scholarly journals Relative importance analysis with multivariate models: Shifting the focus from independent variables to parameter estimates

2020 ◽  
Vol 4 (2) ◽  
pp. 1-20
Author(s):  
Joseph N. Luchman ◽  
Xue Lei ◽  
Seth Kaplan
Keyword(s):  
Statistical Models ◽  
Likelihood Estimation ◽  
Parameter Estimate ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Relative Importance ◽  
Multivariate Models ◽  
Independent Variables ◽  
Dependent Variables ◽  
Independent Variable

Conclusions regarding the relative importance of different independent variables in a statistical model have meaningful implications for theory and practice. However, methods for determining relative importance have yet to extend beyond statistical models with a single dependent variable and a limited set of multivariate models. To accommodate multivariate models, the current work proposes shifting away from the concept of independent variable relative importance toward that of parameter estimate relative importance (PERI). This paper illustrates the PERI approach by comparing it to the evaluation of regression slopes and independent variable relative importance (IVRI) statistics to show the interpretive and methodological advantages of the new concept and associated methods. PERI’s advantages above standardized slopes stem from the same fit metric that is used to compute PERI statistics; this makes them more comparable to one another than standardized slopes. PERI’s advantages over IVRI stem from situations where independent variables do not predict all dependent variables; hence, PERI permits importance determination in situations where independent variables are nested in dependent variables they predict. We also provide recommendations for implementing PERI using dominance analysis with statistical models that can be estimated with maximum likelihood estimation combined with a series of model constraints using two examples.

Determining relative importance in Stata using dominance analysis: domin and domme

2021 ◽  
Vol 21 (2) ◽  
pp. 510-538
Author(s):  
Joseph N. Luchman
Keyword(s):  
Statistical Models ◽  
Common Method ◽  
Parameter Estimates ◽  
Relative Importance ◽  
Dominance Analysis ◽  
Independent Variables ◽  
Data Analytic

Dominance analysis is a common method applied to statistical models to determine the importance of independent variables. In this article, I describe two community-contributed commands, domin and domme, that can be used to dominance-analyze both independent variables and parameter estimates in Stata estimation commands. I discuss how to compute dominance statistics, provide multiple examples of each command applied to data, and outline how to interpret the results from each data-analytic example. I conclude with computational considerations for users applying larger models.

scholarly journals PENGARUH RESPONSIVENESS PERAWAT DALAM PRAKTIK KOMUNIKASI TERAPEUTIK TERHADAP KEPUASAN PASIEN INSTALASI RAWAT INAP RSU HAJI SURABAYA

2018 ◽  
Vol 6 (2) ◽  
pp. 173
Author(s):  
Ajenk Nanda Saprilla
Keyword(s):  
Therapeutic Communication ◽  
Patient’S Satisfaction ◽  
Independent Variables ◽  
Patients Satisfaction ◽  
Dependent Variables ◽  
Patient Responsiveness ◽  
Human Resource Training ◽  
Independent Variable ◽  
Strongly Agree ◽  
Service Standards

Background: Patient’s satisfaction is one of indicators measured in the hospital minimum service standards. In Installation of Inpatient (IRNA) of Haji Surabaya hospital, there are 17 indicators, but only nine are met (52.94%). One of them is patients’ satisfaction level amounted to 74.35% out of the standard (82%). The high number of complaints on nurses’ competence in providing services causes the unachieved patients’ satisfaction. There were 61 complaints from 2014 to 2017.Aim: This study aimed to analyze the influence of nurses’ responsiveness to patients’ satisfaction of in-patient installation (IRNA) at Haji Surabaya Hospital.Method: The questionnaires used Likert scale 1-5 for independent variables and dependent variables. The scoring scales for the independent variable or nurses’ responsiveness range from strongly disagree to strongly agree. Meanwhile, the scoring scale for the dependent variable ranges from very dissatisfied to very satisfied.Results: The findings indicated that there was a significant influence of responsiveness on patients’ satisfaction amounted to 0.003 (<α = 0.05). This indicated that the better assessment on the nurses’ responsiveness is, themore satisfied the patients are at in-patient installation (IRNA), Haji Surabaya Hospital.Conclusion: It can be concluded that more than 20% of responses was satisfied with the nurses’ responsiveness. The hospital needs to hold a human resource training especially a nurse-patient therapeutic communication training for maintaining the service quality at the hospital.Keywords: hospitalization, patient, responsiveness, satisfaction

