Jocelyn Bolin | Ball State University (original) (raw)
Papers by Jocelyn Bolin
International Journal of Trend in Scientific Research and Development, Jun 30, 2019
As the decreasing availability of the fossil fuel is rising day by day, the search for alternate ... more As the decreasing availability of the fossil fuel is rising day by day, the search for alternate fuel that can be used as a substitute to the conventional fuels is rising rapidly. A new type of biofuel, Neem oil biodiesel, is introduced in this work for the purpose of fuelling diesel engine. Neem oil was extracted from neem seed by solvent extraction method and biodiesel was produced by transesterification method. The percentage yield of Neem oil and biodiesel were found to be 40% and 75% respectively. The properties were simulated in a model produced using GT power suite. The engine speed was varied and engine performance such as brake power, brake specific fuel consumption, brake mean effective pressure and the emission of biodiesel and petroleum diesel at various speed were determined and compared. The results show the improve performance of biodiesel. The performance characteristics of an engine were studied with biodiesel and petrodiesel. The brake power 31.25 kW, brake torque 102.8 N-mare found higher at 3600 rpm (case 1) and 1200 rpm (case 4) respectively. In biodiesel, specific fuel consumption is found more than the petro-diesel and the CO and CO2 emission were found lower in biodiesel than petro-diesel. The biodiesel have shown better performance than the petro-diesel.
International Journal of Sports Science and Coaching, 2015
Journal of Educational Measurement, 2014
Journal of Sport Rehabilitation, 2015
Athletic identity has been associated with rehabilitation overadherence in college athletes. To e... more Athletic identity has been associated with rehabilitation overadherence in college athletes. To explore which constructs of athletic identity predict rehabilitation overadherence, gauge athletes' views of the most salient aspect of their athletic participation, and understand their perceptions of the reasons they adhere to their rehabilitation program. Cross-sectional, mixed-methods. University athletic training clinics and online. Currently injured college athletes (n = 80; 51 male, 29 female). Athletic Identity Measurement Scale (AIMS) and Rehabilitation Overadherence Questionnaire (ROAQ). Two open-ended questions were also asked about athletic participation and rehabilitation adherence. Higher levels of athletic identity were associated with higher levels of rehabilitation overadherence (r = .29, p = . 009). Hierarchical multiple regression used on AIMS subscales to predict ROAQ subscales did not reveal a significant model for the subscale Ignore Practitioner Recommendations. However, a significant model was revealed for the subscale Attempt an Expedited Rehabilitation, F(5, 73) = 2.56, p = .04, R(2) = .15. Negative affectivity was the only significant contribution to the equation (β = .33, t = 2.64, p = .01). Content analysis revealed that bodily benefits, sport participation, personal achievement, social relationships, and athlete status were perceived to be the most important aspects of being an athlete. The themes of returning to competition, general health, and relationship beliefs were identified as the major factors for adhering to a rehabilitation program. We found that negative affectivity accounted for a significant, but low amount of variance for rehabilitation overadherence, suggesting that athletic trainers should pay attention to personal variables such as athletic identity that might influence the rehabilitation process. Using the knowledge of why athletes adhere to their rehabilitation and what is most important to them about being an athlete, athletic trainers can use appropriate interventions to facilitate proper rehabilitation adherence.
Journal of Counseling & Development, 2015
The objective of this exploratory study was to describe changes in body composition and resting e... more The objective of this exploratory study was to describe changes in body composition and resting energy expenditure (REE) in adult women during weight reduction. A total of 69 client records were collected retrospectively from a 25 week commercial weight loss program that restricted calories to 90% of measured REE. Data analyzed included total body mass (TBM), fat mass (FM) and fat-free mass (FFM) from air displacement plethysmography; measured REE from indirect calorimetry; and predicted REE from linear regression. From baseline to week 25, there were significant declines in TBM (95.0 ± 24.1 kg to 87.2 ± 22.9 kg; P < 0.001) and FM (47.5 ± 18.5 kg to 39.9 ± 17.6 kg; P < 0.001). During the same time period, FFM remained unchanged (47.5 ± 7.3 kg to 47.2 ± 7.0 kg; P ≥ 0.05). REE was signify-cantly lower at weeks 13 (6595.2 ± 1312.1 kJ) and 25 (6608.2 ± 1404.6 kJ) compared to baseline (7117.4 ± 1471.5 kJ) (P < 0.001); however, REE at weeks 13 and 25 were similar (P ≥ 0.05). At w...
