Modelling Information Search Behaviour of Car Purchasers

1989 ◽  
Vol 19 (3) ◽  
pp. 138-143 ◽  
Author(s):  
Phillip J. Du Plessis ◽  
Michael J. Greenacre
Keyword(s):  
Logistic Regression ◽  
Information Search ◽  
Ordinal Logistic Regression ◽  
Predictor Variables ◽  
Sources Of Information ◽  
Search Behaviour ◽  
Information Usage ◽  
Used Car ◽  
External Sources ◽  
First Time

The objective in the study was to establish whether there was any relationship between certain information usage categories and four selected predictor variables namely (1) new or used car purchase, (2) other-than-white or white buyer, (3) male or female, and (4) first-time buyer or experienced buyer. Certain external sources of information (non-market dominated and market dominated) which are available to the buyer of a car and the development of a model of the probability of buyers using the source are investigated. The technique of ordinal logistic regression is assumed to be the appropriate modelling tool in this study where the response variables of interest are ordinal.

Mutual fund investor’s behaviour towards information search and selection criteria

2018 ◽  
Vol 10 (4) ◽  
pp. 395-414 ◽  
Author(s):  
Inderjit Kaur
Keyword(s):  
Mutual Funds ◽  
Information Search ◽  
Mutual Fund ◽  
Selection Criteria ◽  
Selection Process ◽  
Sources Of Information ◽  
National Capital Region ◽  
Search Behaviour ◽  
Content Type ◽  
Search And Selection

PurposeThe fund selection process of investors in a mutual fund needs to be understood for designing better marketing strategies. Knowledge and perception about the mutual funds can affect investor’s behaviour towards information search and selection criteria during the decision process. Therefore, this study aims to examine Indian mutual fund investors under the framework of Theory of Planned Behaviour and consumer’s behaviour model.Design/methodology/approachThe data have been collected from mutual fund investors in the National Capital Region–Delhi, India, through structured questionnaire. The collected data were examined with relevant statistical tools.FindingsKnowledge and perception affect information search behaviour of the investor. Investors having better knowledge of mutual funds access impersonal sources of information and performance of fund affects their choice, whereas investors having lesser knowledge of mutual fund take advice of experts and select funds based on fund characteristics. Investors with better return perception for mutual funds ignore performance as selection criteria, whereas investors having poor risk perception tend to reduce their bias by accessing personal sources of information. Education and income of investor affect knowledge and perception of mutual funds.Practical implicationsThe financial advisor-driven investors ignore performance as selection criteria and could lead to dissatisfaction later. Therefore, to make the industry investor driven, mutual funds need to focus on improving the knowledge of investors.Originality/valueThis paper shows the unique effect of knowledge and perception on information search behaviour of investors towards mutual funds. The knowledgeable investor selects mutual funds by understanding all risks and benefits.

scholarly journals PEMODELAN INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA BARAT, JAWA TIMUR DAN JAWA TENGAH TAHUN 2019 DENGAN MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL

2021 ◽  
Vol 10 (1) ◽  
pp. 149-158
Author(s):  
Meylita Sari ◽  
Purhadi Purhadi
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Logistic Regression ◽  
Unemployment Rate ◽  
Classification Accuracy ◽  
Poverty Rate ◽  
Ordinal Logistic Regression ◽  
Predictor Variables ◽  
Clean Water ◽  
Economic Growth Rate

Ordinal logistic regression is one of the statistical methods to analyze response variables (dependents) that have an ordinal scale consisting of three or more categories. Predictor variables (independent) that can be included in the model are category or continuous data consisting of two or more  variables. Human Development Index (HDI) is an indicator of the success of human development in a region and can be categorized into medium, high and very high. Based on the further categorization, in this study would like to know more about the HDI model using the Ordinal Logistic Regression method, with predictor variables that are suspected to affect, so that it is obtained in West Java Province is influenced by variable poverty rates and clean water sources with a classification accuracy value of 77.78%, Central Java Province is influenced by variable economic growth rate based on constant price GDP, poverty rate and open unemployment rate with a classification accuracy value of 82.85%. East Java province is influenced by variable poverty rate and open unemployment rate with a classification accuracy value of 76.31%. As well as in the three provinces in Java Island is influenced by variable economic growth rate, variable poverty rate, variable clean water source with a classification accuracy value of 73%. Keywords : Ordinal Logistic Regression, HDI, Classification Accuracy

