Understanding the Big Five Personality Traits

Todays psychologists agree that personality can be described by five broad traits: openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. Together, these traits make up the five-factor model of personality known as the Big Five. Key Takeaways: Big Five Personality Traits The Big Five personality traits are openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism.Each trait represents a continuum. Individuals can fall anywhere on the continuum for each trait.Evidence suggests that personality is highly stable during adulthood, although small changes may be possible. Origin of the Big Five Model The Big Five, as well as other models that specify human personality traits, arises from the lexical hypothesis, which was first proposed by Francis Galton in the 1800s. The lexical hypothesis states that every natural language contains all the personality descriptions that are relevant and important to the speakers of that language. In 1936, pioneering psychologist Gordon Allport and his colleague Henry Odbert explored this hypothesis by going through an unabridged English dictionary and creating a list of 18,000 words related to individual differences. Approximately 4,500 of those terms reflected personality traits. This sprawling set of terms gave psychologists interested in the lexical hypothesis a place to start, but it wasnt useful for research, so other scholars attempted to narrow the set of words down. Eventually, in the 1940s, Raymond Cattell and his colleagues used statistical methods to reduce the list to a set of only 16 traits. Several additional scholars analyzed Cattell’s work, including Donald Fiske in 1949, and they all came to a similar conclusion: the data contained a strong, stable set of five traits. However, it wasnt until the 1980s that the Big Five began to receive wider scholarly attention. Today, the Big Five is a ubiquitous part of psychology research, and psychologists largely agree that personality can be grouped into the five basic traits specified by the Big Five. The Big Five Traits Each Big Five trait represents a continuum. For example, the trait of extraversion’s opposite is introversion. Together, extraversion and introversion make up opposing ends of a spectrum for that Big Five trait. People can be very extraverted or very introverted, but most people will fall somewhere in between the extremes of the spectrum.   Its also important to remember that each trait of the Big Five is very broad, representing a cluster of many personality characteristics. These characteristics are more specific and granular than each of the five traits as a whole. Thus, each trait can be defined in general and also broken down into several facets. Openness to Experience If you possess high openness to experience, you are open to all the original and complex things life has to offer, both experientially and mentally. The opposite of openness to experience is close-mindedness. Individuals with this trait are usually: CuriousImaginativeArtisticInterested in many thingsExcitableUnconventional Conscientiousness Conscientiousness means having good impulse control, which enables individuals to fulfill tasks and meet goals. Conscientious behavior includes planning and organization, delaying gratification, avoiding compulsive action, and following cultural norms. The opposite of conscientiousness is lack of direction. Key facets of conscientiousness include: CompetenceOrder, or organizational skillsDutifulness, or a lack of carelessnessAchievement through hard workSelf-disciplineBeing deliberate and controlled Extraversion Extraverted individuals who draws their energy from their interactions with the social world. Extraverts are sociable, talkative, and outgoing. The opposite of extraversion is introversion. Extraverts are typically: GregariousAssertiveActiveExcitement-seekingEmotionally positive and enthusiasticWarm and outgoing Agreeableness The trait of agreeableness refers to a positive and altruistic orientation. This trait enables individuals to see the best in others, trust others, and behave prosocially. The opposite of agreeableness is antagonism. Agreeable people are often: Trusting and forgivingStraightforward and undemandingAltruisticAffable and amenableModestSympathetic to others Neuroticism Neuroticism refers to a tendency towards negative emotions and includes experiences like feeling anxious and depressed. The opposite of neuroticism is emotional stability. Key facets of neuroticism include: Anxiety and tensionAngry hostility and irritability,Depression,Self-consciousness and shyness,Being impulsive and moodyLack of self-confidence The acronym OCEAN is a handy device for the traits specified by the Big Five. Can Personality Be Changed? Personality traits tend to be highly stable during adulthood. While some gradual shifts in personality traits may be possible, these shifts are generally not drastic. In other words, if an individual is low on the trait of extraversion (meaning they are more introverted than extraverted), they are likely to stay that way, though they may become slightly more or less extraverted over time. This consistency is partially explained by genetics, which plays a significant role in the traits one develops. For example, one twin study showed that when the Big Five personality traits of identical and fraternal twins were assessed, the influence