Handbook of Quantitative Methods for Educational Research, 2013
International Journal of Sustainability in Higher Education, 2015
The Journal of Perinatal & Neonatal Nursing, 2013
The purpose was to describe sources of infant formula samples during the perinatal period and ass... more The purpose was to describe sources of infant formula samples during the perinatal period and assess their associations with breast-feeding outcomes at 1 month postpartum. Subjects included expectant mothers who anticipated breast-feeding at least 1 month. Infant feeding history and sources of formula samples were obtained at 1 month postpartum. Associations between sources and breast-feeding outcomes were assessed using partial correlation. Of the 61 subjects who initiated breast-feeding, most were white (87%), married (75%), college-educated (75%), and planned exclusive breast-feeding (82%). Forty-two subjects (69%) continued breast-feeding at 1 month postpartum. Subjects received formula samples from the hospital (n = 40; 66%), physician&amp;amp;amp;#39;s office (n = 10; 16%), and mail (n = 41; 67%). There were no significant correlations between formula samples from the hospital, physician&amp;amp;amp;#39;s office, and/or mail and any or exclusive breast-feeding at 1 month (P &amp;amp;amp;gt; .05). In addition to the hospital, a long-standing source of formula samples, mail was also frequently reported as a route for distribution. The lack of statistically significant associations between formula samples and any or exclusive breast-feeding at 1 month may be related to small sample size and unique characteristics of the group studied.
Frontiers in psychology, 2014
Classification using standard statistical methods such as linear discriminant analysis (LDA) or l... more Classification using standard statistical methods such as linear discriminant analysis (LDA) or logistic regression (LR) presume knowledge of group membership prior to the development of an algorithm for prediction. However, in many real world applications members of the same nominal group, might in fact come from different subpopulations on the underlying construct. For example, individuals diagnosed with depression will not all have the same levels of this disorder, though for the purposes of LDA or LR they will be treated in the same manner. The goal of this simulation study was to examine the performance of several methods for group classification in the case where within group membership was not homogeneous. For example, suppose there are 3 known groups but within each group two unknown classes. Several approaches were compared, including LDA, LR, classification and regression trees (CART), generalized additive models (GAM), and mixture discriminant analysis (MIXDA). Results of...
Frontiers in Psychology, 2014
Statistical classification of phenomena into observed groups is very common in the social and beh... more Statistical classification of phenomena into observed groups is very common in the social and behavioral sciences. Statistical classification methods, however, are affected by the characteristics of the data under study. Statistical classification can be further complicated by initial misclassification of the observed groups. The purpose of this study is to investigate the impact of initial training data misclassification on several statistical classification and data mining techniques. Misclassification conditions in the three group case will be simulated and results will be presented in terms of overall as well as subgroup classification accuracy. Results show decreased classification accuracy as sample size, group separation and group size ratio decrease and as misclassification percentage increases with random forests demonstrating the highest accuracy across conditions.
Educational and Psychological Measurement, 2011
The statistical classification of N individuals into G mutually exclusive groups when the actual ... more The statistical classification of N individuals into G mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis, quadratic discriminant analysis, and logistic regression. However, recent developments in the statistics literature have brought new and potentially more flexible classification models to the forefront. Although these new models are increasingly being used in the physical sciences and marketing research, they are still relatively little used in the social and behavioral sciences. The purpose of this article is to provide a comparison of these modern methods with the classical methods widely used in situations that are relevant in the social and behavioral sciences. This study uses a large-scale Monte Carlo simulation study for the comparisons, as analytic comparisons are often not tractable. Results indicate that classification and regression trees generally produced the highest classification accuracy of all techniques tested, though study design characteristics such as sample size and model complexity can greatly influence optimal choice or effectiveness of statistical classification method.
Educational and Psychological Measurement, 2010
Classification procedures are common and useful in behavioral, educational, social, and manageria... more Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture models (FMM) do not require, or even use if available, knowledge of group status to estimate parameters or classifying. This study investigates the impact of two types of misclassification errors on the classification accuracy of discriminant function analysis (both linear [LDA] and quadratic [QDA]) and FMM for two groups with a single predictor. Analytic and Monte Carlo results are provided for a variety of misclassification scenarios to investigate the performance of the two methods. Discriminant function techniques recovered the highest overall percentages of correctly classified data, whereas FMM captured higher percentages of the smaller group when group sizes are unequal. LDA marginally outperformed QDA under misclassified conditions.
Behavior Research Methods, 2011
Statistical prediction of an outcome variable using multiple independent variables is a common pr... more Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression treesprovided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.
Frontiers in Psychology, 2014
Although traditional clustering methods (e.g., K-means) have been shown to be useful in the socia... more Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.
