Relevant bibliographies by topics / Prediction (Psychology)

Academic literature on the topic 'Prediction (Psychology)'

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Published: 4 June 2021
Last updated: 1 August 2024

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Journal articles on the topic "Prediction (Psychology)"

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Hall, Andrew N., and Sandra C. Matz. "Targeting Item–level Nuances Leads to Small but Robust Improvements in Personality Prediction from Digital Footprints." European Journal of Personality 34, no. 5 (September 2020): 873–84. http://dx.doi.org/10.1002/per.2253.

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In the past decade, researchers have demonstrated that personality can be accurately predicted from digital footprint data, including Facebook likes, tweets, blog posts, pictures, and transaction records. Such computer–based predictions from digital footprints can complement—and in some circumstances even replace—traditional self–report measures, which suffer from well–known response biases and are difficult to scale. However, these previous studies have focused on the prediction of aggregate trait scores (i.e. a person's extroversion score), which may obscure prediction–relevant information at theoretical levels of the personality hierarchy beneath the Big 5 traits. Specifically, new research has demonstrated that personality may be better represented by so–called personality nuances—item–level representations of personality—and that utilizing these nuances can improve predictive performance. The present work examines the hypothesis that personality predictions from digital footprint data can be improved by first predicting personality nuances and subsequently aggregating to scores, rather than predicting trait scores outright. To examine this hypothesis, we employed least absolute shrinkage and selection operator regression and random forest models to predict both items and traits using out–of–sample cross–validation. In nine out of 10 cases across the two modelling approaches, nuance–based models improved the prediction of personality over the trait–based approaches to a small, but meaningful degree (4.25% or 1.69% on average, depending on method). Implications for personality prediction and personality nuances are discussed. © 2020 European Association of Personality Psychology
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Putka, Dan J., Adam S. Beatty, and Matthew C. Reeder. "Modern Prediction Methods." Organizational Research Methods 21, no. 3 (April 3, 2017): 689–732. http://dx.doi.org/10.1177/1094428117697041.

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Predicting outcomes is critical in many domains of organizational research and practice. Over the past few decades, there have been substantial advances in predictive modeling methods and concepts from the computer science, machine learning, and statistics literatures that may have potential value for organizational science and practice. Nevertheless, treatment of these modern methods in major management and industrial-organizational psychology journals remains minimal. The purpose of this article is to (a) raise awareness among organizational researchers and practitioners with regard to several modern prediction methods and concepts, (b) discuss in nonmathematical terms how they compare to traditional regression-based prediction methods, and (c) provide an empirical example of their application and performance relative to traditional methods. Beyond illustrating their potential for improving prediction, we will also illustrate how these methods can offer deeper insights into how predictor content functions beyond simple construct-based explanations.
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Ganzach, Yoav, and David H. Krantz. "The psychology of moderate prediction." Organizational Behavior and Human Decision Processes 47, no. 2 (December 1990): 177–204. http://dx.doi.org/10.1016/0749-5978(90)90036-9.

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Ganzach, Yoav, and David H. Krantz. "The psychology of moderate prediction." Organizational Behavior and Human Decision Processes 48, no. 2 (April 1991): 169–92. http://dx.doi.org/10.1016/0749-5978(91)90011-h.

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Dall’Aglio, John. "Sex and Prediction Error, Part 3: Provoking Prediction Error." Journal of the American Psychoanalytic Association 69, no. 4 (August 2021): 743–65. http://dx.doi.org/10.1177/00030651211042059.

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In parts 1 and 2 of this Lacanian neuropsychoanalytic series, surplus prediction error was presented as a neural correlate of the Lacanian concept of jouissance. Affective consciousness (a key source of prediction error in the brain) impels the work of cognition, the predictive work of explaining what is foreign and surprising. Yet this arousal is the necessary bedrock of all consciousness. Although the brain’s predictive model strives for homeostatic explanation of prediction error, jouissance “drives a hole” in the work of homeostasis. Some residual prediction error always remains. Lacanian clinical technique attends to this surplus and the failed predictions to which this jouissance “sticks.” Rather than striving to eliminate prediction error, clinical practice seeks its metabolization. Analysis targets one’s mode of jouissance to create a space for the subject to enjoy in some other way. This entails working with prediction error, not removing or tolerating it. Analysis aims to shake the very core of the subject by provoking prediction error—this drives clinical change. Brief clinical examples illustrate this view.
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Watson-Daniels, Jamelle, David C. Parkes, and Berk Ustun. "Predictive Multiplicity in Probabilistic Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 10306–14. http://dx.doi.org/10.1609/aaai.v37i9.26227.