of genetics was 61% for openness to experience, 44% for conscientiousness, 53% for extraversion, and 41% for both agreeableness and neuroticism. Environment may indirectly reinforce inherited traits as well. For instance, in creating an environment that works with their own traits, parents also create an environment that works with their children’s traits. Similarly, as adults, people choose environments that reinforce and support their traits. The Big Five in Childhood Research on the Big Five has been criticized in the past for focusing primarily on adult personality development and ignoring the development of these traits in children. Yet, recent research has shown that children as young as five have the ability to describe their personality and that by six, children begin to show consistency and stability in the traits of conscientiousness, extraversion, and agreeableness. Two other studies showed that while the Big Five seems to manifest in children, childrens personalities may also include additional traits. One study of American adolescent boys found that in addition to the Big Five traits, participants also displayed two additional traits. The researchers labeled these as irritability  (negative affect that led to developmentally inappropriate behaviors like whining and tantrums) and activity (energy and physical activity). Another study of Dutch children of both sexes between the ages of 3 and 16 also found two additional personality traits. While one was similar to the activity trait found in the previously discussed study, the other, dependency (relying on others), was different. Age Differences in Personality Traits Research has suggested the Big Five traits evolve with age over the life span. In an analysis of 92 longitudinal studies that examined changes in personality traits from youth to old age, scholars found that people became more conscientious, less neurotic, and increase in social dominance, a facet of extraversion, as they get older. People also became more agreeable in old age. And while adolescents were more open to experience and demonstrated greater social vitality, another facet of extraversion, especially during the college years, people decreased in these traits during old age. Sources Allport, Gordon W. and Henry S. Odbert. â€Å"Trait-Names: A Psycho-Lexical Study.† Psychological Monographs, vol. 47, no. 1, 1936, pp. i-171. http://dx.doi.org/10.1037/h0093360Cattell, Raymond B. â€Å"The description of Personality: Basic Traits Resolved Into Clusters.† Journal of Abnormal and Social Psychology, vol. 38, vol. 4, 1943, pp. 476-506. http://dx.doi.org/10.1037/h0054116Costa, Paul T., and Robert R. McCrae. â€Å"The NEO-PI-R: Professional Manual.† Psychological Assessment Resources, 1992. http://www.sjdm.org/dmidi/NEO_PI-R.htmlDigman, John M. â€Å"Personality Structure: Emergence of the Five-Factor Model.† Annual Review of Psychology, vol. 41, 1990, pp. 417-440. http://dx.doi.org/10.1146/annurev.ps.41.020190.002221Fiske, Donald W. â€Å"Consistency of the Factorial Structures of Personality Ratings from Difference Sources.† Journal of Abnormal and Social Psychology, vol. 44, 1949, pp. 329-344. http://dx.doi.org/10.1037/h0057198Jang, K erry J., John Livesley, and Philip A. Vernon. â€Å"Heritability of the Big Five Personality Dimensions and Their Facets: A Twin Study.† Journal of Personality, vol. 64, no. 3, 1996, pp. 577-592. https://doi.org/10.1111/j.1467-6494.1996.tb00522.xJohn, Oliver P., Avshalom Caspi, Richard W. Robins, Terrie E. Moffitt, and Magda Stouthamer-Loeber. â€Å"The ‘Little Five’: Exploring The Nomological Network of the Five-Factor Model of Personality in Adolescent Boys. Child Development, vol. 65, 1994, pp. 160-178. https://doi.org/10.1111/j.1467-8624.1994.tb00742.xJohn, Oliver P., Laura P. Naumann, and Christopher J. Soto. â€Å"Paradigm Shift to the Integrative Big Five Trait Taxonomy: History, Measurement, and Conceptual Issues.† Handbook of Personality: Theory and Research, 3rd ed., edited by Oliver P. John, Richard W. Robins, and Lawrence A. Pervin, The Guilford Press, 2008, pp. 114-158.John, Oliver P. and Sanjay Srivastava. â€Å"The Big Five Trait Taxonomy: History, Measurement, and Theoretical Perspectives.† Handbook of Personality: Theory and Research, 2nd ed., edited by Lawrence A. Pervin, and Oliver P. John, The Guilford Press, 1999, pp. 102-138.McAdams, Dan P. â€Å"Can Personality Change? Levels of Stability and Growth In Personality Across the Life Span.† Can Personality Change? edited by Todd F. Heatherton and Joel L. Weinberger, American Psychological Association, 1994, pp. 299-313. http://dx.doi.org/10.1037/10143-027McAdams, Dan. The Person: An Introduction to the Science of Personality Psychology. 5th ed., Wiley, 2008.Measelle, Jeffrey R., Oliver P. John, Jennifer C. Ablow, Philip A. Cowan, and Carolyn P. Cowan. â€Å"Can Children Provide Coherent, Stable, and Valid Self-Reports on the Big Five Dimensions? A Longitudinal Study from Ages 5 to 7. Journal of Personality and Social Psychology, vol. 89, 2005, pp. 90-106. http://dx.doi.org/10.1037/0022-3514.89.1.90Roberts, Brent W., Kate E. Walton, and Wolfgang Viec htbauer. â€Å"Patterns of Mean-Level Change in Personality Traits Across the Life Course: A Meta-Analysis of Longitudinal Studies.† Psychological Bulletin, vol. 132. No. 1, 2006, pp. 1-35.  Van Lieshout, Cornelis F. M. and Gerbert J. T. Haselager. â€Å"The Big Five Personality Factors in Q-Sort Descriptions of Children and Adolescents.† The Developing Structure of Temperament and Personality From Infancy to Adulthood, edited by Charles F. Halverson, Gedolph A. Kohnstamm, and Roy P. Martin, Lawrence Erlbaum Associates, 1994, pp. 293-318.

