Relevant bibliographies by topics / Predictor factors

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Predictor factors'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Predictor factors"

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Spanos, Nicholas P., Joyce L. D'Eon, Anne E. Pawlak, Christopher D. Mah, and Gary Ritchie. "A Multivariate Study of Hypnotic Susceptibility." Imagination, Cognition and Personality 9, no. 1 (September 1989): 33–48. http://dx.doi.org/10.2190/9y7a-7hqm-2rne-v759.

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Twelve variables previously shown to predict hypnotic susceptibility were factor, analyzed. Six of them loaded on a common factor labeled “a positive set toward, imagining.” The items from two hypnotic susceptibility scales were also factor, analyzed, and fell into three factors (one “cognitive” and two motor factors)., Multiple regression analyses using the susceptibility scales and also the three, susceptibility factors as criterion variables indicated that most of the predicted, variance was accounted for by the predictor variables that loaded on the, “imaginative set” factor. Many of the predictor variables did not contribute, significantly to the prediction of the susceptibility measures. Moreover, a number of, predictors, that purportedly assess similar processes, failed to intercorrelate, significantly. Methodological and theoretical implications of these findings are, discussed.
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TOZATTI, Joana, André Luiz Parizi MELLO, and Orli FRAZON. "Predictor factors for choledocholithiasis." ABCD. Arquivos Brasileiros de Cirurgia Digestiva (São Paulo) 28, no. 2 (June 2015): 109–12. http://dx.doi.org/10.1590/s0102-67202015000200006.

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BACKGROUND: The choledocolithiasis has an incidence of 8-20% in patients with cholecystolithiasis. The preoperative diagnosis guides the interventional treatment on the bile duct AIM: To evaluate the sensitivity and specificity of the laboratory markers and imaging studies for choledocholithiasis preoperatively. METHODS: The study comprised 254 patients divided into two groups: the control group (207 patients), patients without choledocholithiasis intraoperatively and cases group (47 patients), that enrolled the patients with choledocholithiasis intra-operatively. Were evaluated the laboratory markers, image exams and intra-operative diagnostic aspects. RESULTS: The sample was homogeneous for age and gender. It was observed that 47% of the cases the patients did not show comorbidities. Hospitalization showes in cases group acute pancreatitis in12.8%, jaundice in 30%, fever in 30% and pain in the right hypochondrium in 95%. By comparing them, was observed that fever and jaundice were the signs and symptoms with statistical significance. Patients with choledocholithiasis had transaminases, alkaline phosphatase, gamma-glutamyl transferase and higher bilirubin with statistical significance (p<0.001). In regard to imaging studies, ultrasound was fairly accurate for cholelithiasis and choledocholithiasis (p<0.001). CONCLUSION: Changes in canalicular and transaminase enzymes are suggestive for preoperative choledocholithiasis; GGT showed better sensitivity and alkaline phosphatase greater specificity; ultrasonography and nuclear magnetic resonance cholangiopancreatography showed high specificity.
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Diedrich, Jennifer, Aljoscha C. Neubauer, and Anna Ortner. "The Prediction of Professional Success in Apprenticeship: The Role of Cognitive and Non-Cognitive Abilities, of Interests and Personality." International Journal for Research in Vocational Education and Training 5, no. 2 (August 30, 2018): 82–110. http://dx.doi.org/10.13152/ijrvet.5.2.1.

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Context: We addressed the issue of person-job-fit by focussing on both professional success and work satisfaction. Publications studying the predictive validity of (cognitive) ability, personality, or vocational interest alone have shown relationships with professional success or work satisfaction for each predictor separately. Nevertheless, these predictors have rarely been studied simultaneously. Methods: To this end we tested the incremental validity of abilities, traits, and interests in a sample from diverse occupations: In 648 apprentices and students from five different branches (Food, Tech, People, Office, Craft) the (incremental) contributions of 3 intelligence factors (verbal, numerical, spatial), 3 alternative abilities (social-emotional, creative, practical), 4 conscientiousness facets, other big five factors (O, E, A, N), and of 14 professional interests were analysed regarding prediction of GPA in professional schools and school/job satisfaction. Results: Intelligence and conscientiousness were best predictors, followed by social-emotional competence and interests, whereas other traits provided marginal contributions. Predictors varied between branches, mostly following expectations. The test battery allowed a very good prediction of apprenticeship success (max. 37%), but for some branches prediction was considerably lower.Conclusion: Criteria for person-job-fit are not swappable, neither are the predictors. Professional success was mostly predicted by a different predictor set -namely ability and the personality dimension of conscientiousness- then satisfaction, which was mostly predicted by non-interest in a certain occupation. As a practical implication, we conclude that choosing the right candidate for a certain branch one needs to use a broad set of predictor variables. Besides cognitive ability also personality and vocational interests had predictive validity for an individuals person-job-fit.
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De Man, Anton, and Paul Simpson-Housley. "Factors in Perception of Earthquake Hazard." Perceptual and Motor Skills 64, no. 3 (June 1987): 815–20. http://dx.doi.org/10.2466/pms.1987.64.3.815.