An Investigation of the Effect of Latency on the Operator’s Trust and Performance for Manual Multi-robot Teleoperated Tasks

2017 ◽  
Vol 61 (1) ◽  
pp. 390-394 ◽  
Author(s):  
Hunter Rogers ◽  
Amro Khasawneh ◽  
Jeffery Bertrand ◽  
Kapil Chalil Madathil
Keyword(s):  
Confidence Level ◽  
Search And Rescue ◽  
The Other ◽  
Repeated Measure ◽  
Independent Variables ◽  
Subjective Workload ◽  
Dependent Variables ◽  
Independent Variable ◽  
And Performance ◽  
Two Independent Variables

Latency is an important factor when conducting teleoperated missions. This study investigates the effects of latency on a set of dependent variables: performance (measured by time and number of errors), subjective workload, trust, and usability. These measures were tested in a simulated search-and-rescue mission over two levels of two independent variables. One independent variable was the number of robots – one or two (within-subject), and the other independent variable was latency – simulations with and without latency (between-subject.) The significant effect of the independent variables on the dependent variables were checked using repeated measure two-way ANOVA with a confidence level of 95%. The data determined any significant effects that latency and/or the number of robots had on such factors as errors, dependability, reliability, harmful outcomes, temporal demand, and frustration.

scholarly journals PENGARUH KECERDASAN EMOSI, LINGKUNGAN SEKOLAH TERHADAP MOTIVASI BELAJAR DAN DAMPAKNYA TERHADAP PRESTASI BELAJAR SISWA

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Fransiscus Amonio Halawa ◽  
Fabianus Fensi
Keyword(s):  
Emotional Intelligence ◽  
School Environment ◽  
Quantitative Research ◽  
Survey Methods ◽  
Learning Motivation ◽  
Learning Achievement ◽  
Independent Variables ◽  
Is Research ◽  
Dependent Variables ◽  
Independent Variable

<p align="justify"><em>This research is to analyze the effect of emotional intelligence, school environment on learning motivation and its impact on student achievement. The research methodology used is quantitative research using survey methods that are intended to provide an explanation. Survey research is research that takes a sample from a population and uses a questionnaire as a primary collection tool. There are 3 variables used, they are independent variable, mediation variable and dependent variable. Emotional intelligence and school environment as independent variables, learning motivation as mediating variables and learning achievement as dependent variables. The study involved as many as 89 students as respondents, the data were analyzed with SmartPLS 3.0. The results showed that emotional intelligence had a positive and significant effect on learning motivation, emotional intelligence had a positive and significant effect on learning achievement, the school environment had a positive and significant effect on learning motivation, the school environment had a positive and significant effect on learning achievement and learning motivation had a positive and significant effect towards learning achievement.</em><em></em></p><p align="justify"><em> </em></p><p align="justify"> </p>

scholarly journals Conditional Maximum Likelihood Estimation in Probability-Branched Multistage Designs

2021 ◽  
Author(s):  
Jan Steinfeld ◽  
Alexander Robitzsch
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Parameter Estimate ◽  
Item Parameter ◽  
Parameter Estimates ◽  
Conditional Maximum Likelihood ◽  
Item Parameter Estimates ◽  
Item Parameter Estimation ◽  
Conditional Maximum

