From Measures of Association to Multilevel Models: Sociology Journals and the Quantitative Literacy Gap

2020 ◽  
pp. 0092055X2098042
Author(s):  
Thomas J. Linneman
Keyword(s):  
Multilevel Models ◽  
Ordinary Least Squares ◽  
Quantitative Literacy ◽  
Least Squares Regression ◽  
Journal Articles ◽  
Measures Of Association ◽  
Chi Square ◽  
Literacy Gap ◽  
Statistics Course ◽  
Sociology Journals

While most sociology majors must take a statistics course, the content of this course varies widely across departments. Starting from the assumption that sociology students should be able to engage effectively with the sociological literature, this article examines the statistical techniques used in 2,804 journal articles—from four generalist sociology journals from 1990 to 2019 and 11 additional sociology journals from 2019—in order to assess which techniques have risen or fallen in prevalence. Although stalwarts such as ordinary least squares regression, chi-square tests, and t tests maintain strong presences, the rise of logistic regression, interaction effects, and multilevel models has been dramatic. After assessing the proportion of articles students hypothetically could understand given various levels of statistical training, the article ends with suggestions for how to revamp the statistics course to help our students become more numerate citizens, both in their sociology courses and in the world at large.

scholarly journals Combining Regression and Factor Analysis for Use in Agricultural Economics Research

1976 ◽  
Vol 8 (2) ◽  
pp. 145-149
Author(s):  
John T. Scott
Keyword(s):  
Time Series Data ◽  
Correlation Coefficients ◽  
Ordinary Least Squares ◽  
Statistical Technique ◽  
Regression Coefficients ◽  
Series Data ◽  
Least Squares Regression ◽  
Chi Square ◽  
Explanatory Variables ◽  
Chi Square Test

While ordinary least squares regression has become a standard statistical technique, there are problems frequently overlooked or ignored by researchers in applying this statistical method. Two basic assumptions of the OLS regression model—(1) that the explanatory variables are independent of each other and (2) that the explanatory variables are known, fixed numbers—do not hold for most economic data, particularly time series data. This has been a consternation for econometricians, if not for the general researcher, for many years.In the case of nonindependence of explanatory variables (multicollinearity), signs of the regression coefficients often are inconsistent with economic theory and with correlation coefficients calculated from the data. Also, variances of the estimated regression coefficients are inconsistent. In practice for prediction equations, multicollinearity can usually be sufficiently reduced by either dropping one or more multicollinear variables or by indexing them and using the index as a regressor, thus circumventing the assumption regarding independence of the explanatory variables. A chi-square test for multicollinearity is available, and can be used as a guide to alert a researcher to the problem.

scholarly journals Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters

2016 ◽  
Vol 78 (2) ◽  
pp. 297-318 ◽  
Author(s):  
Francis L. Huang
Keyword(s):  
Randomized Trials ◽  
Multilevel Models ◽  
Clustered Data ◽  
Ordinary Least Squares ◽  
Alternative Procedure ◽  
Least Squares Regression ◽  
Cluster Randomized Trials ◽  
Number Of Clusters ◽  
Nested Data ◽  
Cluster Randomized

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

scholarly journals Psychological distress during the COVID-19 epidemic in Chile: the role of economic uncertainty

2020 ◽  
Author(s):  
Fabian Duarte ◽  
Alvaro Jimenez-Molina
Keyword(s):  
Psychological Distress ◽  
Adult Population ◽  
Well Being ◽  
Ordinary Least Squares ◽  
Depression Symptoms ◽  
Least Squares Regression ◽  
Economic Uncertainty ◽  
Chi Square ◽  
Data Set ◽  
Psychological Well Being

Previous research has shown that the COVID-19 outbreak, social distancing and lockdown can affect people's psychological well-being. The aim of this study was to estimate the extent to which perceptions and expectations regarding the social, economic and domestic effects of the COVID-19 outbreak are associated with psychological distress, and identify some demographic, psychosocial and economic factors associated with increased vulnerability to psychological distress during the COVID-19 outbreak in Chile. 1078 people participated in a telephone survey between May 30 and June 10, 2020. The sample is representative of the Chilean adult population. Psychological distress was assessed through a questionnaire of anxious and depressive symptoms (Patient Health Questionnaire-4). We analyze the data set using ordinary least-squares regression models, first estimating models for the entire sample, and then stratifying the sample into different groups to explore differences by gender and age. 19.2% participants displayed significant psychological distress, with moderate to severe anxiety-depression symptoms being more prevalent in women than in men (23.9% vs 14.09%, chi square 6.89, p < 0.001). The results of this study suggest that being a woman, feeling lonely and isolated, living in the areas hit hardest by the pandemic and lockdown, expecting a lack of income due to having to stop working as a consequence of the pandemic, and having a history of mental health diagnosis are significantly associated with psychological distress (p < 0.05). The results of this study highlight the need to implement psychosocial programs to protect people's psychological well-being and social policies to address economic uncertainty during the current COVID-19 outbreak in Chile.