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130 subjects participated in a study of the relationship between selected predictors and responses to potential earthquake hazard. The results of backstep regression analyses indicated (a) that amount of education was the best predictor from those selected of perceived probability of earthquake occurrence, (b) that estimation of potential damage was related to number of damage reduction measures, perceived reliability of official support systems, and expectation of earthquake, and (c) that trait-anxiety and expectation of earthquake accounted for a significant percentage of the variance in acknowledged anxiety in response to prediction of an earthquake.
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McIntosh, Kent, Sterett H. Mercer, Rhonda N. T. Nese, M. Kathleen Strickland-Cohen, Angus Kittelman, Robert Hoselton, and Robert H. Horner. "Factors Predicting Sustained Implementation of a Universal Behavior Support Framework." Educational Researcher 47, no. 5 (May 16, 2018): 307–16. http://dx.doi.org/10.3102/0013189x18776975.

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In this 3-year prospective study, we tested the extent to which school-, practice-, and district-level variables predicted sustained implementation for schools in various stages of implementation of school-wide positive behavioral interventions and supports (SWPBIS) Tier 1 (universal) systems. Staff from 860 schools in 14 U.S. states completed a research-validated measure of factors associated with sustained implementation of school interventions during Year 1 of this study. Analyses included multigroup structural equation modeling of school and district implementation fidelity data. Results indicated that adequate implementation fidelity and better Team Use of Data for decision making in Study Year 1 were the strongest predictors of sustained implementation in Year 3. In addition, the number of other schools in the district adopting SWPBIS was a similarly strong predictor. A critical mass of schools implementing was also predictive, especially for schools earlier in implementation. School characteristics were not predictive, except for grade levels served, which was an inconsistent predictor by stage.
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Ren, Yan, Shiyao Huang, Qianrui Li, Chunrong Liu, Ling Li, Jing Tan, Kang Zou, and Xin Sun. "Prognostic factors and prediction models for acute aortic dissection: a systematic review." BMJ Open 11, no. 2 (February 2021): e042435. http://dx.doi.org/10.1136/bmjopen-2020-042435.

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ObjectiveOur study aimed to systematically review the methodological characteristics of studies that identified prognostic factors or developed or validated models for predicting mortalities among patients with acute aortic dissection (AAD), which would inform future work.Design/settingA methodological review of published studies.MethodsWe searched PubMed and EMBASE from inception to June 2020 for studies about prognostic factors or prediction models on mortality among patients with AAD. Two reviewers independently collected the information about methodological characteristics. We also documented the information about the performance of the prognostic factors or prediction models.ResultsThirty-two studies were included, of which 18 evaluated the performance of prognostic factors, and 14 developed or validated prediction models. Of the 32 studies, 23 (72%) were single-centre studies, 22 (69%) used data from electronic medical records, 19 (59%) chose retrospective cohort study design, 26 (81%) did not report missing predictor data and 5 (16%) that reported missing predictor data used complete-case analysis. Among the 14 prediction model studies, only 3 (21%) had the event per variable over 20, and only 5 (36%) reported both discrimination and calibration statistics. Among model development studies, 3 (27%) did not report statistical methods, 3 (27%) exclusively used statistical significance threshold for selecting predictors and 7 (64%) did not report the methods for handling continuous predictors. Most prediction models were considered at high risk of bias. The performance of prognostic factors showed varying discrimination (AUC 0.58 to 0.95), and the performance of prediction models also varied substantially (AUC 0.49 to 0.91). Only six studies reported calibration statistic.ConclusionsThe methods used for prognostic studies on mortality among patients with AAD—including prediction models or prognostic factor studies—were suboptimal, and the model performance highly varied. Substantial efforts are warranted to improve the use of the methods in this population.
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Klentrou, Panagiota, Izabella A. Ludwa, and Bareket Falk. "Factors associated with bone turnover and speed of sound in early and late-pubertal females." Applied Physiology, Nutrition, and Metabolism 36, no. 5 (October 2011): 707–14. http://dx.doi.org/10.1139/h11-085.

