how imperative emotional intelligence and personality is in achieving success,

jimmy-petruzzi

 

Research by Eby, Adam, Russell & Gaby, (2000) demonstrated how imperative emotional intelligence and personality is in achieving success, significantly stating how people adapt to the environment, and obtain goals.

According to Carmeli (2003) senior managers with high levels of emotional intelligence develop more positive attributions, with the ability to focus on controllable factors, increased awareness and empathy for staff, and achieve more  positive outcomes, also senior managers with high levels of emotional intelligence are able to segregate wok and home life more effectively.

Research by Schulte et al. (2004) suggested that emotional intelligence is not a unique concept, their research suggests that other variable’s such as personality traits, IQ, cognitive Intelligence, and gender contributed a significant variance in the scoring of emotional intelligence, which would suggest limitations EI tests in identifying the correlation between Emotional intelligence and career success.

According to Conte (2005) the self-reporting measure of emotional intelligence testing could impact the reliability and validity of results, suggesting if someone is partaking in an EI test for a specific purpose they may cognitively think through an appropriate answer. Which would suggest separating the mesh of other variables, and personality traits with key aspects of emotional intelligence is a challenge; suggesting  a close relation between the big 5 and emotional intelligence.

Salovey & Mayer, (1990) who first introduced the concept of EI in 1990, define EI as how people express emotion, regulate, and adapt the utilisation of emotions within application to tasks and problem solving, and suggest EI can be improved. Unlike personality traits that are fixed, if someone is neurotic at 15 years of age they are likely to be neurotic at 50.

Although  it is important to note different researchers describe emotional intelligence in different ways for example Goleman (2000) describes EI as self-awareness and perception of others, Martinez & Alda, (2005) defines EI as an extraction of non-cognitive skills and one’s ability to deal with external pressure. Emotional Intelligence like personality Traits can be difficult to define as they are an abstract concept, although Emotional intelligence is comparatively new, and does not have the scientific research base  as the FFM.

Research by Zadal (2004) examined the correlation between emotional intelligence and personality traits using the Goleman’s inventory test, which demonstrated a correlation between emotional intelligence and extraversion. Consistent with other research, extraversion appears to appear predominantly linked to emotional intelligence. Extraversion also has a strong correlation to performance.

Research by Williams, Myerson, & Hale (2008) suggests a person’s ability to process information has a correlation with behaviour, which affirms that individual differences play a role in an individual’s behaviour, although what is not clear is the correlation, context, consistency.

Conclusion

According to Pekaar, van der Linden,Bakker & Born (2017) There is  a correlation of Emotional intelligence tests  and individual’s performance at work. The research suggests emotional intelligence is a combination of individual differences and has a correlation to personality and several other variables in performance, emotional intelligence plays a bigger role in success in certain occupations. Though it is likely to be a correlation rather than a causation.

 

References:

Carmeli, A. (2003). The relationship between emotional intelligence and work attitudes, behaviour and outcomes: An examination among senior managers. Journal of Managerial Psychology, 18, (8): 788-813

  1. Cattell, R. (1943). The measurement of adult intelligence. Psychological Bulletin. 40. 153-193. 10.1037/h0059973.

 

Conte, J. M. (2005). A review and critique of emotional intelligence measures.

Journal of Organizational Behavior, 26 , 433–440.

Conte, J. M. (2005). A review and critique of emotional intelligence measures. Journal of Organizational Behavior, 26 , 433–440.

Ebby, L.Y, Adam, D.M, Rusell, J.E.A. & Gaby, S.H. (2000). Perceptions of organization readiness for change: factor related to employees’ reactions to the implementation of teambases selling.

Goleman, D. (2000). An EI-based theory of performance. In Goleman, D. & Cherniss, C. (Eds.), The Emotionally Intelligent Workplace: How to Select for, Measure, and Improve Emotional Intelligence in Individuals, Groups, and Organizations. San Francisco, CA: Jossey-Bass, pp. 27-44.

 

Schulte MJ, Ree MJ, Carretta TR. Emotional intelligence: not much more than g and personality. Personality Individ Differ 2004;37:1059–68.

 

Joseph, D. L., & Newman, D. A. (2010). Emotional intelligence: An integrative meta-analysis and cascading model. Journal of Applied Psychology, 95(1), 54-78.

http://dx.doi.org/10.1037/a0017286

Keri A. Pekaar, Dimitri van der Linden, Arnold B. Bakker & Marise Ph. Born (2017) Emotional intelligence and job performance: The role of enactment and focus on others’ emotions, Human Performance, 30:2-3,135-153, DOI: 10.1080/08959285.2017.1332630

Martınez-Miranda, J., & Aldea, A. (2005) Emotions in human and artificial intelligence. Computers in Human Behavior, 21(2), 323-341.

Salovey, P., & Mayer, J. (1990). Emotional intelligence. Imagination, Cognition, and Personality, 9, 185–211

Williams, B., Myerson, J., & Hale, S. (2008). Individual Differences, Intelligence, and Behavior Analysis. Journal of the Experimental Analysis of Behavior90(2), 219–231. http://doi.org/10.1901/jeab.2008.90-219

 

Zadel, A.(2004). Impact of personality and emotional intelligence on successful training in competences. Managing Global Transitions, 4(4), 363-376.Psychology,19 (2),88-110.