Predict the Medicare Functional Classification Level (K-level) using the Amputee Mobility Predictor in people with unilateral transfemoral and transtibial amputation: A pilot study

2017 ◽  
Vol 42 (2) ◽  
pp. 191-197 ◽  
Author(s):  
Michael P Dillon ◽  
Matthew J Major ◽  
Brian Kaluf ◽  
Yuri Balasanov ◽  
Stefania Fatone
Keyword(s):  
Logistic Regression ◽  
Pilot Study ◽  
Patient Characteristics ◽  
Ordinal Logistic Regression ◽  
Predictor Variables ◽  
Functional Classification ◽  
Transfemoral Amputation ◽  
Potential Predictor ◽  
Younger Age ◽  
Representative Samples

Background: While Amputee Mobility Predictor scores differ between Medicare Functional Classification Levels (K-level), this does not demonstrate that the Amputee Mobility Predictor can accurately predict K-level. Objectives: To determine how accurately K-level could be predicted using the Amputee Mobility Predictor in combination with patient characteristics for persons with transtibial and transfemoral amputation. Study design: Prediction. Method: A cumulative odds ordinal logistic regression was built to determine the effect that the Amputee Mobility Predictor, in combination with patient characteristics, had on the odds of being assigned to a particular K-level in 198 people with transtibial or transfemoral amputation. Results: For people assigned to the K2 or K3 level by their clinician, the Amputee Mobility Predictor predicted the clinician-assigned K-level more than 80% of the time. For people assigned to the K1 or K4 level by their clinician, the prediction of clinician-assigned K-level was less accurate. The odds of being in a higher K-level improved with younger age and transfemoral amputation. Conclusion: Ordinal logistic regression can be used to predict the odds of being assigned to a particular K-level using the Amputee Mobility Predictor and patient characteristics. This pilot study highlighted critical method design issues, such as potential predictor variables and sample size requirements for future prospective research. Clinical relevance This pilot study demonstrated that the odds of being assigned a particular K-level could be predicted using the Amputee Mobility Predictor score and patient characteristics. While the model seemed sufficiently accurate to predict clinician assignment to the K2 or K3 level, further work is needed in larger and more representative samples, particularly for people with low (K1) and high (K4) levels of mobility, to be confident in the model’s predictive value prior to use in clinical practice.

scholarly journals PENERAPAN REGRESI LOGISTIK ORDINAL UNTUK MENGANALISIS TINGKAT KEPARAHAN KORBAN KECELAKAAN LALU LINTAS KABUPATEN BULELENG

2015 ◽  
Vol 4 (2) ◽  
pp. 54
Author(s):  
DEWA AYU MADE DWI YANTI PURNAMI ◽  
I KOMANG GDE SUKARSA ◽  
G. K. GANDHIADI
Keyword(s):  
Logistic Regression ◽  
Statistical Method ◽  
Traffic Accident ◽  
Ordinal Logistic Regression ◽  
Ordinal Scale ◽  
Predictor Variables ◽  
The Status ◽  
Minor Injuries ◽  
Serious Injuries

Ordinal logistic regression is a statistical method for analyzing the respone variables that have an ordinal scale consisting of three or more categories. This method is an extension of logistic regression with a binary respone variable. In this study the cases studies was the severity of traffic accident victims in Buleleng. The severity of the victims were divided into three categories: minor injuries, serious injuries and died. This research also used six predictor variables, namely age, hours of accident, education, gender, the status of location, and the venicles involved. Result of study shows that the variables age, hours of accident, education and the status of location have a significan effect on the severity of traffic accident victims.