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This cross-sectional study examines whether maturity, body composition, physical activity, dietary intake, and hormonal concentrations are related to markers of bone turnover and tibial speed of sound (tSOS) in premenarcheal (n = 20, 10.1 ± 1.1 years) and postmenarcheal girls (n = 28, aged 15.0 ± 1.4 years). Somatic maturity was evaluated using years from age of peak height velocity (aPHV). Daily dietary intake was assessed with a 24-h recall interview, and moderate to very vigorous physical activity (MVPA) was measured using accelerometry. Plasma levels of 25-OH vitamin D, serum levels of insulin-like growth-factor 1 (IGF-1) and leptin, and serum levels of bone turnover markers including osteocalcin (OC), bone-specific alkaline phosphatase (BAP) and cross-linked N-teleopeptide of type I collagen (NTX) were measured using ELISA. OC, BAP, and NTX were significantly higher while IGF-1 and tSOS were lower in the premenarcheal group. The premenarcheal girls were more active and had higher daily energy intake relative to their body mass but there were no group differences in body mass index percentile. Maturity predicted 40%–57% of the variance in bone turnover markers. Additionally, daily energy intake was a significant predictor of OC, especially in the postmenarcheal group. IGF-1 and MVPA were significant predictors of BAP in the group as a whole. However, examined separately, IGF-1 was a predictor of BAP in the premenarcheal group while MVPA was a predictor in the postmenarcheal group. Adiposity and leptin were both negative predictors of tSOS, with leptin being specifically predictive in the postmenarcheal group. In conclusion, while maturity was the strongest predictor of bone markers and tSOS, dietary intake, physical activity, body composition, and hormonal factors further contribute to the variance in bone turnover and bone SOS in young Caucasian females. Further, the predicting factors of bone turnover and tSOS were different within each maturity group.
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Blazel, Madeleine M., Karen K. Lazar, Carol A. Van Hulle, Yue Ma, Aleshia Cole, Alice Spalitta, Nancy Davenport-Sis, et al. "Factors Associated with Lumbar Puncture Participation in Alzheimer’s Disease Research." Journal of Alzheimer's Disease 77, no. 4 (October 13, 2020): 1559–67. http://dx.doi.org/10.3233/jad-200394.

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Background: Cerebrospinal fluid (CSF) provides insight into the spectrum of Alzheimer’s disease (AD) pathology. While lumbar punctures (LPs) for CSF collection are generally considered safe procedures, many participants remain hesitant to participate in research involving LPs. Objective: To explore factors associated with participant willingness to undergo a research LP at baseline and follow-up research study visit. Methods: We analyzed data from 700 participants with varying cognition (unimpaired, mild cognitive impairment, and dementia) in the Wisconsin Alzheimer’s Disease Research Center. We evaluated the relationship of demographic variables (age, sex, race, ethnicity, and years of education) and clinical variables (waist-to-hip ratio, body mass index, AD parental history, cognitive diagnosis) on decision to undergo baseline LP1. We evaluated the relationship of prior LP1 experience (procedure success and adverse events) with the decision to undergo follow-up LP2. The strongest predictors were incorporated into regression models. Results: Over half of eligible participants opted into both baseline and follow-up LP. Participants who underwent LP1 had higher mean education than those who declined (p = 0.020). White participants were more likely to choose to undergo LP1 (p < 0.001); 33% of African American participants opted in compared to 65% of white participants. Controlling for age, education, and AD parental history, race was the only significant predictor for LP1 participation. Controlling for LP1 mild adverse events, successful LP1 predicted LP2 participation. Conclusion: Race was the most important predictor of baseline LP participation, and successful prior LP was the most important predictor of follow-up LP participation.
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de Man, Anton F. "Familial Factors and Relative Weight in Children." Psychology and Human Development: an international journal 2, no. 1 (March 1, 1988): 27–33. http://dx.doi.org/10.2224/sbp.6422.

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Ninety-five French-Canadian children and their parents took part in this study of the relationship between selected familial variables and children's relative weight. Results of a backstep regression analysis showed that socioeconomic status was the best single predictor for girls, whereas maternal rejection/hostility, duration of breast-feeding, and socioeconomic status were significant predictors for boys.
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Daley, Tamara C., Shannon E. Whaley, Marian D. Sigman, Donald Guthrie, Charlotte G. Neumann, and Nimrod Bwibo. "Background and classroom correlates of child achievement, cognitive, and behavioural outcomes in rural Kenyan schoolchildren." International Journal of Behavioral Development 29, no. 5 (September 2005): 399–408. http://dx.doi.org/10.1177/01650250500172780.

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In the current study, background data (sex, age, and SES) and classroom factors were examined as predictors of scholastic achievement and child cognitive and behavioural outcomes in a group of rural Kenyan schoolchildren during their first year of formal schooling. Previous research in this area has provided mixed results regarding the characteristics of children and school environments that best predict optimal outcomes for children. This study extended previous research through the use of multiple culturally grounded predictor and outcome variables; in addition to using observational techniques to assess the classroom environment, this study examined cognitive, academic, and behavioural measures. Results suggested that while background factors such as child age and SES are important predictors of child outcomes, inclusion of classroom factors significantly improved prediction for all types of child outcomes, and the addition of behaviour as a predictor shows an even greater effect. The largest effect was seen for the outcome variables most closely tied to classroom activities.
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Dissertations / Theses on the topic "Predictor factors"

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Speakman, Jennifer J. "Psychological and Behavioral Predictor of Adolescent Substance Use." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1249860380.

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Lazar, Kathryn A. "Current life engagement factors as a predictor of elder life satisfaction." Online version, 2000. http://www.uwstout.edu/lib/thesis/2000/2000lazark.pdf.