Factor Analysis of Personality Data Performing the Factor Analysis, Methodology, and Results

Module Project: Factor Analysis of Personality Data

Performing the Factor Analysis, Methodology, and Results

 

Name: Jimmy Petruzzi

jimmy petruzzi

MSC Mental health psychology  LPSY-302-5

LPSY 316: Personality, Individual Differences, and Intelligence

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

METHOD

 

Participants:

Participants included 1006 people aged from 9 years of age to 12 years of age. Equal distribution of male and females, residing in the Nunavut state in  Canada. In accordance to the Canada School of Public Service Act, primary and secondary school attendance in Nunavut state is compulsory, part of the selection criteria was based on levels of school attendance. Participants were excluded if they had above unauthorised absenteeism above legal school attendance.

Participants parents and caregivers  were requested to   provide information on employment status in order for the questionnaire participation to reflect society to increase  social validity.

Design

In designing the questionnaire we took into consideration the variable factors   which can impact the reliability of a participation response, factors such as interpretation of the question, the participants cognitive ability and motivation during the test.

The following research Gurven, Rueden, Massenkoff,Kaplan, Vie,(2013) indicated limitations of the FFM when administered amongst a rural community in Bolivia, the questionnaire was translated, though it appeared literacy was amongst the correlating factors impacting the reliability of the questionnaire, the significance  of this research was the FFM has been demonstrated to be more reliable in developed nations, whilst this is not a direct  correlating factor amongst our participants in the Intuit community  it did suggest taking  this and previous research into consideration  about adapting the FFM  for  the adolescents participants.

We produced a modified sample questionnaire, parent or carer consent was authorised to conduct   an initial modified version of the questionnaire.  The questionnaire was trialled amongst 124 participants, equal distribution of male and female, the age ranges between 9 years of age to 12 year of age. Upon modification  and administration  of the  modified  Questionnaire and an evaluation of the data. A 25-item questionnaire, Costa and McCrae (1992) was developed to measure the Big Five personality factors in Inuit children in Canada.

Five items were taken  from each of the five IPIP scales that measure the Big Five personality factors, adapting them where necessary so that they would be relevant to the lives and cognitive abilities of 9-year-old to 12-year-old Inuit children and translating them into the Inuktitut language.

Materials:

Using the FFM Inventory developed by Costa and McCrae (1992), as a platform to develop  a  25- Item questionnaire procured  from  the IPIP,  utilising  a five point  Likert scale.

The participants completed the questionnaire with paper and black pen, SPSS program and software was used to   implement   the, Factor Analysis and PCA, the parallel analysis implemented with the e O’Connor learning resource

Procedure:

Informed consent was compulsory from parents and caregivers of the participants as the participants age range was 9 years of age to 12 years of age. The questionnaire was administered to the participants on the 23rd of June 2018 at 930am on commencement of the morning classroom lesson, the test was conducted in the school classrooms and the young people remained anonymous. The participants were instructed not to discuss questions with other participants, any questions could be discussed with study supervisors which could also speak intuit language

The duration for completion of the questionnaire, was in line with the duration of the compulsory education morning framework in Canada, which is approx. 2 hours and 30 minutes, as the questionnaire was designated during academic term time, minimal disruption to designated lesson, students returned to the usual class on completion.

The content of the questionnaire was the same for all participants, research by  Goldberg (2001) suggests the FFM can be adapted successfully  for the administration to suit the cognitive abilities of the participant’s, in accordance and consideration of research by Goldberg (2001) the questionnaire was adapted to suit the cognitive abilities of the participants and translated  from English into Intuit language to ensure  increased validity and reliability in measurements.

 

 

 

 

 

 

 

 

Results

The object of the PCA was to Identify the eigenvalues, there after utilizing the eigenvalues  to conduct  a scree test.  We also conducted  the Kaiser-Guttman test to define the  number of factors, although we were uncertain about the accuracy of the data. We proceeded in  utilizing  the O’Connor resource and conducted a parallel analysis ( which can be referred to in the  appendix)

Once we identified the number of factors using the  O’Connor resource, based on the number of factors we had discovered in our data, we were able to conduct a Factor Analysis with Oblique Rotation using oblimin  utilising SPSS software ( the SPSS data can be referred to in the appendix)

Having conducted the PCA, the data indicated the first 6 components having an eigenvalue > 1.0.

The Mean eigenvalues we identified as 1.297, 1.252, 1.217, 1.189, 1.163, 1.137 see Table 2: below

Extraction Method: Principal Component Analysis.

Factor Critical value eigenvalue Variance % Cumulative %
1 1.297 2.160 8.641 8.641
2 1.252 2.114 8.456 17.098
3 1.221 1.893 7.571 24.668
4 1.189 1.703 6.810 31.478
5 1.163 1.642 6.569 38.047
6 1.137 1.064 4.258 42.305
         

Table 2: Parallel-test of Eigenvalues

Having identified the list of eigenvalues we performed a scree test.