The Health Care System Within The United States - 2541 Words

Although the health care system within the United States has operated on a fee-for-service system in which a provider benefits by being paid per treatment or procedure given to a patient, the Patient Protection and Affordable Care Act has influenced us to look into alternative forms of provider reimbursement. One form of reimbursement that has recently been highlighted as a potential means to lower health care expenditures are bundled payments, which are also known as episode-based payment. Bundled payments are being touted as a means to reduce national health expenditures, increase coordination of care, and improve quality of care; however like any other form of provider reimbursement there are advantages as well as disadvantages that need to be analyzed. A bundled payment provides a physician with a set amount of money upfront in order to efficiently treat a patient’s condition; the amount of money is determined on the basis of a specific condition. 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Business Intelligence Regression and Multilevel Models - Free Sample

Question: Discuss abouit the Business Intelligence for Regression and Multilevel Models. Answer: Significance of the project The methodology of data mining is mainly consisted of searching of huge data stores that have been prepared on the basis of discovering the various types of trends and patterns. It has been found that the mathematical algorithms are mainly used as here the data segment is sent and evaluated on the basis of the events in the future. In addition to this, it has been noted that the properties are associated with a special type of pattern called automatic discovery pattern where the predictions are implemented. These said predictions are implemented on the basis of the development of the actionable information. It has been found that Gelman et al. (2006) stated that the larger sets of the databases and the data along with the automatic discovery pattern have been mainly highlighted. This has been set by the models buildings. From the detailed analysis, it has been highlighted that the set of the data is only for the data mining execution that can be used for the development of the genera lizable model in order to create the new data. According to the research study, it has been identified that there are various predictive forms for the data mining and these are quite capable for handling the methodology of predicting and working on the basis of the related probability system. In addition to these, there are many rules and regulations that are related to the various supportive forms of data mining. This particular model is created on the basis of the identification of the various segments along with the record of driving for the information regarding actions. Detailed analysis has indicated that there are various types of data mining and statistics and here various technologies are implemented for setting up the statistical framework. Additionally, this statistical framework has been developed on the basis of handling the validations along with the correctness of model. These patterns are prepared on the basis of the larger data sets that can work on the automation m odel along with the handling factor of the patterns of data mining and the OLAP. It has been found that the OLAP mainly works on different activities like allocation of the costs, analysis of the time series and the summarization of the data and database. However, this is created on the basis of the process of inference that is mainly useful for handling the various workings on different features of data mining and indicative inference. According to Crawley (2002) the OLAP mainly works on the included hierarchies that are fully supportive and these views are able to analyze the firms and the works that are directed towards the integration of various ways. The OLAP is defined as a multidimensional view that involves the analysis and support of the business of an organization that have several concepts regarding dimensions along with various patterns. It has been found that there are various ways through which data mining can be used for the development of new values, different types of cube and the dimensions. However, if the analysis of the results is done through predictive data, then it could be used for the customization of the aggregation process and the measures. Data Transformations Whenever the data has been completely formatted and when there is a proper set up of the data, this process is used for handling such types of system. The information capturing process would be handled by the destination source and it is set by them itself. The transformation of the complete program works on the code generation. The complex transformation having many to one pattern and one to one pattern is provided with the element sing this particular system. The mapping of the code generation is issued to handle the executable program that helps in maintaining the different language with ease while running on the computer program. Provided this, the recasting of the transformation is