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Rippon, Wendy Leigh. "Age as a Predictor of Factors Involved in Targeted School Violence." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/3467.

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Targeted school violence (TSV) in the United States is increasing, causing a loss of innocent lives and challenges for teachers and students in building rapport. In addition, TSV increases levels of anxiety and makes it difficult for parents and community members to believe students are safe while at school. Several studies have highlighted the fact that age may be a factor in school shootings, calling for future research to determine if age is indeed influential. The problem is to date age has not been established as a predictive factor, even though the extant research is beginning to identify possible variances. Guided by general strain theory and ceremonial violence, this study determined statistical significance between age and select variables in the personal, event, and ecological categories. This information could be illuminating to educators, mental health professionals, and law enforcement for threat assessment purposes. The information was gathered on all TSV members within the United States from 1966 to 2015 through archival data, and the data were analyzed using logistic regression, Pearson's correlation, and Spearman's correlation. Results indicated that, as age increases, the offenders are more likely to have a higher social status, have a mental health and criminal history, carry out their act in the afternoon, and choose a knife as a weapon. In addition, older offenders are less likely to be students and less likely to have been bullied. Implications for social change include modifications to current threat assessment protocol regarding weapon choice and previous mental health or criminal history, which could be utilized to change public policy for mandatory reporting of students identified as at risk. Also, younger offenders are being bullied more often than older offenders and this could add more awareness to antibullying program procedure and earlier mental health intervention.
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Kitzman, Heather E. "Family factors as a predictor of weight change in obese adolescent females." Ann Arbor, Mich. : ProQuest, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1430296.

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Thesis (M.A. in Psychology)--S.M.U.
Title from PDF title page (viewed July 9, 2007). Source: Masters Abstracts International, Volume: 44-03, page: 1509. Adviser: Robert Hampson. Includes bibliographical references.
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Bishop, Keith Allan. "Predictor Variables Related To Falls In A Long-Term Care Environment." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/9717.

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Although a great deal is known about the etiology of falls in elderly individuals, fall accidents continue to represent a significant burden to elders residing in long-term care facilities. It has been stated that 75% of deaths due to falls in the United States occur in the 13% of the population age 65 and over. The first objective of the study was to identify which fall-predictor variables acknowledged in the research literature are associated with increased fall frequency with the older population. Identifying specific predictor variables related to a high occurrence of falls in long-term care setting can assist in the redesign of tools and programs aimed to recognize fall risk, and prevent fall-related accidents and fatalities in the geriatric population. The second objective of the study was to identify which combination of predictor variables could better predict the frequency of falls. A history of falls variable was the only predictive variable that differed significantly between groups of residents who had sustained subsequent falls and those who had not. Other variables including age, mental status, day number of stay, elimination, visual impairment, confinement, blood pressure drop, gait and balance, and medication were found to not be statistically significant between groups of fallers and non-fallers. In this setting, the current design of the tool had limited accuracy and exhibited an inability to effectively discriminate between resident populations at risk of falling and those not at risk of falling. Consequently, the current fall risk assessment tool is not adequate for assessing fall risk in this clinical setting.
Master of Science
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Weigand, Daniel A. (Daniel Arthur). "Validity of the Health Belief Model as a Predictor of Activity in Younger and Older Adults." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc500472/.

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The present investigation assessed Health Belief Model (HBM) variables and a measure of physical activity for both younger and older adults. Results of discriminant analyses suggest HBM variables and physical activity can predict age-group membership with 89% accuracy. The younger sample (n = 88; M= 21.5 years) was significantly more anxious about aging, perceived more barriers to exercise, less control from powerful others, and more social support than the older sample (n = 56; M = 71.8 years). For the younger sample, those who perceived more benefits of exercise, had social support, were male, and were less anxious about aging were more active. For the older sample, those who perceived more benefits of exercise were more likely to be active.
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Zaleski, Stephanie A. "Factors Predicting Weight Loss in Females After Gastric Bypass Surgery." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1289946728.

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Healey, Amanda Christel. "Father Absence and Early Family Composition as a Predictor of Menarcheal Onset: Psychosocial and Familial Factors That are Associated with Pubertal Timing." Digital Commons @ East Tennessee State University, 2006. https://dc.etsu.edu/etd/2172.

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Father absence and the introduction of a stepfather before menarche have been shown to contribute to the early onset of menarche. The present study analyzes the effects of father absence situations that tend to result on the onset of menarche. Presence of a related male in a father-absent homes is also considered as a protective factor for menarcheal onset. Participants consisted of 342 female students enrolled in undergraduate work at a southeastern university. The mean age of participants was 20.7 years. Participants completed a survey consisting of 12 questions pertaining to their family environment before menarche. Participants were asked to give their age at first menarche in years and months. Results indicted a significant difference in menarcheal age between those from homes where both biological parents were present and those where the biological father was absent before menarche. No other significant results were found. Implications for future research discussed.
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Froehner, Michael, Stefan Propping, Rainer Koch, Manfred P. Wirth, Angelika Borkowetz, Dorothea Liebeheim, Marieta Toma, and Gustavo B. Baretton. "Is the Post-Radical Prostatectomy Gleason Score a Valid Predictor of Mortality after Neoadjuvant Hormonal Treatment?" Karger, 2016. https://tud.qucosa.de/id/qucosa%3A70594.