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Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or predicting a person's risk to appear in court. Given multiple models that perform almost equally well for a prediction task, to what extent do predictions vary across these models? If predictions are relatively consistent for similar models, then the standard approach of choosing the model that optimizes a penalized loss suffices. But what if predictions vary significantly for similar models? In machine learning, this is referred to as predictive multiplicity i.e. the prevalence of conflicting predictions assigned by near-optimal competing models. In this paper, we present a framework for measuring predictive multiplicity in probabilistic classification (predicting the probability of a positive outcome). We introduce measures that capture the variation in risk estimates over the set of competing models, and develop optimization-based methods to compute these measures efficiently and reliably for convex empirical risk minimization problems. We demonstrate the incidence and prevalence of predictive multiplicity in real-world tasks. Further, we provide insight into how predictive multiplicity arises by analyzing the relationship between predictive multiplicity and data set characteristics (outliers, separability, and majority-minority structure). Our results emphasize the need to report predictive multiplicity more widely.
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Fokkema, Marjolein, Dragos Iliescu, Samuel Greiff, and Matthias Ziegler. "Machine Learning and Prediction in Psychological Assessment." European Journal of Psychological Assessment 38, no. 3 (May 2022): 165–75. http://dx.doi.org/10.1027/1015-5759/a000714.

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Abstract. Modern prediction methods from machine learning (ML) and artificial intelligence (AI) are becoming increasingly popular, also in the field of psychological assessment. These methods provide unprecedented flexibility for modeling large numbers of predictor variables and non-linear associations between predictors and responses. In this paper, we aim to look at what these methods may contribute to the assessment of criterion validity and their possible drawbacks. We apply a range of modern statistical prediction methods to a dataset for predicting the university major completed, based on the subscales and items of a scale for vocational preferences. The results indicate that logistic regression combined with regularization performs strikingly well already in terms of predictive accuracy. More sophisticated techniques for incorporating non-linearities can further contribute to predictive accuracy and validity, but often marginally.
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Stenhaug, Benjamin A., and Benjamin W. Domingue. "Predictive Fit Metrics for Item Response Models." Applied Psychological Measurement 46, no. 2 (February 13, 2022): 136–55. http://dx.doi.org/10.1177/01466216211066603.

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The fit of an item response model is typically conceptualized as whether a given model could have generated the data. In this study, for an alternative view of fit, “predictive fit,” based on the model’s ability to predict new data is advocated. The authors define two prediction tasks: “missing responses prediction”—where the goal is to predict an in-sample person’s response to an in-sample item—and “missing persons prediction”—where the goal is to predict an out-of-sample person’s string of responses. Based on these prediction tasks, two predictive fit metrics are derived for item response models that assess how well an estimated item response model fits the data-generating model. These metrics are based on long-run out-of-sample predictive performance (i.e., if the data-generating model produced infinite amounts of data, what is the quality of a “model’s predictions on average?”). Simulation studies are conducted to identify the prediction-maximizing model across a variety of conditions. For example, defining prediction in terms of missing responses, greater average person ability, and greater item discrimination are all associated with the 3PL model producing relatively worse predictions, and thus lead to greater minimum sample sizes for the 3PL model. In each simulation, the prediction-maximizing model to the model selected by Akaike’s information criterion, Bayesian information criterion (BIC), and likelihood ratio tests are compared. It is found that performance of these methods depends on the prediction task of interest. In general, likelihood ratio tests often select overly flexible models, while BIC selects overly parsimonious models. The authors use Programme for International Student Assessment data to demonstrate how to use cross-validation to directly estimate the predictive fit metrics in practice. The implications for item response model selection in operational settings are discussed.
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Yaniv, Ilan, and Robin M. Hogarth. "Judgmental Versus Statistical Prediction: Information Asymmetry and Combination Rules." Psychological Science 4, no. 1 (January 1993): 58–62. http://dx.doi.org/10.1111/j.1467-9280.1993.tb00558.x.