Figure-02: Scree-plot of unrotated PCA-test (please see index for scree plot diagram )

According to Field (2013) the inflection point of retaining factors is above the curve which can be identified on the Scree plot graph. The point of inflexion is where the slope of the line changes dramatically, our findings demonstrated  six factor-items loading  at point of inflection at component 7,  amongst components of the IPIP-25-item questionnaire  2.160,2.114,1.893,1.703,1.642  please see Table-03:

According to Stevens (2002) the Scree plot is relatively reliable in providing data for factor selection in samples of 200 participants or more, we had 1006 sample so we were confident in our data collected factor selection.

Rotated Principle analysis

Initial eigenvalues                                                      Loadings rotations

Factor total cumulative% total cumulative% total
1 2.160 8.641 1.411 5.642 1.375
2 2.114 17.098 1.370 11.121 1.359
3 1.893 24.668 1.131 15.647 1.100
4 1.703 31.478 .960 19.487 1.054
5 1.642 38.047 .866 22.952 .878
           
           

Table-03: PAF- Analysis test results

The factor rotation technique was used to differentiate between factors to interpret factor variable low and high loading with reliable extraction of data.

Upon establishing the data we ran a unrotated PCA to assist us in omitting the number of factor loads from the IPIP- 25 item questionnaire.

According to Horn (1965) we retain the factors that are higher from the research data, than the corresponding data which is run randomly.

Research by Fabrigar, & Wegener,(2012) was significant in our decision to utilised an oblique rotation method, we were able to identify the highest factor loadings see Figure 4:

According to  Cooper (2010), if we  use an oblique rotation, we should analyse the  Pattern matrix a table ( please see index)  the table enabled us to identify the factor for each item which has the highest loadings The items we analysed had correlations of  .4 and  higher, If they are related to each other, this means there are factors underlying the items and data.

 

 

 

 

 

Factor Analysis with Oblique Rotation

Factor            
1:  Conscientiousness 2 (.524) 13 (-507) 17 (.506) 19 (.543) 22 (.519)  
2:  Neuroticism 5 (.430) 8 (.520) 11 (.492) 16 (.507) 24 (.476) 15 (-.407)
3: Extroversion 4 (.385) 6 (-.520) 14 (.476) 20(.404) 25 (.524)  
4: openness 3(.535) 10 (.552) 23 (.508) 21 (.423)    
5:agreeableness 1 (.448) 7(.448) 12 (.408) 18 (.535)    
             

Figure 4: Relationships between factors

We identified that 25 items questionnaire had successfully measured, by the five factors we expected. After analysing the results we were able to determine, factor 1 Conscientiousness  had a correlation  With 2 ,13 ,17 ,19 ,22,  Factor 2 neuroticism  had a correlation with 5,8,11,16,24,15 ,Factor 3 had a correlation with 4,6 ,14,20,25 ,Factor 4  openness had a correlation with 3,10,23,21,factor  5 had a correlation with 1,7,12 ,18

According to Cooper (2010) a minus sign indicates a negative correlation. We were able to establish item 13 belonging to factor 1 the consciousness group had a negative value of   (-507) which would indicate a question that was reverse scored. By conducting the analysis we were able to identify the responses that the participants had given to each item our specifically designed 25 questionnaire personality test.

Other Items which indicated a negative value were  6 (-.520) in the factor  3 group : Extroversion and 15  in the factor 2 group : Neuroticism (-.407) which also indicates reverse scoring questions,we were able to establish factor  2:  Neuroticism  had the highest level of responses from participants. We also established that  factor 1: consciousness  had each item scored >.5.

The item  16 (.507) loaded onto factor 2 instead of the expected factor 5,  (please see in the index)

And item 9 was omitted because it was  loaded onto factor  6, which did not feature on the PA test.

Using the following methods Kaiser-Meyer-Olkin and The Bartlett’s Test of Sphericity

The results from the Kaiser-Meyer-Olkin test demonstrated a value of 0.703  according to Field (2013) the minimum level is 0.6>x

The Bartlett’s Test of Sphericity  result  was  (df 300)= ( 2152.769,  p< 0.005)

(statistics table  in Appendix)

 

 

 

 

 

 

 

Word count 1498 total

Results 871

 

References:

Arthurs N et al (2014) Achievement for Students Who are Persistently Absent: Missing School, Missing Out? Urban Review. Dec2014, Vol. 46 Issue 5, p860-876. 17p.(Abstract only)

Cooper, C. (2010). Individual differences and personality (3rd ed.). London: Hodder Education. Retrieved from http://cw.tandf.co.uk/psychology/individual-differences-and-personality/

Statistics Canada, Education in Canada: A Statistical Review, Ottawa, 1973-2000.

Costa, P. T., & McCrae, R. R. (1992). NEO-PI(R) professional manual. Odessa, FL: Psychological Assessment Resources.

Fabrigar, L. R., & Wegener, D. T. (2012). Exploratory factor analysis. [electronic book]. Oxford; Oxford University PressChapter 17: “Exploratory factor analysis”

Field, A. (2013). Discovering Statistics using IBM SPSS statistics (4th Eds). UK: Sage Publication.

Gurven, M., von Rueden, C., Massenkoff, M., Kaplan, H., & Vie, M. L. (2013). How Universal Is the Big Five? Testing the Five-Factor Model of Personality Variation Among Forager–Farmers in the Bolivian Amazon. Journal of Personality and Social Psychology104(2), 354–370. http://doi.org/10.1037/a0030841

Horn, J. L. (1965), “A Rationale and Test For the Number of Factors in Factor Analysis,” Psychometrika, 30, 179-85.