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Purpose: To evaluate the validity of the Gleason score after neoadjuvant hormonal treatment as predictor of diseasespecific mortality after radical prostatectomy. Patients and Methods: A total of 2,880 patients with a complete data set and a mean follow-up of 10.3 years were studied; 425 of them (15%) had a history of hormonal treatment prior to surgery. The cumulative incidence of deaths from prostate cancer was determined by univariate and multivariate competing risk analysis. Cox proportional hazard models for competing risks were used to study combined effects of the variables on prostate cancer-specific mortality. Results: A higher portion of specimens with a history of neoadjuvant hormonal treatment were assigned Gleason scores of 8–10 (28 vs. 17%, p < 0.0001). The mortality curves in the Gleason score strata <8 vs. 8–10 were at large congruent in patients with and without neoadjuvant hormonal treatment. In patients with neoadjuvant hormonal treatment, a Gleason score of 8–10 was an independent predictor of prostate cancer-specific mortality; the hazard ratio was, however, somewhat lower than in patients without neoadjuvant hormonal treatment. Conclusion: This study suggests that the prognostic value of the post-radical prostatectomy Gleason score is not meaningfully jeopardized by heterogeneous neoadjuvant hormonal treatment in a routine clinical setting.
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Henderson, Sally. "Attachment security as a predictor of blood glucose control in adolescents with type 1 diabetes, when the roles of additional psychological factors are considered." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4915.

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Introduction: Key studies have found an association between attachment style and poor diabetes outcomes in the adult diabetic populations. Specifically insecure attachment has been found to predict elevated glycated haemoglobin levels (HbA1c). Further studies have indicated that substance use and mental health difficulties also influence HbA1c. These factors have been looked at individually making it difficult to directly assess the overall effect of attachment on HbA1c and the potential mediating effects of substance use and mental health. The adolescent population has not been considered in studies examining these relationships. This study compares attachment security, level of substance use, interpersonal problems, anxiety and depression in relation to their role in blood glucose control in an adolescent population with Type 1 diabetes. Method: A quantitative, cross sectional, questionnaire design was employed to examine the role of the aforementioned factors in relation to HbA1c level. The target population included all patients aged 14 years to 18 years, inclusive, who attended for review at Diabetes Clinics across Lothian. Participants had a diagnosis of Type 1 Diabetes for at least one year and no additional diagnoses of mental health disorder or other chronic condition. At the clinic patients were approached and asked to complete a set of self report questionnaires. Measures of attachment were adapted versions of the Relationship Questionnaire (RQ) and the Relationship Scales Questionnaire (RSQ). Interpersonal problems were assessed using the short version of the Inventory of Interpersonal Problems (IIP-32). The Hospital Anxiety and Depression Scale (HADS) assessed levels of anxiety and depression. The Adolescent Substance Abuse Subtle Screening Inventory- A2 (SASSI-A2) was used to measure substance use. Blood glucose levels (HbA1c%) were obtained from clinic staff. A total of 88 participants returned completed questionnaires (response rate 79.3%). Results: When all correlations between predictors and HbA1c were examined, a negative correlation was found between attachment and HbA1c level. A positive correlation was found between anxiety and HbA1c level. Multiple regression analyses examined the relationship between attachment security and HbA1c before analysing additional predictors in the same model. No significant relationships emerged however the multiple regression model was not a significant fit for the data. Path Analysis considered all relationships between variables simultaneously while also providing information on how the model fits the data. Attachment security directly related to HbA1c levels when the contributions of gender, interpersonal problems and substance use were considered. Anxiety and depression did not predict HbA1c nor did they contribute to any other relationships with HbA1c. Interpersonal problems had a direct relationship with HbA1c when the contribution of substance use and attachment were considered. Conclusion: Attachment predicts HbA1c. The nature of this relationship is further understood when the contribution of additional psychological variables are considered. Methodological issues, clinical implications and directions for future research are discussed.
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Books on the topic "Predictor factors"

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Kosior, Christine A. Visual factors as predictors of achievement and behaviour. Sudbury, Ont: Laurentian University, Department of Psychology, 1999.

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Malchose, Donald Clarence. Identifying factors that predict teen driver crashes. Fargo, N.D.]: Mountain-Plains Consortium, 2011.

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Gillespie, Maggie. Factors affecting student persistence: A longitudinal study. Iowa City, Iowa: American College Testing Program, 1992.

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Gillespie, Maggie. Factors affecting student persistence: A longitudinal study. Iowa City, Iowa: American College Testing Program, 1992.