International Journal of Trend in Scientific Research and Development, Jun 30, 2019
As the decreasing availability of the fossil fuel is rising day by day, the search for alternate ... more As the decreasing availability of the fossil fuel is rising day by day, the search for alternate fuel that can be used as a substitute to the conventional fuels is rising rapidly. A new type of biofuel, Neem oil biodiesel, is introduced in this work for the purpose of fuelling diesel engine. Neem oil was extracted from neem seed by solvent extraction method and biodiesel was produced by transesterification method. The percentage yield of Neem oil and biodiesel were found to be 40% and 75% respectively. The properties were simulated in a model produced using GT power suite. The engine speed was varied and engine performance such as brake power, brake specific fuel consumption, brake mean effective pressure and the emission of biodiesel and petroleum diesel at various speed were determined and compared. The results show the improve performance of biodiesel. The performance characteristics of an engine were studied with biodiesel and petrodiesel. The brake power 31.25 kW, brake torque 102.8 N-mare found higher at 3600 rpm (case 1) and 1200 rpm (case 4) respectively. In biodiesel, specific fuel consumption is found more than the petro-diesel and the CO and CO2 emission were found lower in biodiesel than petro-diesel. The biodiesel have shown better performance than the petro-diesel.
International Journal of Sports Science and Coaching, 2015
Journal of Educational Measurement, 2014
Journal of Sport Rehabilitation, 2015
Athletic identity has been associated with rehabilitation overadherence in college athletes. To e... more Athletic identity has been associated with rehabilitation overadherence in college athletes. To explore which constructs of athletic identity predict rehabilitation overadherence, gauge athletes' views of the most salient aspect of their athletic participation, and understand their perceptions of the reasons they adhere to their rehabilitation program. Cross-sectional, mixed-methods. University athletic training clinics and online. Currently injured college athletes (n = 80; 51 male, 29 female). Athletic Identity Measurement Scale (AIMS) and Rehabilitation Overadherence Questionnaire (ROAQ). Two open-ended questions were also asked about athletic participation and rehabilitation adherence. Higher levels of athletic identity were associated with higher levels of rehabilitation overadherence (r = .29, p = . 009). Hierarchical multiple regression used on AIMS subscales to predict ROAQ subscales did not reveal a significant model for the subscale Ignore Practitioner Recommendations. However, a significant model was revealed for the subscale Attempt an Expedited Rehabilitation, F(5, 73) = 2.56, p = .04, R(2) = .15. Negative affectivity was the only significant contribution to the equation (β = .33, t = 2.64, p = .01). Content analysis revealed that bodily benefits, sport participation, personal achievement, social relationships, and athlete status were perceived to be the most important aspects of being an athlete. The themes of returning to competition, general health, and relationship beliefs were identified as the major factors for adhering to a rehabilitation program. We found that negative affectivity accounted for a significant, but low amount of variance for rehabilitation overadherence, suggesting that athletic trainers should pay attention to personal variables such as athletic identity that might influence the rehabilitation process. Using the knowledge of why athletes adhere to their rehabilitation and what is most important to them about being an athlete, athletic trainers can use appropriate interventions to facilitate proper rehabilitation adherence.
Journal of Counseling & Development, 2015
The objective of this exploratory study was to describe changes in body composition and resting e... more The objective of this exploratory study was to describe changes in body composition and resting energy expenditure (REE) in adult women during weight reduction. A total of 69 client records were collected retrospectively from a 25 week commercial weight loss program that restricted calories to 90% of measured REE. Data analyzed included total body mass (TBM), fat mass (FM) and fat-free mass (FFM) from air displacement plethysmography; measured REE from indirect calorimetry; and predicted REE from linear regression. From baseline to week 25, there were significant declines in TBM (95.0 ± 24.1 kg to 87.2 ± 22.9 kg; P < 0.001) and FM (47.5 ± 18.5 kg to 39.9 ± 17.6 kg; P < 0.001). During the same time period, FFM remained unchanged (47.5 ± 7.3 kg to 47.2 ± 7.0 kg; P ≥ 0.05). REE was signify-cantly lower at weeks 13 (6595.2 ± 1312.1 kJ) and 25 (6608.2 ± 1404.6 kJ) compared to baseline (7117.4 ± 1471.5 kJ) (P < 0.001); however, REE at weeks 13 and 25 were similar (P ≥ 0.05). At w...