This article describes the conditional maximum likelihood-based item parameter estimation in probabilistic multistage designs. In probabilistic multistage designs, the routing is not solely based on a raw score j and a cut score c as well as a rule for routing into a module such as j &lt; c or j ≤ c but is based on a probability p(j) for each raw score j. It can be shown that the use of a conventional conditional maximum likelihood parameter estimate in multistage designs leads to severely biased item parameter estimates. Zwitser and Maris (2013) were able to show that with deterministic routing, the integration of the design into the item parameter estimation leads to unbiased estimates. This article extends this approach to probabilistic routing and, at the same time, represents a generalization. In a simulation study, it is shown that the item parameter estimation in probabilistic designs leads to unbiased item parameter estimates.

scholarly journals PEMODELAN TINGKAT KERAWANAN DEMAM BERDARAH DI KABUPATEN BANJAR DENGAN METODE ANALISIS REGRESI LOGISTIK YANG TERBOBOTI GEOGRAFIS

2019 ◽  
Vol 14 (4) ◽  
pp. 2393
Author(s):  
Dewi Sri Susanti ◽  
Pamona Dwi Rahayu ◽  
Oni Soesanto
Keyword(s):  
Logistic Regression ◽  
Dengue Fever ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Rate Model ◽  
Independent Variables ◽  
Dependent Variables ◽  
Geographically Weighted Logistic Regression ◽  
The Relationship ◽  
Maximum Likelihood Estimation Method

Regression analysis is a metodh for investigating the relationship between the dependent variable (Y) and independent variables (X). Logistic regression is a regression model that used related to the qualitative Dependent variable. If the Logistic regression influenced by factors of the location of each point from observation where the data is collected, it will be a Geographically Weighted Logistic Regression (GWLR). In the case of insecurity rate model of dengue fever has two or more categories, so that this case can be resolved by GWLR. This research aims to clarify the procedure of testing the parameters GWLR model and form insecurity rate model of dengue fever with GWLR method in Banjar Regency. Dependent variable with catagoric is Insecurity rate of dengue fever ( ) and independent variables is the population density ( ), the distance from the capital of the subdistrict to capital of regency ( ), fogging per subdistrict ( ), the percentage of households living clean and healthy ( ), pesentase healthy homes ( ), the percentage of access to decent sanitation ( ). The results from this research are estimate parameters using Maximum Likelihood Estimation method and presented in the form of thematic map that shows not all dependent variables give influence on Insecurity rate dengue fever

scholarly journals Analysis of Relationship Between Personality and Favorite Places with Poisson Regression Analysis

2018 ◽  
Vol 16 ◽  
pp. 02001 ◽  
Author(s):  
Ha Yoon Song ◽  
Hwa Baek Kang
Keyword(s):  
Regression Analysis ◽  
Poisson Regression ◽  
Human Mobility ◽  
High Tendency ◽  
Independent Variables ◽  
Location Data ◽  
Dependent Variables ◽  
Independent Variable ◽  
The Relationship ◽  
University Laboratory

A relationship between human personality and preferred locations have been a long conjecture for human mobility research. In this paper, we analyzed the relationship between personality and visiting place with Poisson Regression. Poisson Regression can analyze correlation between countable dependent variable and independent variable. For this analysis, 33 volunteers provided their personality data and 49 location categories data are used. Raw location data is preprocessed to be normalized into rates of visit and outlier data is prunned. For the regression analysis, independent variables are personality data and dependent variables are preprocessed location data. Several meaningful results are found. For example, persons with high tendency of frequent visiting to university laboratory has personality with high conscientiousness and low openness. As well, other meaningful location categories are presented in this paper.

scholarly journals Impact of Humor Advertising on Brand Purchase Strategy

2019 ◽  
Vol 12 (1) ◽  
pp. 60-70
Author(s):  
Amin Palikhe
Keyword(s):  
Data Analysis ◽  
Purchase Intention ◽  
Brand Attitude ◽  
Test Results ◽  
Huge Amount ◽  
Independent Variables ◽  
Descriptive Research ◽  
Dependent Variables ◽  
Independent Variable ◽  
The Impact