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Turner, Chandra Ramphal. Factors that put students at risk of leaving school before graduation. Scarborough, Ont: Program Dept., Research Centre, Scarborough Board of Education, 1993.

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Blanc, Jean-Luc Le. Climate information and prediction services for fisheries. Geneva, Switzerland: World Meteorological Organization, 1995.

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Caldwell, Mary Margaret. Factors to predict the establishment of Ontario hospital foundations. Ottawa: National Library of Canada, 1990.

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An introduction to risk prediction and preventive dentistry. Chicago, Ill: Quintessence Pub. Co., 1999.

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Perinatal risk and infant development: Assessment and prediction. New York: Guilford Press, 1989.

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McTavish, Donald J. Prediction and measurement of modal damping factors for viscoelastic space structures. Washington, D. C: AIAA, 1992.

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Book chapters on the topic "Predictor factors"

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Poberaj, Boris. "Predictor Factors in Anterior Shoulder Instability." In 360° Around Shoulder Instability, 17–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61074-9_3.

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Duttaroy, Asim K., and Sanjay Basak. "Placentation as a Predictor of Feto-Placental Outcome: Effects of Early Nutrition." In Early Nutrition and Lifestyle Factors, 1–12. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38804-5_1.

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Flaa, Arnljot, Morten Rostrup, Sverre E. Kjeldsen, and Ivar Eide. "Sympathoadrenal Reactivity to Stress as a Predictor of Cardiovascular Risk Factors." In Updates in Hypertension and Cardiovascular Protection, 493–525. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75310-2_33.

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Décieux, Jean Philippe, and Elke Murdock. "Sense of Belonging: Predictors for Host Country Attachment Among Emigrants." In IMISCOE Research Series, 265–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67498-4_15.

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AbstractGerman citizens usually leave their home country voluntarily and face fewer barriers, e.g. in terms of freedom of travel or labour market integration. However, when arriving in their host country, they are confronted with the need to adapt to life in a new society. Analysing data from the German Emigration and Remigration Panel Study, we found that half of the emigrants developed a sense of belonging to their new host society. Moreover, we set out to examine this development of host country attachment. Guided by findings from acculturation and expatriate attachment research, we identified factors potentially contributing to host country attachment and tested these in a series of regression models. Permanence of the intended stay is the strongest predictor, and social integration also plays an important role. Host country language competence is also important for the identification processes. Regarding cultural distance, our findings suggest an inverted U-shaped relationship with certain cultural novelty facilitating the development of host country belonging. Moreover, the data point to a complex relationship between cultural characteristics of the target country and factors related to an emotional settlement.
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Guillán, Amanda. "Epistemological Factors of Scientific Prediction." In Pragmatic Idealism and Scientific Prediction, 101–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63043-4_4.

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Olphin, Tom, and Katrin Mueller-Johnson. "Targeting Factors that Predict Clearance of Non-domestic Assaults." In Crime Solvability Factors, 149–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17160-5_9.

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Zhang, Linlin, and Kin Fun Li. "Factors for Academic Performance Prediction." In Advances in Intelligent Systems and Computing, 447–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44038-1_41.

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Riley, Richard D., Aroon Hingorani, and Karel GM Moons. "Predictors of treatment effect." In Prognosis Research in Health Care, edited by Danielle A. van der Windt, Harry Hemingway, Peter Croft, and Danielle A. van der Windt, 188–207. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198796619.003.0009.

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A predictor of treatment effect is any factor or combination of factors (such as a patient characteristic, symptom, sign, test, or biomarker result) associated with the effect (benefit or harm) of a specific treatment in persons with a particular disease or health condition. Various terms are used across disciplines to refer to prediction of treatment effect, including treatment-predictor (treatment-covariate) interaction, effect modification, predictive (as opposed to prognostic) factors (in oncology), or moderation analysis. This chapter reviews principles of the design of studies of treatment effect predictors, such as exploration of treatment-predictor interactions in randomized trials and the importance of replication of such estimates using data from multiple trials. The application of predictors of treatment effect in practice for matching individuals or subgroups to specific treatments is introduced as one type of stratified care, and the need for impact studies to investigate whether stratified care leads to better outcomes and improved efficiency of healthcare is highlighted.
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Hanzec Marković, Ivana, and Gordana Kuterovac Jagodić. "Transition to Elementary School in Croatia." In Advances in Early Childhood and K-12 Education, 165–87. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4435-8.ch008.

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This chapter presents the results of an empirical study conducted in Croatia examining individual child characteristics (specific cognitive skills and social-emotional competence) and contextual factors (parental involvement in transition and the schools' readiness) as possible determinants of a successful transition to elementary school (i.e., children's early social and academic school adjustment). The results of the study with 417 first-grade students showed that specific cognitive skills were the best predictor of academic adjustment, and also a significant predictor of some social adjustment indicators, while social-emotional competence predicted the student-teacher relationship. Contextual factors showed no significance as predictors or moderators in the model. Patterns of relationships were equal for girls and boys. The chapter offers possible explanations for the study results, along with suggestions for future research and potential practical implications of the obtained results.
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Day, David M., and Margit Wiesner. "Predictors and Correlates of Criminal Trajectory Groups." In Criminal Trajectories, 169–203. NYU Press, 2019. http://dx.doi.org/10.18574/nyu/9781479880058.003.0007.