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The relative predictive accuracy of humans and statistical models has long been the subject of controversy even though models have demonstrated superior performance in many studies. We propose that relative performance depends on the amount of contextual information available and whether it is distributed symmetrically to humans and models. Given their different strengths, human and statistical predictions can be profitably combined to improve prediction.
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Poon, Connie S. K., Derek J. Koehler, and Roger Buehler. "On the psychology of self-prediction: Consideration of situational barriers to intended actions." Judgment and Decision Making 9, no. 3 (May 2014): 207–25. http://dx.doi.org/10.1017/s1930297500005763.

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AbstractWhen people predict their future behavior, they tend to place too much weight on their current intentions, which produces an optimistic bias for behaviors associated with currently strong intentions. More realistic self-predictions require greater sensitivity to situational barriers, such as obstacles or competing demands, that may interfere with the translation of current intentions into future behavior. We consider three reasons why people may not adjust sufficiently for such barriers. First, self-predictions may focus exclusively on current intentions, ignoring potential barriers altogether. We test this possibility, in three studies, with manipulations that draw greater attention to barriers. Second, barriers may be discounted in the self-prediction process. We test this possibility by comparing prospective and retrospective ratings of the impact of barriers on the target behavior. Neither possibility was supported in these tests, or in a further test examining whether an optimally weighted statistical model could improve on the accuracy of self-predictions by placing greater weight on anticipated situational barriers. Instead, the evidence supports a third possibility: Even when they acknowledge that situational factors can affect the likelihood of carrying out an intended behavior, people do not adequately moderate the weight placed on their current intentions when predicting their future behavior.
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Dissertations / Theses on the topic "Prediction (Psychology)"

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Maskill, Louise. "Prediction in discourse : the problems and potential of qualitative forecasting in psychology." Thesis, University of Nottingham, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.394849.

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Stark, Darryl Wayne. "The validity of cognitive and non-cognitive predictors over time /." Access abstract and link to full text, 1994. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/9513944.

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Ivarsson, Andreas. "Psychology of Sport Injury : Prediction, Prevention and Rehabilitation in Swedish Team Sport Athletes." Doctoral thesis, Växjö, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-42982.

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The dissertation consists of five separate studies that all have focused on different aspects of the relationship between psychological factors and sport injuries. In the first study the aim was to investigate female elite soccer players’ experiences of the time prior to the occurrence of an ACL injury. In the result three themes of player experiences was identified: fatigue, life stress, and worry. The players interpreted these three themes as risk factors for ACL-injury.   The second study aimed to investigate whether personality, stress, and coping predicted injury occurrence in an elite soccer population. The result showed that an interaction between psychological variables (i.e. trait anxiety, negative life event stress and daily hassles, ineffective coping) could explain 24 % of the variance in injury occurrence. Moreover, the result showed that negative life event stress was found to have an indirect effect on injury occurrence through daily hassles. In the third study the aim was to investigate whether individual level and changes in hassle and uplift levels over a 10-week period could predict injury outcome in an elite junior soccer population. The results showed that both initial level as well as change in hassle influenced injury risk. More specific, both high initial level as well as slow decrease in hassle was associated with an increased risk of injury. The fourth study aimed to investigate the extent to which a mindfulness and acceptance based intervention program could reduce the number of sports injuries in a sample of soccer players. The result showed no statistically significant differences in injury rates between the two groups, but there was a medium effect size (adjusted Cohen´s d = - 0.59). In the fifth study the aim was to investigate an athletic injury as a career transition through the narrative expression of successful and less successful injury experiences of a former elite handball player. The participant’s narratives made possible to identify four phases (i.e., pre-injury, injury and first reactions, diagnosis and treatment, rehabilitation and consequences) in the injury transition with distinct psychological content (e.g., demands, resources, barriers, and coping strategies) relevant to each phase
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af, Wåhlberg Anders. "The Prediction of Traffic Accident Involvement from Driving Behavior." Doctoral thesis, Uppsala universitet, Institutionen för psykologi, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6296.