Jolliffe, I. (1986). Principal Component Analysis. Springer Verlag.

Parallel Analysis. Retrieved from https://analytics.gonzaga.edu/parallelengine/

Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4th ed.). Hillsdale, NS: Erlbaum.

 

 

 

 

 

 

 

 

 

 

 

Appendix:

 

6 scores greater than 1

 

Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 2.160 8.641 8.641 2.160 8.641 8.641
2 2.114 8.456 17.098 2.114 8.456 17.098
3 1.893 7.571 24.668 1.893 7.571 24.668
4 1.703 6.810 31.478 1.703 6.810 31.478
5 1.642 6.569 38.047 1.642 6.569 38.047
6 1.064 4.258 42.305 1.064 4.258 42.305
7 .932 3.730 46.035      
8 .907 3.626 49.661      
9 .881 3.525 53.186      
10 .866 3.462 56.648      
11 .850 3.399 60.047      
12 .828 3.312 63.359      
13 .817 3.268 66.628      
14 .805 3.221 69.848      
15 .769 3.074 72.923      
16 .756 3.025 75.948      
17 .740 2.960 78.908      
18 .727 2.907 81.814      
19 .706 2.825 84.639      
20 .684 2.734 87.373      
21 .678 2.714 90.087      
22 .668 2.673 92.760      
23 .633 2.533 95.292      
24 .596 2.383 97.676      
25 .581 2.324 100.000      
Extraction Method: Principal Component Analysis.

 

 

The factors that are above the curve ( inflection point of retaining factors

 

 

 

 

 

 

 

 

According to Horn (1965) we retain the factors that are higher from the research data, than the corresponding data which is run randomly

Component or Factor Mean Eigenvalue Percentile Eigenvalue
1 1.297120 1.335867
2 1.252433 1.283924
3 1.217893 1.246695
4 1.189013 1.212341
5 1.162260 1.184262
6 1.138026 1.159807
7 1.115636 1.137627
8 1.093496 1.114383
9 1.071495 1.088771
10 1.051154 1.068740
11 1.030922 1.048493
12 1.011218 1.027736
13 0.991996 1.008935
14 0.973965 0.989698
15 0.955395 0.970783
16 0.936674 0.953473
17 0.917567 0.934328
18 0.898220 0.915141
19 0.879027 0.896027
20 0.859719 0.877748
21 0.838560 0.856029
22 0.817029 0.835840
23 0.795043 0.814159
24 0.769624 0.792291
25 0.736516 0.765227

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .703
Bartlett’s Test of Sphericity Approx. Chi-Square 2152.769
df 300
Sig. .000

 

 

 

Factor

Items

Extraversion

4, 6, 14, 20, 25

Neuroticism

5, 8, 11, 15, 24

Openness

3, 9, 10, 21, 23

Agreeableness

1, 7, 12, 16, 18

Conscientiousness

2, 13, 17, 19, 22

 

 

 

 

 

 

Pattern Matrixa
  Factor
1 2 3 4 5
19 .543        
2 .524        
22 .519        
13 -.507        
17 .506        
8   .520      
16   .507      
11   .492      
24   .476      
5   .430      
15   -.407      
25     .524    
6     -.520    
14     .476    
20     .404    
4     .385    
9          
10       .552  
3       .535  
23       .508  
21       .423  
18         .535
1         .448
7         .448
12         .408
Extraction Method: Principal Axis Factoring.

Rotation Method: Oblimin with Kaiser Normalization.

a. Rotation converged in 4 iterations.

 

Eysenck and Freud personality traits

 

 

 

 

Eysenck and Freud personality traits

 

by Jimmy Petruzzi

jimmy petruzzi

According to Eysenck (1967) we are born with a unique temperament with a genetic basis, and personality can be measured by dimensions on a continuum. The dimensions labelled by Eysenck consist of, extraversion/introversion, neurotic/stable, and psychotic each dimension has specific traits or characteristic’s related to them.

Freud (1923) Suggested our personality is governed by our unconscious thoughts, Freud’s theory involves  what he termed the id, which is based on biological drive and desire for instant gratification which develops  up to the age of 2 or 3, then the ego is developed through external experiences, the ego distinguishes boundaries of rationality and is developed up to around 5 years of age, then modification of the ego to super ego , the super ego sculpted by experiences  develops a sense of morality. The theory is based on sexual drives, based on stages of development influencing a person’s response to stimulus represents aspects of the development of personality.

Eysenck (1967) suggested that people on the high end of the scale of extraversion are searching for stimulus due to lower levels of brain activity and the opposite is for people who are considered to be introverts.

Research by Green (1984) demonstrated how a group of introverts and extroverts participated in a mundane task, the extroverts had chosen a higher volume of music whilst participating in the task comparatively to the introverts, under their chosen music volume levels both groups had displayed task efficiency. Interestingly when the volume levels were swapped around amongst the two groups’ task efficiency had been less effective.

According to the following research Mitchell & Kumari (2016)Using MRI and DTI and assessing the evidence over the past 15 years , were able to establish  Eysenck’s  theory, examining  extraversion and introversion  had a correlation to the function of different brain regions Including cortical regions  involved in  emotion regulation including limbic regions. Suggesting neuroticism is particularly sensitive to negative emotional cues and extraversion is sensitive to positive emotional cues.