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This chapter reviews the literature on developmental predictors and correlates of high-rate-chronic offense trajectories identified in trajectory studies. A number of studies have identified that a small group of offenders account for a disproportionate number of offenses. Therefore, understanding the developmental precursors of this pernicious group may inform early intervention and prevention programs. To set the stage for the discussion, key terms, such as risk factors, correlates, and causal risk factors, are differentiated and defined to provide conceptual clarification. Findings across studies suggest that no one variable in childhood or adolescence emerged as a significant predictor or correlate of the high rate, chronic trajectory group. Rather, multiple variables across various life domains (e.g., family, peer, school, and neighborhood) predicted trajectory membership. Further research is needed to understand the potential causal mechanisms linking risk factors to criminal outcomes.
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Conference papers on the topic "Predictor factors"

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Fenton, N., M. Neil, W. Marsh, P. Hearty, L. Radlinski, and P. Krause. "Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction." In 2007 3rd International Workshop on Predictor Models in Software Engineering. IEEE, 2007. http://dx.doi.org/10.1109/promise.2007.11.

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Ottley, Alvitta, Huahai Yang, and Remco Chang. "Personality as a Predictor of User Strategy." In CHI '15: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2702123.2702590.

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Akilli, H., B. Tunc, YA Tohma, K. Esra, and A. Ayhan. "EP240 Predictor factors of lymph node metastases in cervical cancer." In ESGO Annual Meeting Abstracts. BMJ Publishing Group Ltd, 2019. http://dx.doi.org/10.1136/ijgc-2019-esgo.301.

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Akçeli Şahin, Aysegül, Zuleyha Bingol, Zeki Kilicaslan, Tulin Cagatay, and Gulfer Okumus. "Predictor factors of mortality in patients with idiopathic pulmonary fibrosis." In ERS International Congress 2018 abstracts. European Respiratory Society, 2018. http://dx.doi.org/10.1183/13993003.congress-2018.pa2954.

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Rami´rez, M. de J., M. Correa, C. Rodri´guez, and J. R. Alique. "Surface Roughness Modeling Based on Surface Roughness Feature Concept for High Speed Machining." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82256.

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This paper explains the concepts to develop a Model of Surface Roughness in order to help researchers to model predictors for high speed machining, also a concept of a surface roughness feature (RaF) is introduced. A RaF is an information piece that shows the factors used by a Ra prediction technique associate with a specific geometric feature. The surface roughness information model is a repository of the RaFs designed to focus on particular workpiece geometries. The Ra predictor developer can design the content of the Ra information model according with his Ra prediction technique to be developed. Each RaF matches with a prediction technique to form RaF predictors and they are united to form a general Ra predictor for the entire workpiece profile.
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Mamonov, Stanislav, and Marios Koufaris. "It’s cool! analysis of factors that influence smart thermostat adoption intention." In Enabling Technology for a Sustainable Society. University of Maribor Press, 2020. http://dx.doi.org/10.18690/978-961-286-362-3.5.

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Smart thermostats represent an innovative smart home technology and a growing commercial opportunity, yet little is known about the salient factors that affect the adoption of such devices. To address this gap in research, we conduct a three-stage study that progresses through belief elicitation, exploratory factor analysis and confirmatory factory analysis within a nomological network. We leverage the mixed methods approach to explore the factorial structure of salient perceived benefits and concerns associated with smart thermostats, and we examine the effects of the emergent factors on the adoption intention. We discover that a novel factor, which we term techno-coolness, is the key predictor of the smart thermostat adoption intention. Techno-coolness encompasses the perceptions that a smart thermostat can make a home look modern and futuristic, be fun to use, and make the user feel technologically advanced. We also find that compatibility concerns as well as privacy concerns are significant impediments to the smart thermostat adoption intention.
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Rotimi, Omolola, Kikelomo Evbuoma, Sussan Adeusi, and Abiodun Gesinde. "PEER INFLUENCE AND FAMILY FACTORS AS PREDICTOR OF STUDENTS TRUANT BEHAVIOUR IN SCHOOLS." In 13th International Technology, Education and Development Conference. IATED, 2019. http://dx.doi.org/10.21125/inted.2019.1473.

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Innocent, Okechukwu Prince. "Application of Machine Learning in Predicting Crude Oil Production Volume." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207079-ms.

