s):  
Arkadiusz Wiśniowski ◽  
Joseph W Sakshaug ◽  
Diego Andres Perez Ruiz ◽  
Annelies G Blom
Keyword(s):  
Data Collection ◽  
Linear Model ◽  
Survey Data ◽  
Simulated Data ◽  
Empirical Literature ◽  
Informative Priors ◽  
Small Probability ◽  
Prior Distributions ◽  
Probability Sampling ◽  
Mean Squared Errors

Abstract Survey data collection costs have risen to a point where many survey researchers and polling companies are abandoning large, expensive probability-based samples in favor of less expensive nonprobability samples. The empirical literature suggests this strategy may be suboptimal for multiple reasons, among them that probability samples tend to outperform nonprobability samples on accuracy when assessed against population benchmarks. However, nonprobability samples are often preferred due to convenience and costs. Instead of forgoing probability sampling entirely, we propose a method of combining both probability and nonprobability samples in a way that exploits their strengths to overcome their weaknesses within a Bayesian inferential framework. By using simulated data, we evaluate supplementing inferences based on small probability samples with prior distributions derived from nonprobability data. We demonstrate that informative priors based on nonprobability data can lead to reductions in variances and mean squared errors for linear model coefficients. The method is also illustrated with actual probability and nonprobability survey data. A discussion of these findings, their implications for survey practice, and possible research extensions are provided in conclusion.

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