worked by the usage of master data. This is where the data base concerning different values is working on the data extractions. There is a network of the foreign key patterns which forms a part of the well designed pattern (Liang et al., 1986). The pattern of the original cost index would be able to a ssess the different costs and such types of pattern would be handled by the unique database index. The languages, which are mainly responsible for the transformation of the data into documents, are comprised of language of template, AWK and perl. The transformation of the data and the source of code are provided by the patterns. The useful information are able to achieve with the help of using the transformational language and the useful informations include text pad and tax editors such as emacs. The ext pad is used to support the use of regular expressions in a different way and the support would include the complete argument structure. There has been working on the handling of invocation with the use of different functions and this would replace the use of the single functions. The regular expression need not be performed if the test could not be performed and when there is no adequate performance on the data (Agresti et al., 2011). Data Cleansing The data cleansing also popularly known as scrubbing data indicates the process of detection as well as correction of the corrupt or else incorrect data from a specified dataset. As rightly put forward by Hair (2010), data cleansing refers to the process of the operating appropriately for uncovering and at the same time rectification of the tarnished records. The data cleansing refers to the different large data set that are presented in tables, charts and other statistical tools. The dataset can thereby be utilized for detecting different unfinished and at the same time incorrect data. However, data cleansing also help in the process of modification of the entire data with the assimilation of different tools and batch processing by way of scripting. (Hair, 2010). As such, the data cleansing can also help in the handling different data set in a system. The particular procedure also helps in removal of different types of inconsistencies and at the same time, errors that are primarily committed due to the entry errors (Gelman Hill, 2006). Again, there is diverse process based on the validation of data in which the data can be rejected right at the entry level. However, the procedures of data cleansing mainly involve removal of diverse errors and validation of data along with correction of different values. As rightly indicated by Crawley (2002), different patterns of cleansing the data refers to the need for scrutinizing the validated set of data together with the addition of the information. This process of data cleansing also depends of actions such as the harmonization as well as standardization. Methods for Analysis The applicable method is that of the regression analysis that in turn can be used for approximation of the associations. The current procedure comprises of different techniques that is essentially based on management of different types of associations between diverse variables (Gelman Hill, 2006). However, several techniques can be used for modelling as well as evaluation of the nature of association in the regression analysis. The regression analysis is primarily founded on the different dependent and at the same time independent interpreter. Therefore, the regression analysis also calls for the need of concentrating on different criteria in which the analysis is mainly based on the management of conditional anticipations (Crawley, 2002). Again, the regression analysis also has the need for enumeration of the mean value with the identified fixed variable. Furthermore, the parameters are also set for the purpose of distribution that is entirely dependent on different functions that can target for regression analysis. In addition to this, different patterns can also characterize the differences in the variables that are founded on probability distribution. Again, in regression analysis, there is a wide possibility of future forecast and forecasting procedures (Hair, 2010). As such, the regression analysis, is therefore, based on management of the ground of machine learning. The understanding of the regression analysis is fundamentally based on exploration of different types of association by different independent variables. The present type of analysis thereby includes the causal firm of association. The techniques mainly relates to the ordinary read square method and linear progression method, which are completely parametric. If there is a need to relate the approach which have been used , the performance is made on the basis of the process of data generating. This would enable the system to work on the responsive variables and on the responses of the effects of the observations. There is a need to continue the research in that area, as the patterns are metric regressions. The basis of regression is on the parameters, which include different types of non-parametric regressions and different types of missing data (Gelman et al, 2006). The different underlying operations that are working on the measurement of the model pattern with no errors would be included in this. The variable covariance and the error seem to be uncorrelated. The setup is done on the basis of the parameters