According to Eysenck (1986) he implies Freud’s theories can be classed as science and can be falsified, which a different view is than Popper (1986) who suggests Freud’s theories cannot be falsified therefore not considered scientific.

Lo, Hinds,Tung, Franz, Fan,Wang, and Chen (2017) identified genetic spectrum of correlations between certain genes and FFM personality traits.

Twin studies (Hur, 2007) demonstrates how identical  twins are more likely to  demonstrate  similar  personality traits, compared  to  fraternal twins, and biological siblings are more likely to have similar personality traits then adopted sibling’s, this is significant because it suggests that a biological foundation to personality.

According to Laurent (2015) it is Impossible to eliminate the genetic variable which could potentially correlate with Child development correlation with adult development. The empirical research around Freud’s development theory is limited subjective experience.

Revelle, 2016 suggested Eysenck’s has left a strong legacy and influence field of psychology for example the neuroticism and extraversion of the FFM

Empirical and theoretically Eysenck theories on personality are more plausible than Freud. The evidence around biology around personality is overwhelming

 

References:

 

Eysenck, H. J. (1986). Failure of treatment–failure of theory? Behavioral and Brain Sciences, 9, 236.

Eysenck, 1967 H.J. Eysenck The biological basis of personality Thomas, Springfield, IL (1967)

 

Eysenck H J. The effects of psychotherapy: an evaluation. J. Consult. Clin. Psychol. 16:319-24, 1952. [Inst. Psychiatry, Maudsley Hosp., Univ. London, London, England]

The Effects of Psychotherapy: An Evaluation H. J. Eysenck (1952) Institute of Psychiatry, Maudsley Hospital University of London First published in Journal of Consulting Psychology16, 319-324.

Freud, S. (1923). The ego and the id. SE, 19: 1-66.

Hur, Y. (2007). Evidence for Nonadditive Genetic Effects on Eysenck Personality Scales in South Korean Twins. Twin Research And Human Genetics, (2), 373.

 

Lo, M.-T., Hinds, D. A., Tung, J. Y., Franz, C., Fan, C.-C., Wang, Y., … Chen, C.-H. (2017). Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nature Genetics49(1), 152–156. http://doi.org/10.1038/ng.3736

Mitchell, R. L. C., & Kumari, V. (2016). Hans Eysenck’s interface between the brain and personality: Modern evidence on the cognitive neuroscience of personality. Personality and Individual Differences, 74-81. DOI: 10.1016/j.paid.2016.04.009

Popper, K. (1986). Predicting overt  behavior versus predicting hidden states. Behavioral and Brain Sciences, 9, 254-255.

Plomin, R., DeFries, J. C., McClearn, G. E., & Rutter, M. (1997). Behavioral genetics (3rd. ed.). New York: Freeman.

Revelle, W. (2016). Hans Eysenck: Personality theorist. Personality & Individual Differences, 10332-39. doi:10.1016/j.paid.2016.04.007

Effects of mindfulness training on different components of impulsivity in borderline personality disorder

Title

Effects of mindfulness training on different components of impulsivity in borderline personality disorder

Author

Jimmy Petruzzi

jimmy petruzzi

Selected articles for comparison

Elices, M., Soler, J., Feliu-Soler, A., Carmona, C., Tiana, T., Pascual, J. C., … Álvarez, E. (2017). Combining emotion regulation and mindfulness skills for preventing depression relapse: a randomized-controlled study. Borderline Personality Disorder and Emotion Dysregulation, 4, 13. http://doi.org/10.1186/s40479-017-0064-6

Soler J, Valdepérez A, Feliu-Soler A, Pascual JC, Portella MJ, Martín-Blanco A, et al. Effects of the dialectical behavioral therapy-mindfulness module on attention in patients with borderline personality disorder. Behav Res Ther. 2012;50:150–7

Methodology

Elices, M., Soler, J., Feliu-Soler, A., Carmona, C., Tiana, T., Pascual, J. C., … Álvarez, E. (2017) conducted a pilot study the application of mindfulness with patients who had a diagnosis of BPD, the study took into consideration that mindfulness  training could assist in the modification of facets of impulsivity in patients with BPD. The method for the research  was  64 subjects with BPD diagnosis were  Subjected to 10 weeks of mindfulness training, with all participants being assessed pre and post intervention measuring impulsivity and neuropsychological tasks comparing the effects of MT and IE on borderline symptons with research randomization software to ensure reliability and validity.Interviews were conducted by skilled professionals with out prior knowledge of participants who were separated in groups of 8, the patients were recruited from a out patient psychiatry unit and of 92 screened participants 64 were randomised 32 to each treatment protocol, patients were selected with a strict quality control procedure

 