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The aim of the studies was to predict individual traffic accident involvement by the quantification of driving style in terms of speed changes, using bus drivers as subjects. An accident database was constructed from the archives of the bus company whose drivers were used as subjects. The dependent variable was also discussed regarding whether responsibility for crashes should be included, and what time period to use for optimal prediction. A new theory was constructed about how accidents are caused by driver behavior, more specifically the control movements of the driver, i.e. all actions taken which influence the relative motion of the vehicle in a level plane when v>0. This theory states that all traffic safety related behavior can be measured as celerations (change of speed of the vehicle in any direction of a level plane) and summed. This theoretical total sum is a measure of a person's liability to cause accidents over the same time period within a homogenous traffic environment and a similarly homogenous driving population. Empirically, the theory predicts a positive correlation between mean driver celeration behavior and accident record. The theory was tested in three empirical studies. The first tested equipment and methods, the second studied the question whether driver celeration behavior is stable over time. Celeration behavior turned out to be rather variable between days, and repeated measurements were therefore needed to stabilize the measure. In the third study, a much larger amount of data brought out correlations of sizes sufficient to lend some credibility to the theory. However, the predictive power did not extend beyond two years of time. The reported results would seem to imply that the celeration variable can predict accident involvement (at least for bus drivers), and is practical to use, as it is easily and objectively measured and semi-stable over time.
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Hasinski, Adam E. "Interactions between Prediction, Perception and Episodic Memory." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437731857.

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Falk, Robert S. "Prediction of children's sociometric status from adult ratings." VCU Scholars Compass, 1986. http://scholarscompass.vcu.edu/etd/4705.

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Poor peer relations in childhood predict difficulty in adolescent and adult adjustment. Sociometric methods provide a useful way to operationalize social competence. Five groups of children (Average, Popular, Rejected, Neglected, and Controversial), identified by nomination sociometrics, show significant differences on a variety of behavioral and non-behavioral indices. This categorization scheme has value in the identification of children at risk for later maladaptive outcomes, and may be useful in designing preventive intervention programs. The current study attempted to determine the degree to which diagnostic ratings generated by significant adults can be generalized to the sociometric status of children. Parent and teacher ratings were gathered on 134 children who had previously been categorized sociometrically as Popular, Average, Neglected or Rejected. Two significant discriminant functions were found that together accounted for 95% of the variance shared between the sociometric groups and adult ratings. Interpretation of the discriminant functions suggests that Rejected children are rated by adults as more emotionally labile, interpersonally hostile, and less able to cope with failure and social pressure than the Neglected and Popular children. Neglected children are seen as displaying slight motoric, cognitive, and/or academic deficits compared to their Popular peers. The discriminant functions generated were able to correctly classify 62% of the total original sample, 48% with bias removed. Diagnostic inferences and implications of the results for clinical practice are discussed. Limitations of the study together with possible directions for future research are presented.
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Sun, Jie. "Object categorization for affordance prediction." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24625.

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Thesis (Ph.D.)--Computing, Georgia Institute of Technology, 2009.
Committee Chair: Rehg, James M.; Committee Co-Chair: Bobick, Aaron; Committee Member: Balch, Tucker; Committee Member: Christensen, Henrik I.; Committee Member: Pietro Perona
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Chapman, Christopher L. "Clinical Prediction in Group Psychotherapy." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2144.

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Prior research in individual therapy has provided evidence that therapists are poor predictors of client outcome without the aid of objective measures and often misjudge clients' perceptions of the therapeutic relationship. The focus of the current research was to conduct a similar study in a group setting. Therapists from a university counseling center and a state psychiatric hospital were recruited to test their accuracy in predicting client outcome, quality of therapeutic relationship and their own use of empirically supported group interventions. Results indicated that therapists are poor predictors of all three, providing support for the implementation of measure-based feedback systems to inform therapists about key information that may affect the effectiveness of group psychotherapy.
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Murdock, Melissa E. (Melissa Erleene). "The Prediction of Adjustment in Institutionalized Juvenile Offenders." Thesis, University of North Texas, 1997. https://digital.library.unt.edu/ark:/67531/metadc279119/.

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Predictors of institutional adjustment for juvenile offenders were examined using a sample of 120 males in a detention facility. While demographic information failed to differentiate between well and poorly adjusted juveniles, psychological measures appeared to be more effective. Several MMPI-A clinical scales were useful predictors with the overall elevation in clinical scales being one of the strongest predictors. In addition, the Psychopathy Checklist - Clinical Version (PCL-CV) was a strong predictor of adjustment. Major ethnic differences occurred in the prediction of adjustment, with the MMPI-A and PCL-CV scales predicting infraction rates for the African American group but not Anglo American or Hispanic American groups.
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Tate, Charles Ulysses. "Mental simulation of the future : processes and principles /." view abstract or download file of text, 2006. http://proquest.umi.com/pqdweb?index=0&did=1251849171&SrchMode=1&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1181338221&clientId=11238.