Knowledge Organization Systems in Canadian Digital Library Collections

2013 ◽  
Author(s):  
Ali Shiri
Keyword(s):  
Digital Library ◽  
Information Search ◽  
Knowledge Organization ◽  
Digital Collections ◽  
Search Behaviour ◽  
Knowledge Organization Systems ◽  
Knowledge Organization System ◽  
Use Of Knowledge ◽  
Library Collections

The paper reports on a study of the ways in which Canadian digital library collections make use of knowledge organization systems to support users’ information search behaviour. The study identified 33 digital collections which have employed some type of knowledge organization system in their search interfaces.Cet article présente les résultats d’une étude sur la manière dont les systèmes d’organisation des connaissances sont utilisés par les collections des bibliothèques numériques canadiennes, afin d’assister le comportement de recherche informationnelle des utilisateurs. Cette étude a identifiée 33 collections numériques qui ont employé certains types de systèmes d’organisation des connaissances dans leurs interfaces de recherche. 

scholarly journals Global Sensory Impairment Independently Predicts Decreased Social Function Over Time

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 414-414
Author(s):  
Anna Huang ◽  
Kristen Wroblewski ◽  
Ashwin Kotwal ◽  
Linda Waite ◽  
Martha McClintock ◽  
...  
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Cognitive Function ◽  
Social Engagement ◽  
Social Function ◽  
Ordinal Logistic Regression ◽  
Sensory Function ◽  
Sensory Impairment ◽  
Over Time

Abstract The classical senses (vision, hearing, touch, taste, and smell) play a key role in social function by allowing interaction and communication. We assessed whether sensory impairment across all 5 modalities (global sensory impairment [GSI]) was associated with social function in older adults. Sensory function was measured in 3,005 home-dwelling older U.S. adults at baseline in the National Social Life, Health, and Aging Project and GSI, a validated measure, was calculated. Social network size and kin composition, number of close friends, and social engagement were assessed at baseline and 5- and 10-year follow-up. Ordinal logistic regression and mixed effects ordinal logistic regression analyzed cross-sectional and longitudinal relationships respectively, controlling for demographics, physical/mental health, disability, and cognitive function (at baseline). Adults with worse GSI had smaller networks (β=-0.159, p=0.021), fewer close friends (β=-0.262, p=0.003) and lower engagement (β=-0.252, p=0.006) at baseline, relationships that persisted at 5 and 10 year follow-up. Men, older people, African-Americans, and those with less education, fewer assets, poor mental health, worse cognitive function, and more disability had worse GSI. Men and those with fewer assets, worse cognitive function, and less education had smaller networks and lower engagement. African-American and Hispanic individuals had smaller networks and fewer close friends, but more engagement. Older respondents also had more engagement. In summary, GSI independently predicts smaller social networks, fewer close friends, and lower social engagement over time, suggesting that sensory decline results in decreased social function. Thus, rehabilitating multisensory impairment may be a strategy to enhance social function as people age.

Sources of Information About Gestational Weight Gain, Diet, and Exercise Among Brazilian Immigrant Women Living in the United States: A Cross-Sectional Study

2021 ◽  
pp. 1-41
Author(s):  
Ana Cristina Lindsay ◽  
Qun Le ◽  
Denise Lima Nogueira ◽  
Márcia M. T. Machado ◽  
Mary L. Greaney
Keyword(s):  
United States ◽  
Weight Gain ◽  
Gestational Weight Gain ◽  
The United States ◽  
The Internet ◽  
Sources Of Information ◽  
Cross Sectional ◽  
Gestational Weight ◽  
Diet And Exercise ◽  
First Time

Abstract Objectives: The objective of this study was to assess sources of information about gestational weight gain (GWG), diet, and exercise among first-time pregnant Brazilian women in the United States (US). Design: Cross-sectional survey. Setting: Massachusetts, United States. Participants: First-time pregnant Brazilian women. Results: Eighty-six women, the majority of whom were immigrants (96.5%) classified as having low-acculturation levels (68%), participated in the study. Approximately two-thirds of respondents had sought information about GWG (72.1%), diet (79.1%), and exercise (74.4%) via the internet. Women classified as having low acculturation levels were more likely to seek information about GWG via the internet (OR = 7.55; 95% CI: 1.41, 40.26) than those with high acculturation levels after adjusting for age and receiving information about GWG from healthcare provider (doctor or midwife). Moreover, many respondents reported seeking information about GWG (67%), diet (71%), and exercise (52%) from family and friends. Women who self-identified as being overweight pre-pregnancy were less likely to seek information about diet (OR = 0.32; 95% CI: 0.11, 0.93) and exercise (OR = 0.33; 95% CI: 0.11, 0.96) from family and friends than those who self-identified being normal weight pre-pregnancy. Conclusions: This is the first study to assess sources of information about GWG, diet, and exercise among pregnant Brazilian immigrants in the US. Findings have implications for the design of interventions and suggest the potential of mHealth intervention as low-cost, easy access option for delivering culturally and linguistically tailored evidence-based information about GWG incorporating behavioral change practices to this growing immigrant group.