According to Behaviour Research and Therapy Soler J, Valdepérez A, Feliu-Soler A, Pascual JC, Portella MJ, Martín-Blanco A, Alvarez E, Pérez V Behav Res Ther. 2012 Feb; 50(2):150-7  there have been measurable improvements in patients with BPD utilising mindfulness in adverse to controlled interventions measured by the  (CPT-II)

this study was conducted with patients receiving treatment along side psychiatric treatment 60 patients we recruited for this study all of them having BPD, 40 of the patients received  DBT Mindfulness and Psychiatric treatment and 20 psychiatric treatment alone,based on the CPT-II neuropsychological test the more participation of mindfulness the more improvement of psychiatric symptons , in order to be recruited the patients had to meet a BPD diagnostic criteria and recruitment was from a psychiatric hospital , the interviews consisted of two semi structured interviews , the interviews and evaluations were conducted by experienced psychologists and psychiatrists , the main variable  assessed by CPT-II, psychopathological symptom’s assessed pre and post interventions using  HRSD-17, BPRS and  POMS; mindfulness questionnaire’s were given to the patients pre and post intervention  FFMQ and (EQ,

One of the themes of DBT is utilising psycho education, teaching and making patients aware of their condition. Mindfulness is a central part of DBT, cultivating an attitude of acceptance, not resignation, though acceptance, the situation is as it is, or the feeling, then taking responsibility to change. As it stands DBT is the treatment with the most empirical evidence for Border line Personality Disorder. Due to the high impulsivity of patients with BPD utilising mindfulness could potentially help the patients become more aware and not react as impulsive.

According to the results of the following study  Jessica R. Peters, Shannon M. Erisman, Brian T. Upton, Ruth A. Baer, Lizabeth Roemer. (2011): 228-235.Mindfulness has the potential to help with impulsivity and maladaptive behaviour due to impulsivity and aspect of patients with BPD is impulsivity.

 

I think the study provided some interesting findings and has the potential to be further developed, the researchers took into consideration whether some of the patient had exposure to mindfulness skills before, they also took into consideration which set of skills the patients had exposure to first and how that would correlate to DBT skills training, that said a core component of DBT is mindfulness and it is likely that patients with DBT will have had exposure to mindfulness skills prior to the participation. I think one of the biggest challenges is to construct a reliable model for testing impulsivity in BPD, it appears the researchers did extremely well  to  construct a multi model of assessment and in contrast to a former study of the group  Soler J, Valdepérez A, Feliu-Soler A, Pascual JC, Portella MJ, Martín-Blanco A, Alvarez E, Pérez V Behav Res Ther. 2012 Feb; 50(2):150-7

The improvement ratio differed in the areas of inhibition, the authors point out that might have been due to other variable factors such as the  co-morbidities differential’s between bulimia nervosa ADHD , the authors explain this is why the response  delay of gratification was improved and response inhibition. Though it is important to note ADHD was not directly assesses and going off the   CPT-II only a relative small proportion of the group 14 percent had 70 percent ADHD symptomology, I think it is important that future studies do incorporate variable BPD profiles which factor in the co-morbidities ratios and in the way the MT is applied

  1. Giluk, (2009)has demonstrated correlations between mindfulness and the impulsive aspect of DBT, though to date there is not much evidence to support mindfulness interventions on impulsivity related to BPD. I think one of the challenges of both studies would be the spectrum of low attention deficit in patients with DBT and the ability to partake and sustain a program of mindfulness which has a core base of awareness and being present.

Lars Schulze,(2016) conducted a study from a biological perspective of patients from BPD , pooling data from 19 published studies, and they reported structural differences in the Amygdala and pre frontal cortex, these are important findings because, it would suggest that depending on the severity and spectrum of biologically differences each person is going to respond in a different way. Hence I believe it is a challenge to monitor the effects of mindfulness on patients diagnosed with DBT,

Though I think with the benefit of technology, if a study could be conducted to assess structural changes to the brain, this may be a more accurate way to monitor improvements from treatment.

 

 

 

References

Elices, M., Soler, J., Feliu-Soler, A., Carmona, C., Tiana, T., Pascual, J. C., … Álvarez, E. (2017). Combining emotion regulation and mindfulness skills for preventing depression relapse: a randomized-controlled study. Borderline Personality Disorder and Emotion Dysregulation, 4, 13. http://doi.org/10.1186/s40479-017-0064-6

Jessica R. Peters, Shannon M. Erisman, Brian T. Upton, Ruth A. Baer, Lizabeth Roemer. (2011) A Preliminary Investigation of the Relationships Between Dispositional Mindfulness and Impulsivity. Mindfulness 2:4, 228-235.

Lars Schulze, Christian Schmahl, Inga Niedtfeld. Neural Correlates of Disturbed Emotion Processing in Borderline Personality Disorder: A Multimodal Meta-Analysis. Biological Psychiatry, 2016; 79 (2): 97 DOI: 10.1016/j.biopsych.2015.03.027

Soler J, Valdepérez A, Feliu-Soler A, Pascual JC, Portella MJ, Martín-Blanco A, et al. Effects of the dialectical behavioral therapy-mindfulness module on attention in patients with borderline personality disorder. Behav Res Ther. 2012;50:150–7

 

Tamara L. Giluk , Personality and Individual Differences. Mindfulness, Big Five personality, and affect: A meta-analysis  Dec 2009, Vol. 47, No. 8: 805-811

Conduct an appropriate analysis to determine, what, if anything, predicts self-efficacy in Statistics.

Assignment 8

 

In our week 8 assignment the objective is to Conduct an appropriate analysis to determine, what, if anything, predicts self-efficacy in Statistics.