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Thesis (Ph. D.)--University of Oregon, 2006.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 152-158). Also available for download via the World Wide Web; free to University of Oregon users.
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Books on the topic "Prediction (Psychology)"

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Meehl, Paul E. Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. Northvale, N.J: J. Aronson, 1996.

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John, Colombo, Fagen Jeffrey, and Society for Research in Child Development. Meeting, eds. Individual differences in infancy: Reliability, stability, prediction. Hillsdale, N.J: Lawrence Erlbaum Associates, 1990.

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Monahan, John. The prediction of violent behaviour: Developments in psychology and law. [Preston: Lancashire Polytechnic, 1986.

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P, Farrington David, ed. Criminal recidivism: Explanation, prediction and prevention. Abingdon, Oxon: Routledge, 2015.

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1953-, Brizer David A., and Crowner Martha 1956-, eds. Current approaches to the prediction of violence. Washington, DC: American Psychiatric Press, 1989.

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Lester, David. Suicide behind bars: Prediction and prevention. Philadelphia: Charles Press, 1993.

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Lester, David. Suicide behind bars: Prediction and prevention. Philadelphia: Charles Press, 1993.

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Petrova, V. N. Kommunikativnai︠a︡ priroda obraza budushchego. Tomsk: Tomskiĭ gos. universitet, 2010.

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Dr, Iannou Maria, and Youngs Donna, eds. Explorations in investigative psychology and contemporary offender profiling. London: IA-IP Pub., 2006.

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L, Erlenmeyer-Kimling, and Miller Nancy E, eds. Life-span research on the prediction of psychopathology. Hillsdale, N.J: L. Erlbaum, 1986.

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Book chapters on the topic "Prediction (Psychology)"

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Grove, William M. "Actuarial prediction." In Encyclopedia of psychology, Vol. 1., 33–35. Washington: American Psychological Association, 2000. http://dx.doi.org/10.1037/10516-012.

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Hanson, R. Karl. "Introduction to prediction statistics in psychology." In Prediction statistics for psychological assessment., 3–20. Washington: American Psychological Association, 2022. http://dx.doi.org/10.1037/0000275-001.

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Smithson, Michael. "Prediction and Fuzzy Logic." In Recent Research in Psychology, 251–87. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4680-0_8.

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Hirt, Edward R., and Hector Ruiz Guevara. "Forecasting and Prediction." In Handbook of Research Methods in Consumer Psychology, 241–58. Second edition. | New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9781351137713-13.

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Wang, Shirley B., Walter Dempsey, and Matthew K. Nock. "Machine Learning for Suicide Prediction and Prevention: Advances, Challenges, and Future Directions." In SpringerBriefs in Psychology, 21–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06127-1_3.

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AbstractThis chapter describes the role of machine learning in youth suicide prevention. Following a brief history of suicide prediction, research is reviewed demonstrating that machine learning can enhance suicide prediction beyond traditional clinical and statistical approaches. Strategies for internal and external model evaluation, methods for integrating model results into clinical decision-making processes, and ethical issues raised by building and implementing suicide prediction models are discussed. Finally, future directions for this work are highlighted, including the need for collaborative science and the importance of both data- and theory-driven computational methods.
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Hahlweg, Kurt. "Schizophrenie Psychoses: Prediction and Prevalence." In Perspectives and Promises of Clinical Psychology, 177–94. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-3674-5_16.

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Bonta, James, and D. A. Andrews. "Prediction and Classification of Criminal Behavior." In The Psychology of Criminal Conduct, 197–239. 7th ed. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003292128-13.

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Heilbrun, Kirk. "Violence Risk: From Prediction to Management." In Handbook of Psychology in Legal Contexts, 127–42. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470013397.ch5.

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Lamade, Raina V., Mariesa Pennine, and Chloe R. Grabanski. "Maternal Filicide: Prediction of Risk Factors." In Encyclopedia of Sexual Psychology and Behavior, 1–10. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-08956-5_1498-1.

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Hall, Harold V. "Violence Prediction and Risk Analysis." In Forensic Psychology and Neuropsychology for Criminal and Civil Cases, 365–402. 2nd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.4324/9781003213307-12.

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Conference papers on the topic "Prediction (Psychology)"

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Varadarajan, Karthik Mahesh, Kai Zhou, and Markus Vincze. "Transport psychology based cognitive architecture for traffic behavior prediction." In 2011 14th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/itsc.2011.6082797.