Handbook of Quantitative Methods for Educational Research, 2013
International Journal of Sustainability in Higher Education, 2015
The Journal of Perinatal & Neonatal Nursing, 2013
The purpose was to describe sources of infant formula samples during the perinatal period and ass... more The purpose was to describe sources of infant formula samples during the perinatal period and assess their associations with breast-feeding outcomes at 1 month postpartum. Subjects included expectant mothers who anticipated breast-feeding at least 1 month. Infant feeding history and sources of formula samples were obtained at 1 month postpartum. Associations between sources and breast-feeding outcomes were assessed using partial correlation. Of the 61 subjects who initiated breast-feeding, most were white (87%), married (75%), college-educated (75%), and planned exclusive breast-feeding (82%). Forty-two subjects (69%) continued breast-feeding at 1 month postpartum. Subjects received formula samples from the hospital (n = 40; 66%), physician&amp;amp;amp;#39;s office (n = 10; 16%), and mail (n = 41; 67%). There were no significant correlations between formula samples from the hospital, physician&amp;amp;amp;#39;s office, and/or mail and any or exclusive breast-feeding at 1 month (P &amp;amp;amp;gt; .05). In addition to the hospital, a long-standing source of formula samples, mail was also frequently reported as a route for distribution. The lack of statistically significant associations between formula samples and any or exclusive breast-feeding at 1 month may be related to small sample size and unique characteristics of the group studied.
Frontiers in psychology, 2014
Classification using standard statistical methods such as linear discriminant analysis (LDA) or l... more Classification using standard statistical methods such as linear discriminant analysis (LDA) or logistic regression (LR) presume knowledge of group membership prior to the development of an algorithm for prediction. However, in many real world applications members of the same nominal group, might in fact come from different subpopulations on the underlying construct. For example, individuals diagnosed with depression will not all have the same levels of this disorder, though for the purposes of LDA or LR they will be treated in the same manner. The goal of this simulation study was to examine the performance of several methods for group classification in the case where within group membership was not homogeneous. For example, suppose there are 3 known groups but within each group two unknown classes. Several approaches were compared, including LDA, LR, classification and regression trees (CART), generalized additive models (GAM), and mixture discriminant analysis (MIXDA). Results of...
Frontiers in Psychology, 2014
Statistical classification of phenomena into observed groups is very common in the social and beh... more Statistical classification of phenomena into observed groups is very common in the social and behavioral sciences. Statistical classification methods, however, are affected by the characteristics of the data under study. Statistical classification can be further complicated by initial misclassification of the observed groups. The purpose of this study is to investigate the impact of initial training data misclassification on several statistical classification and data mining techniques. Misclassification conditions in the three group case will be simulated and results will be presented in terms of overall as well as subgroup classification accuracy. Results show decreased classification accuracy as sample size, group separation and group size ratio decrease and as misclassification percentage increases with random forests demonstrating the highest accuracy across conditions.
Educational and Psychological Measurement, 2011
The statistical classification of N individuals into G mutually exclusive groups when the actual ... more The statistical classification of N individuals into G mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis, quadratic discriminant analysis, and logistic regression. However, recent developments in the statistics literature have brought new and potentially more flexible classification models to the forefront. Although these new models are increasingly being used in the physical sciences and marketing research, they are still relatively little used in the social and behavioral sciences. The purpose of this article is to provide a comparison of these modern methods with the classical methods widely used in situations that are relevant in the social and behavioral sciences. This study uses a large-scale Monte Carlo simulation study for the comparisons, as analytic comparisons are often not tractable. Results indicate that classification and regression trees generally produced the highest classification accuracy of all techniques tested, though study design characteristics such as sample size and model complexity can greatly influence optimal choice or effectiveness of statistical classification method.
Educational and Psychological Measurement, 2010
Classification procedures are common and useful in behavioral, educational, social, and manageria... more Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture models (FMM) do not require, or even use if available, knowledge of group status to estimate parameters or classifying. This study investigates the impact of two types of misclassification errors on the classification accuracy of discriminant function analysis (both linear [LDA] and quadratic [QDA]) and FMM for two groups with a single predictor. Analytic and Monte Carlo results are provided for a variety of misclassification scenarios to investigate the performance of the two methods. Discriminant function techniques recovered the highest overall percentages of correctly classified data, whereas FMM captured higher percentages of the smaller group when group sizes are unequal. LDA marginally outperformed QDA under misclassified conditions.
Behavior Research Methods, 2011
Statistical prediction of an outcome variable using multiple independent variables is a common pr... more Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression treesprovided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.
Frontiers in Psychology, 2014
Although traditional clustering methods (e.g., K-means) have been shown to be useful in the socia... more Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.