Impact of firm’s specific factors on audit fee of quoted consumer goods firms

2020 ◽  
Vol 4 (1) ◽  
pp. 47-55
Author(s):  
Wasiu Ajani Musa ◽  
Ramat Titilayo Salman ◽  
Ibrahim Olayiwola Amoo ◽  
Muhammed Lawal Subair
Keyword(s):  
Consumer Goods ◽  
Audit Quality ◽  
Audit Fees ◽  
Ordinary Least Squares ◽  
P Value ◽  
Chi Square ◽  
Ifrs Adoption ◽  
Audit Fee ◽  
Specific Factors ◽  
The Impact

Greater pricing presume on audit service has been put by the regulations of the auditing and accounting practices for the disclosure of audit fees, since audit fee is directly related to audit quality. However, the audit fees perceived by the client is often different from the amount charged by the auditors. Hence, this study investigated the impact of firm-specific characteristics on audit fees of quoted consumer goods firms in Nigeria using a purposive sampling technique. Secondary data were obtained from annual reports of the companies for the period from 2009-2016. The empirical result from Breusch-Pagan Lagrange Multiplier Test (BP-LM) produced a chi-square value of 13.94 with p-value of 0.0001 indicating that pooled ordinary least squares (OLS) will not be appropriate for the study. The Hausman test showed a chi-square of 23.55 with a p-value of 0.001 indicating that the null hypothesis is strongly rejected. Thus, the only estimate from the fixed effect model was interpreted to explain the relationship between firm-specific characteristics and audit fees of quoted consumer goods firms in Nigeria. The result revealed that auditee size, auditee risk, auditee profitability and IFRS adoption are the firm specific characteristics that impact on audit fees with only auditee size and IFRS adoption being positively related to audit fees while the other factors are negatively related to audit fees. Based on this finding, this study concluded that the firm’s specific factors are the major drivers of audit fees in Nigeria consumer goods firms. This study recommends among others that companies should implement corporate governance principles that address issues relating to board independence and committee sizes to guide activities in the consumer goods sector since profitability behave negatively with audit fees.

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

Home Health Agencies: Empirical Evidence on the Patient-Driven Groupings Model’s Expected Effects on Agency Reimbursements

2021 ◽  
pp. 108482232199038
Author(s):  
Elizabeth Plummer ◽  
William F. Wempe
Keyword(s):  
Ordinary Least Squares ◽  
Process Quality ◽  
Home Health ◽  
Least Squares Regression ◽  
Residual Variation ◽  
Health Agencies ◽  
Dual Eligible ◽  
Home Health Agencies ◽  
Seriously Ill Patients ◽  
Seriously Ill

Beginning January 1, 2020, Medicare’s Patient-Driven Groupings Model (PDGM) eliminated therapy as a direct determinant of Home Health Agencies’ (HHAs’) reimbursements. Instead, PDGM advances Medicare’s shift toward value-based payment models by directly linking HHAs’ reimbursements to patients’ medical conditions. We use 3 publicly-available datasets and ordered logistic regression to examine the associations between HHAs’ pre-PDGM provision of therapy and their other agency, patient, and quality characteristics. Our study therefore provides evidence on PDGM’s likely effects on HHA reimbursements assuming current patient populations and service levels do not change. We find that PDGM will likely increase payments to rural and facility-based HHAs, as well as HHAs serving greater proportions of non-white, dual-eligible, and seriously ill patients. Payments will also increase for HHAs scoring higher on quality surveys, but decrease for HHAs with higher outcome and process quality scores. We also use ordinary least squares regression to examine residual variation in HHAs’ expected reimbursement changes under PDGM, after accounting for any expected changes related to their pre-PDGM levels of therapy provision. We find that larger and rural HHAs will likely experience residual payment increases under PDGM, as will HHAs with greater numbers of seriously ill, younger, and non-white patients. HHAs with higher process quality, but lower outcome quality, will similarly benefit from PDGM. Understanding how PDGM affects HHAs is crucial as policymakers seek ways to increase equitable access to safe and affordable non-facility-provided healthcare that provides appropriate levels of therapy, nursing, and other care.