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Li, Leida, Jiachen Duan, Yuzhe Yang, Liwu Xu, Yaqian Li, and Yandong Guo. "Psychology Inspired Model for Hierarchical Image Aesthetic Attribute Prediction." In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022. http://dx.doi.org/10.1109/icme52920.2022.9859845.

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Halde, Radhika R., Arti Deshpande, and Anjali Mahajan. "Psychology assisted prediction of academic performance using machine learning." In 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2016. http://dx.doi.org/10.1109/rteict.2016.7807857.

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Orlov, Mikhail. "POLYPEPTIDE’S PHYSICAL PROPERTIES FOR AMYLOID PREDICTION." In XVI International interdisciplinary congress "Neuroscience for Medicine and Psychology". LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m1188.sudak.ns2020-16/356.

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MORSE, ANTHONY F. "NEURAL MODELS OF PREDICTION AND SUSTAINED INATTENTIONAL BLINDNESS." In Proceedings of the 11th Neural Computation and Psychology Workshop. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789812834232_0032.

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Klapproth, Florian, and Paule Schaltz. "The Prediction of Students’ Achievements in School - Students’s Ethnicity as a Moderator of the Prediction’s Validity." In Annual International Conference on Cognitive and Behavioral Psychology (CBP 2014). GSTF, 2014. http://dx.doi.org/10.5176/2251-1865_cbp14.14.

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Makarevskaya, U. E. "The Adequacy Of Personal Success Prediction In Psychological Achievement Testing." In ICPE 2017 International Conference on Psychology and Education. Cognitive-Crcs, 2017. http://dx.doi.org/10.15405/epsbs.2017.12.22.

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Ratnaparkhi, Bhakti, Lokesh Katore, and J. S. Umale. "Improved student psychology prediction & recommendation strategy using 2 state data analysis." In 2015 Global Conference on Communication Technologies (GCCT). IEEE, 2015. http://dx.doi.org/10.1109/gcct.2015.7342786.

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Multusova, Evgenia, Andrei Pokhachevsky, and Aleksey Umryukhin. "METHODS AND OPTIONS FOR THE PREDICTION OF PHYSICAL PERFORMANCE." In XV International interdisciplinary congress "Neuroscience for Medicine and Psychology". LLC MAKS Press, 2019. http://dx.doi.org/10.29003/m484.sudak.ns2019-15/293.

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Maercker, Andreas. "The Interplay Between Traditional and Modern Values and Interpersonal Variables in Mental Disorders and Mental Health." In International Association of Cross Cultural Psychology Congress. International Association for Cross-Cultural Psychology, 2016. http://dx.doi.org/10.4087/rxzf7019.

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Our research focuses on values and mental health, and possible mediating factors. Based on two value-related theories – Schwartz’s and Inglehart’s - we suggest a complex prediction model: It hypothesises that social support mediates the relationship between traditional values and mental health, whereas the relationship between modern values and mental health is mediated by resilience. We tested our model with three large student samples from China, Russia, and Germany. By and large, our hypotheses were confirmed: Particularly traditional values were relevant for mental health by predicting social support and thence mental health. With regard to modern values, the value of self-direction predicted resilience and – in consequence – mental health. Hedonism did not show the predicted association with resilience. We discuss the implications of these findings and future directions.
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Reports on the topic "Prediction (Psychology)"

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Chang, Hyo Jung (Julie), Michael O'Boyle, Ronald Anderson, and Chompoonut Suttikun. A Neural Predictor of Consumer Psychology: An fMRI Study of the Effect of Celebrity, Non-Celebrity, and Rational Advertising Appeals on Dress Attractiveness. Ames: Iowa State University, Digital Repository, November 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1478.

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Can population registry data predict which children with ADHD are at risk of later substance use disorders? ACAMH, July 2020. http://dx.doi.org/10.13056/acamh.12430.

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The first study to examine the potential of machine learning in early prediction of later substance use disorders (SUDs) in youth with ADHD has been published in the Journal of Child Psychiatry and Psychology.
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How useful are Ofsted ratings for predicting educational outcomes and wellbeing at secondary school? ACAMH, October 2020. http://dx.doi.org/10.13056/acamh.13604.

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“The factors parents care about most when selecting a school – their child’s educational achievement and wellbeing – are negligibly predicted by Ofsted ratings”, says Sophie von Stumm, lead researcher of a new study published in the Journal of Child Psychology and Psychiatry.
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