Analysis on application level based on ordinal logistic regression and best of advanced manufacturing technologies (AMT) selection based on fuzzy-TOPSIS integration approach

2021 ◽  
pp. 1-11
Author(s):  
Guilian Wang ◽  
Liyan Zhang ◽  
Jing Guo
Keyword(s):  
Logistic Regression ◽  
Organizational Factors ◽  
Fuzzy Topsis ◽  
Ordinal Logistic Regression ◽  
Industrial Robots ◽  
Policy Implications ◽  
Market Pricing ◽  
Factors Affecting ◽  
Pricing Power ◽  
The Core Competence

This paper try to fully reveal the key factors affecting the the level of AMT application in micro- and small enterprises (MSEs) from its organizational factors by ordinal logistic regression. The results show that MSEs have a relatively high level of AMT application as a whole due to the maturity and cost reduction of basic technologies such as artificial intelligence, digital manufacturing and industrial robots. In this paper we propose manufacturing world analysis at Application using Logistic Regression and best AMT selection using Fuzzy-TOPSIS Integration approach.Considering the influence mechanism of each factor, the important factors that affect the application level of AMT are the enterprise’s market pricing power, the main production types, technical, market and management capabilities, organization development incentives and the interaction with external stakeholders. Based on the results above, the following policy implications are proposed: further expanding the customized production in MSEs to gradually improve the market pricing power, expanding the core competence of enterprises, enhancing the employee autonomy, and strengthening the interaction with industry organizations.

Characteristics of Adenocarcinoma of the Gallbladder Following Cholecystectomies in the Hispanic Population

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S64-S65
Author(s):  
David Gustafson ◽  
Osvaldo Padilla
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Incidental Finding ◽  
Conditional Logistic Regression ◽  
Gallbladder Disease ◽  
Predictor Variables ◽  
Wall Thickening ◽  
Conditional Logistic Regression Analysis

Abstract Introduction Gallbladder adenocarcinoma (GBC) is a rare malignancy. Frequency of incidental adenocarcinoma of the gallbladder in the literature is approximately 0.2% to 3%. Typically, GBC is the most common type and is discovered late, not until significant symptoms develop. Common symptoms include right upper quadrant pain, nausea, anorexia, and jaundice. A number of risk factors in the literature are noted for GBC. These risk factors are also more prevalent in Hispanic populations. This study sought to compare patients with incidental gallbladder adenocarcinomas (IGBC) to those with high preoperative suspicion for GBC. Predictor variables included age, sex, ethnicity, radiologic wall thickening, gross pathology characteristics (wall thickness, stone size, stone number, and tumor size), histologic grade, and staging. Methods Cases of GBC were retrospectively analyzed from 2009 through 2017, yielding 21 cases. Data were collected via Cerner EMR of predictor variables noted above. Statistical analysis utilized conditional logistic regression analysis. Results The majority of patients were female (n = 20) and Hispanic (n = 19). There were 14 IGBCs and 7 nonincidental GBCs. In contrast with previous research, exact conditional logistic regression analysis revealed no statistically significant findings. For every one-unit increase in AJCC TNM staging, there was a nonsignificant 73% reduction in odds (OR = 0.27) of an incidental finding of gallbladder carcinoma. Conclusion This study is important in that it attempts to expand existing literature regarding a rare type of cancer in a unique population, one particularly affected by gallbladder disease. Further studies are needed to increase predictive knowledge of this cancer. Longer studies are needed to examine how predictive power affects patient outcomes. This study reinforces the need for routine pathologic examination of cholecystectomy specimens for cholelithiasis.

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