scholarly journals Predicting presenteeism using measures of health status

2021 ◽  
Author(s):  
Cheryl Jones ◽  
Katherine Payne ◽  
Alexander Thompson ◽  
Suzanne M. M. Verstappen
Keyword(s):  
Health Status ◽  
Online Survey ◽  
Activity Index ◽  
Work Productivity ◽  
Rank Correlation ◽  
Ordinary Least Squares ◽  
Statistical Correlation ◽  
Least Squares Regression ◽  
Mapping Algorithm ◽  
Spearman’S Rank Correlation

Abstract Objectives To identify whether it is feasible to develop a mapping algorithm to predict presenteeism using multiattribute measures of health status. Methods Data were collected using a bespoke online survey in a purposive sample (n = 472) of working individuals with a self-reported diagnosis of Rheumatoid arthritis (RA). Survey respondents were recruited using an online panel company (ResearchNow). This study used data captured using two multiattribute measures of health status (EQ5D-5 level; SF6D) and a measure of presenteeism (WPAI, Work Productivity Activity Index). Statistical correlation between the WPAI and the two measures of health status (EQ5D-5 level; SF6D) was assessed using Spearman’s rank correlation. Five regression models were estimated to quantify the relationship between WPAI and predict presenteeism using health status. The models were specified based in index and domain scores and included covariates (age; gender). Estimated and observed presenteeism were compared using tenfold cross-validation and evaluated using Root mean square error (RMSE). Results A strong and negative correlation was found between WPAI and: EQ5D-5 level and WPAI (r = − 0.64); SF6D (r =− 0.60). Two models, using ordinary least squares regression were identified as the best performing models specifying health status using: SF6D domains with age interacted with gender (RMSE = 1.7858); EQ5D-5 Level domains and age interacted with gender (RMSE = 1.7859). Conclusions This study provides indicative evidence that two existing measures of health status (SF6D and EQ5D-5L) have a quantifiable relationship with a measure of presenteeism (WPAI) for an exemplar application of working individuals with RA. A future study should assess the external validity of the proposed mapping algorithms.

scholarly journals Lack of Informations about COVID-19 Vaccine: From Implications to Intervention for Supporting Public Health Communications in COVID-19 Pandemic

2021 ◽  
Vol 18 (11) ◽  
pp. 6141
Author(s):  
Silva Guljaš ◽  
Zvonimir Bosnić ◽  
Tamer Salha ◽  
Monika Berecki ◽  
Zdravka Krivdić Dupan ◽  
...  
Keyword(s):  
Vaccine Efficacy ◽  
Health Belief ◽  
Ordinary Least Squares ◽  
Vaccine Hesitancy ◽  
Equal Proportion ◽  
Least Squares Regression ◽  
Online Questionnaire ◽  
Demographic Groups ◽  
Population Immunity ◽  
Wide Range

Lack of knowledge and mistrust towards vaccines represent a challenge in achieving the vaccination coverage required for population immunity. The aim of this study is to examine the opinion that specific demographic groups have about COVID-19 vaccination, in order to detect potential fears and reasons for negative attitudes towards vaccination, and to gain knowledge on how to prepare strategies to eliminate possible misinformation that could affect vaccine hesitancy. The data collection approach was based on online questionnaire surveys, divided into three groups of questions that followed the main postulates of the health belief theory—a theory that helps understanding a behaviour of the public in some concrete surrounding in receiving preventive measures. Ordinary least squares regression analyses were used to examine the influence of individual factors on refusing the vaccine, and to provide information on the perception of participants on the danger of COVID-19 infection, and on potential barriers that could retard the vaccine utility. There was an equal proportion of participants (total number 276) who planned on receiving the COVID-19 vaccine (37%), and of those who did not (36.3%). The rest (26.7%) of participants were still indecisive. Our results indicated that attitudes on whether to receive the vaccine, on how serious consequences might be if getting the infection, as well as a suspicious towards the vaccine efficacy and the fear of the vaccine potential side effects, may depend on participants’ age (<40 vs. >40 years) and on whether they are healthcare workers or not. The barriers that make participants‘ unsure about of receiving the vaccine, such as a distrust in the vaccine efficacy and safety, may vary in different socio-demographic groups and depending on which is the point of time in the course of the pandemic development, as well as on the vaccine availability and experience in using certain vaccine formulas. There is a pressing need for health services to continuously provide information to the general population, and to address the root causes of mistrust through improved communication, using a wide range of policies, interventions and technologies.

Sign in / Sign up

Export Citation Format

Share Document