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Abstract The production of oil is of great and immense significance as a source of energy worldwide. The major factors affecting the production volume of oil is classified into two groups namely the geological and the human factor. Each group comprises of factors affecting oilfield production volume. The challenge in this project is to find the variable for the crude oil production volume in an oilfield because there are numerous factors affecting the crude oil production volume in an oilfield. The objective of this paper is to provide a more accurate and efficient solution on how to predict the oil production volume. Furthermore, Machine Learning algorithm called Multiple Linear Regression was developed using Python programming Language to predict the production volume of oil in an oilfield. The model was developed and fitted to train and test the factors that affect and influence the oil production volume. After a several studies have been made, the affecting factors were provided from the oilfield which would be trained and tested in order to model the relationship between predictor variable and response variable which are the significant affecting factors and the oil production volume respectively. The predictor variables are the startup number of wells, the recovery percent of previous year, the injected water volume of previous year and the oil moisture content of previous year. The predictor variable is the oil production volume. Moreover, the model was found to possess greater utility in predicting the production volume of oil as it yielded an oil production volume output with an accuracy of 98 percent. The relationship between oil production volume and the affecting factors was observed and drawn to a perfect conclusion. This model can be of immense value in the oil and gas industry if implemented because of its ability to predict oilfield output more accurately. It is an invaluable and very efficient model for the oilfield manager and oil production manager.
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Xu, Wangkun. "An analysis to modified fuzzy PID controller with adaptive scaling factors and smith predictor." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8244140.

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Gonzalez Alvarez, M. G., J. Molina, L. Nuño, V. Navarro-Compán, A. Villalba, D. Peiteado, P. Bogas, and A. Balsa. "AB0265 Clinical predictor factors associated with sustained disease activity among patients with early rheumatoid arthritis." In Annual European Congress of Rheumatology, EULAR 2018, Amsterdam, 13–16 June 2018. BMJ Publishing Group Ltd and European League Against Rheumatism, 2018. http://dx.doi.org/10.1136/annrheumdis-2018-eular.3543.

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Reports on the topic "Predictor factors"

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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.
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Sexton, Donald W. Combat Related Environmental Risk Factors as Predictors of Self-Rated Health. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada493592.

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Nagahi, Morteza, Raed Jaradat, Mohammad Nagahisarchoghaei, Ghodsieh Ghanbari, Sujan Poudyal, and Simon Goerger. Effect of individual differences in predicting engineering students' performance : a case of education for sustainable development. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40700.

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The academic performance of engineering students continues to receive attention in the literature. Despite that, there is a lack of studies in the literature investigating the simultaneous relationship between students' systems thinking (ST) skills, Five-Factor Model (FFM) personality traits, proactive personality scale, academic, demographic, family background factors, and their potential impact on academic performance. Three established instruments, namely, ST skills instrument with seven dimensions, FFM traits with five dimensions, and proactive personality with one dimension, along with a demographic survey, have been administrated for data collection. A cross-sectional web-based study applying Qualtrics has been developed to gather data from engineering students. To demonstrate the prediction power of the ST skills, FFM traits, proactive personality, academic, demographics, and family background factors on the academic performance of engineering students, two unsupervised learning algorithms applied. The study results identify that these unsupervised algorithms succeeded to cluster engineering students' performance regarding primary skills and characteristics. In other words, the variables used in this study are able to predict the academic performance of engineering students. This study also has provided significant implications and contributions to engineering education and education sustainable development bodies of knowledge. First, the study presents a better perception of engineering students' academic performance. The aim is to assist educators, teachers, mentors, college authorities, and other involved parties to discover students' individual differences for a more efficient education and guidance environment. Second, by a closer examination at the level of systemic thinking and its connection with FFM traits, proactive personality, academic, and demographic characteristics, understanding engineering students' skillset would be assisted better in the domain of sustainable education.
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Wilcove, Gerry L., and Robert F. Morrison. Officer Career Development: Factors that Predict Subspecialty Decisions and Proven-Subspecialty Status. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada234874.

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Gertner, George. Uncertainty Propagation and Partitioning in Spatial Prediction of Topographical Factor for RUSLE. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada379657.

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Kimmel, Donald B. Factors in Risk Prediction and Healing of Stress Fractures and Fatigue Damage in the Female Skeleton. Fort Belvoir, VA: Defense Technical Information Center, November 1998. http://dx.doi.org/10.21236/ada364081.

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Demeure, Cedric J., and Louis L. Scharf. Lattice Algorithms for Computing QR and Cholesky Factors in the Least Squares Theory of Linear Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada196454.

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McCabe, Cameron. Vulnerability and Protective Factors of Stress-Related Drinking: an Exploration of Individual and Day-Level Predictors of Alcohol Involvement. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.3278.

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Brady, Jacquelyn. Family Linked Workplace Resources and Contextual Factors as Important Predictors of Job and Individual Well-being for Employees and Families. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6886.

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Huilai, Zhang. Prognostic factors or prediction models for POD24 in patients with newly diagnosed FL：a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2021. http://dx.doi.org/10.37766/inplasy2021.2.0034.

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