According to Bandura (1997) there is a correlation between our beliefs and completing a specific task, suggesting self-efficacy plays a major role and is highly influential on a person’s ability to achieve a goal, Bandura discussed a relationship between variable factors and four key principles which play a major role in accomplishment.  Though is it the chicken or the egg, is it a question of competence or confidence. If I am competent in a task I am more likely to be confident. And believe I can do it. If I am not competent I am more likely to be less confident. Bandura ( 1997) did suggest even before we take any action there is a correlation of key principles. Though other factors to take into consideration are past experience and the link between past experiences and present learning. So for example if I am a competent driver, and I am travelling to a new destination I may be a be a bit apprehensive about my journey, though once I have travelled the journey on a few occasions it becomes much easier and I become more confident. Equally if a learner was going to start driving lessons, though had never been in a car before, it would be a far greater challenge one would think than if he or she had travelled on a car. One could also suggest past schemas play a role in self-efficacy, say for example you had built a negative association to delivering  presentations in your youth, this may manifest late in life. So one may have the competence, though not the confidence. Though there is a fine line between confidence and competence, and our ability to learn something new is a correlation to many variable factors, including how the information is presented and our learning style.

 

Looking at previous research of predictors in self-efficacy and academia, by exploring  the following research by Hall and Ponton,2002. We can see how in this comparative design study of two groups of students, briefly the study explores self-efficacy in maths, utilising a psychometric  test designed to measure self-efficacy in maths, the study explores different levels in maths. Notably  Algebra and calculus. Using an independent T Test the research found that the higher level maths group calculus, had a high degree of self-efficacy than the lower level maths group. So if we take Banduras ( 1997) theory into consideration it would suggest self-efficacy is linked to achievement. Though whilst the research did find some interesting correlations. Some of the things to take into consideration is the motivation to succeed, and the bigger picture, was the subject a component of additional study or course. Say for example in the mental health psychology course we cover statistics, having a certain level of knowledge of statistics is important, though not everyone who is doing the course will want to be a statistics teacher, given as such would require a different level of knowledge than if someone was choosing a different career path in the field, equally there are different components of a subject. And further more  reflecting back on the research the aspirations of the group studying the higher level maths, would play a part in self-efficacy and past experienced, if one had a goal of being an engineer than knowledge of maths would be more significant than if one had aspirations of being a historian.

One of the features  of the  following research by  Tiyuri et al. (2018) was it included  multiple linear regressions to study the impact of self-efficacy and performance in academia, over 300 learners participated in the study, across a broad range of academic levels, from degree to PHD. The group was divided into pretty much an equal split between female and male. The gender  (P = 0.754)  and school P value (P = 0.364) showed non-significant levels, although the psychometric test did not demonstrate a  significant difference based on gender and school, there was a significance difference (r = 0.393, P = 0.0001 in the level of academia amongst  PHD students. Equally in this study one could imply that the students doing the PHD had more motivation than the other students, also it is important to note the correlation between past experiences, prior knowledge. So whilst this research did produce interesting findings of the link between self-efficacy and learning performance, the objective of the study was not to examine variables which correlated to act as a catalyst for what underlies the self-efficacy.

According to Field (2012).in order for testing to be accurate, we must make the assumption the sampling distribution is normal” the implications as such and methodology if there any doubts.

The reason for the analysis in the assignment:

The Rationale

In order to predict self-efficacy in statistics I conducted a standard multiple regression to predict self-efficacy in statistics we explored self-efficacy, confidence, usefulness, gender, attitudes, age, maths and science levels.

Regression analysis was used to determine which amongst the following variables self-efficacy, confidence; usefulness, gender, attitudes, and age correlate with maths and science to form self-efficacy in statistics. The regression helps us to analyse and understand  which is the variables is statistically significance and non-significant and influential in the dependent variable. The spss output helps us to determine correlative factors. And isolate the variables and negative and positive relationships between variables, which factors are down to chance and which factors appear to be significant. For example the opportunity to examine p values and be able to test the hypothesis.

The regression analysis helped to isolate any patterns , and variable factors which colluded to determine  this case the  self-efficacy in statistics.

The reason I chose a Standard multiple regression was because i wanted to  predict the values on     the   research date data establishing the  continuous dependent variable factors  such as ,Confidence, Usefulness ,Male Dominated Field, Tutor Attitudes and self-efficacy the independent variable, as well as the categorical variables such as age and maths and sciences.

The main aims of using the multiple regression in the analysis were to examine and analyse the effect of ,Confidence, Usefulness, Male Dominated Field, Tutor Attitudes  on self-efficacy in statistics. Also the correlation of self-efficacy on  Confidence,Usefulness,Male Dominated Field, Tutor Attitudes  , how does a change in factor impact another.

This in turn gives us an opportunity to gain point estimates, understand and analyse correlative factors and predict further hypothesis.

The task was to identify self-efficacy in Statistics, I ran a multiple regression comparison on the Stats Confidence file provided

I decided to run a Standard multiple regression to predict predicts self-efficacy in Statistics. With the guidance of (SPSSVideoTutor.com 2014) and Dolan, 2018.

The date consisted of the  following variables, Age,  Self Efficacy,Confidence,Usefulness,Male Dominated Field, Tutor Attitudes and grades in grades in Math or Science.