which seems to be efficient, consistent and completely unbiased. The data, which is used for anaklysis a nd work on the assumption, is not required. However, the assumptions used needs to based on the usefulness of the model and the methodology and it has to be of the statistical analysis. The variable has been identified with the trend of spatial autocorrelation. Data Set The dataset has some missing values, which has been filtered out using Pivot Tables and Pivot Charts in Excel. The missing values has been found in the following columns of the data set: 1. First Seen Clinician Date 2. First Seen Clinician Time 3. First Seen Nurse Time 4. First Seen Nurse Date These values has been cleansed using Pivot Chart. The cells with blanks have been omitted from data analysis. The final data so obtained has been exported into another Excel sheet, on which the analysis can be performed. The pivot chart of obtained is: Reference Gelman, A., Hill, J. (2006).Data analysis using regression and multilevel/hierarchical models. Cambridge University Press. Crawley, M. J. (2002). Statistical computing: An introduction to data analysis using. Liang, K. Y., Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models.Biometrika,73(1), 13-22. Hair, J. F. (2010).Multivariate data analysis. Pearson College Division. Agresti, A., Kateri, M. (2011).Categorical data analysis(pp. 206-208). Springer Berlin Heidelberg.

Memory Improvement Strategies free essay sample

Memory Improvement strategies are split into two main groups which are organisation and mnemonics. Organisation this is when you categorise things into groups to aid memory recall. One method of organisation could be to construct a mind map. Research showing that organisation aids memory recall†¦ * Shuell two groups of participants were presented with a list of words. For one group the words were in a random order, whereas for the other group they were arranged under categories. After looking at the list, paticipants were asked to recall as many words as they could remember. * Participants who had the organised lists recalled significantly more words than participants who had random lists. * Mandler participants were given 52 cards with a randomly selected word on each card, they were then asked to sort the cards into between 2 – 7 categories of their choice. They were then asked to recall as many words as they could remember. The more categories the participants had used, the higher their recall of words. We will write a custom essay sample on Memory Improvement Strategies or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Research showing that organisation does not aid memory recall†¦ * Deese participants were presented with lists of associated words for example: thread, pin, eye, thimble, sewing, sharp. The participants were then asked to recall the words. * Deese found that a non-presented word such as needle was recalled by a large proportion of participants because they had organised it into a group with the words that had been presented. Mnemonic Techniques artificial methods used to enhance people’s memory involving providing a structure so that even random material can be organised effectively at the time of learning and can then be easily recalled. The following are some mnemonic techniques†¦ Method of Loci used to help remember a list of unrelated words. * You would think of a route you take regularly and you would then associate the words to locations on the route. * For example, one word might be eagle and on your route you may see a post box. You could imagine the eagle sitting on the post box. Research showing that the method of loci aids memory recall was carried out by†¦ * Bower asked participants to recall five lists of 20 nouns, one group used the method of loci and one did not. * The group using the method of loci recalled 72% of the nouns compared with only 28% in the group that did not use the method of loci. Story Method used to remember a list of unrelated words by linking them together within the context of a story.Research showing that the story method aids memory recall was carried out by†¦ * Bower and Clark they gave participants 12 lists of 10 nouns to recall, one group used the story method whereas the other group did not. * The group who used the story method recalled 93% of the nouns compared to only 13% in the other group. Face-name System helps to remember names. * You have to think of an image linked to the name of the person and link this image to a prominent feature on the person’s face. * For example – if someone was called Rachel Egginton you could imagine her with an egg on her head. Evaluation Strength all the strategies are effective in improving memory because they capitalise on our previous knowledge. Strength – organisation aids memory recall because it helps to chunk information together into categories and we cannot remember more than 7 chunks of information at any one time. Strength the face-name system has an application to the real world in that teachers often have to remember the names of many pupils. Weakness it is not clear which previous knowledge is used and why that particular knowledge is used. Weakness the methods often lack ecological validity – people do not have to remember a list of unrelated words in real life. Weakness – the techniques cannot be applied to more complex learning, for example understanding explanations of schizophrenia.