   The humor advertisement is important for every types of marketer. The main aim of the study is to analyze the impact of humor advertisement on the brand purchasing strategy of consumers. This study used descriptive research design by testing the hypothesis with dependent and independent variables. The questionnaire based survey has been undertaken upon the sample of 136 respondents. Furthermore, data analysis has been carried forward with the help of SPSS through regression and correlation. The results reveal that there is no significant relationship exists between the independent variable (humor advertisement) and the dependent variables (brand attitude, brand memories, purchase intention). There is low correlation between humor advertisement and brand attitude that shows p<0.1. Industries have been spending huge amount of money on humor advertisement but the study has also revealed that there is no significant changes in brand purchase strategy of consumer by appealing humor advertising. Test results of correlation and regression shows that humor advertisement can’t make brand purchase strategy. Therefore study of consumer behavior is important to create brand purchase strategy and spending nature of consumer towards advertised products.

scholarly journals Use and Efficacy of the Lob to Achieve the Offensive Position in Women´s Professional Padel. Analysis of the 2018 WPT Finals

2020 ◽  
Vol 17 (11) ◽  
pp. 4061 ◽  
Author(s):  
Adrián Escudero-Tena ◽  
José Fernández-Cortes ◽  
Javier García-Rubio ◽  
Sergio J. Ibáñez
Keyword(s):  
Statistical Analysis ◽  
Observational Study ◽  
Quantitative Methodology ◽  
Tennis Players ◽  
Independent Variables ◽  
Significant Relationships ◽  
Dependent Variables ◽  
Independent Variable

Studies that analyze the actions carried out by paddle tennis players during the point are scarce. The present investigation characterizes every action in which a stroke by a pair in a defensive position sends the ball over the position of a pair in an offensive position. It is a descriptive and observational study of quantitative methodology. The sample consisted of 1324 actions, statistical analysis units, from the women’s circuit in the 2018 World Padel Tour (WPT) season. For this study, various situational, dependent, and independent variables were analyzed. The results showed the number of times the categories of each variable occurred, as well as the significant relationships between the independent variable kind of hit and the dependent variables “actions that facilitate the possible change of position” (AFPCP) and “incidences in the game” (IG). The conclusion is that the lob is the most effective kind of hit (CSR = 4.9) to achieve the offensive position (CSR = 11.4), even if the point does not finish (CSR = 5.8), leading to more position exchanges during the same point in the AFPCP. These findings are of great interest since they give information about how and why certain behaviors produce a certain result.

To Lag or Not to Lag?: Re-Evaluating the Use of Lagged Dependent Variables in Regression Analysis

2017 ◽  
Vol 6 (2) ◽  
pp. 393-411 ◽  
Author(s):  
Arjun S. Wilkins
Keyword(s):  
Regression Analysis ◽  
Parameter Estimates ◽  
Coefficient Estimates ◽  
Estimation Strategy ◽  
Independent Variables ◽  
Robust Estimates ◽  
Wide Range ◽  
Dependent Variables ◽  
Data Generating Process ◽  
Parameter Values

Lagged dependent variables (LDVs) have been used in regression analysis to provide robust estimates of the effects of independent variables, but some research argues that using LDVs in regressions produces negatively biased coefficient estimates, even if the LDV is part of the data-generating process. I demonstrate that these concerns are easily resolved by specifying a regression model that accounts for autocorrelation in the error term. This actually implies that more LDV and lagged independent variables should be included in the specification, not fewer. Including the additional lags yields more accurate parameter estimates, which I demonstrate using the same data-generating process scholars had previously used to argue against including LDVs. I use Monte Carlo simulations to show that this specification returns much more accurate coefficient estimates for independent variables (across a wide range of parameter values) than alternatives considered in earlier research. The simulation results also indicate that improper exclusion of LDVs can lead to severe bias in coefficient estimates. While no panacea, scholars should continue to confidently include LDVs as part of a robust estimation strategy.

Sign in / Sign up

Export Citation Format

Share Document