Also important to  note according to lens 2014 a regression analysis cannot be performed on ordinal level data, because maths and science are in this instance ordinal level data, I had to create dummy variables, which is something I really struggled with.

The study is, looks at the relationship between the variables as per above Self Efficacy,Confidence,Usefulness,Male Dominated Field,Tutor Attitudes grades in maths and science by participant’s  to predicting any variables or correlations of variables of self-efficacy in Statistics.

The results of the output the are as follows

Standard multiple regression

R=.568a the value of R Square= .279 it appears the variables are not good predictors of self-efficacy in statistics

The ANOVA significance value of .000b suggests that one of the predictor variables, with this information as per the video Dolan (2018) we can confirm that minimum of one variable predicts self-efficacy in statistics

A multiple regression was run to predict the following variables self-efficacy from

  • Confidence
  • Usefulness
  • Male dominated field
  • Tutor attitudes –
  • Maths
  • Science

Following the ANOVA  output  These variables statistically significant predicated  self-efficacy, F (6, 93)= 7.387, P <.000, R2=.323

 

The Standardized Coefficients for the following variables are

Confidence -.339
Usefulness -.146
Male Dominated Field  -.058
Tutor Attitudes -.132
maths  -.028
Science  -.013

 

 

The significance values are

Confidence .024
Usefulness .221
Male dominated field .529
Tutor attitudes .294
Maths  .755
Science .884

 

It appears that only one variable in the Confidence in the   coefficients with a significance value of  .024, significantly predicts self-efficacy, the rest of the variables in the coefficient are non-significant

The VIF values are

Confidence  3.004
 

Usefulness  1.932

Male dominated field  1.154
Tutor attitudes  2.130
Maths   1.117
science 1.053

All of  the VIF  values are under 4 or 5, according to Dolan ( 2018) if the The VIF values are above 4 or 5, there could potentially be a problem with multi linearity

The Beta values

Confidence  -.339  
Usefulness -.146  
Male dominated field   -.058  
Tutor attitudes  -.132  
Maths   .028  
Science -.013  

 

According to Dolan ( 2018) the higher the beta value, “the higher the impact of the independent variable on the dependent variable”. Interestingly enough, confidence has a beta value of -.339 the highest beta value and  a p value of Confidence .024 the lowest significance value

 

 

 

 

 

 

Anova table

  df F R2 P  
Self-Efficacy 7.387 6 R2=.323 P <.000  
           
           
           
           

Self-Efficacy

Following the ANOVA  output  These variables statistically significant predicated  self-efficacy, F (6, 93)= 7.387, P <.000, R2=.323

A multiple regression table was run to predict self-efficacy from age , which produced the following significance values

    B SE B β t p Sig.    
Confidence       -.339     .024    
Usefulness       .146     .221    
Male dominated field       -.058     .529    
Tutor attitudes  –       -.132     . .294    
Maths Nominal (dummy )     .028     .755    
Science Nominal

( dummy)

    -.013     .884    
Age categorical                
Self-Efficacy Independent variable                
                   

 

 

Conclusion:

Self-efficacy is abstract concept, confidence had a beta value of -.339 the highest beta value and  a p value of Confidence .024 the lowest significance value, though how do you define confidence. And then there is the issue of confidence and competence, Quantitative states  facts for example  maths  and science, Qualitative is more speculative in this instance. Reflecting on my own experience, journey, the module as a whole and this assignment. One of the areas I got bogged down on was the dummy statistics, during which I had a moment of introspection and reflection, I was beginning to think I had come so far in the module and gradually through the attainment of grades and tutorials and assignments I had built a conviction I could get through the module, though more importantly develop my skills around statistics, the challenge I had was not in the understanding though in the technical knowledge around creating the dummy variables, ad all I could think of was the weight of the final project and how a process that is relatively simple to do, could undo all the work I had done. Reflecting on the research papers I had read around the university students and the college students around maths, I was beginning to release self efficacy is a complex area  to define. And whilst agree with some of the  research by Bandura (1997), this assignment suggested the variables work as a cocktail or perfect storm, that collude.  Qualitative Research can be challenging to define,  and quantify, Quantitative data gives us values. Though there is a saying in sport, you do not play on paper, certain data can point you in a certain direction, though what is concise for one person may not be concise for someone else. And how you quantify  Qualitative research can be a challenge in in itself. I think to a point all of the following variables we examine  have an impact on self-efficacy  on some level, although the data points towards confidence, this could be seen as a grey area. As you can start an assignment with to much confidence and that in itself could have negative implication, or you could start with little confidence, though that could grow as you go along. Though not journey is linear, each journey is filled with twists, turns, and permeations.

 

References:

Bandura, A. (1997). Self-Efficacy: The exercise of control. New York, NY: W. H. Freeman.

Field, A. (2012). The linear model (regression part I) [Video file]. Retrieved from https://www.youtube.com/watch?v=LZtPDgoskfI

 

Hall, M., & Ponton, M. (2002). A comparative Analysis of Mathematics Self-Efficacy of Developmental and Non-developmental Freshman Mathematics Students. Presented at the 2002 Meeting of Louisiana/Mississippi Section of the Mathematics Association of America.

 

Laureate Education (Producer). (2014). Weekly lecture notes, Week 8 – Part 1 [Video file]. Baltimore, MD: Author.

Laureate Education (Producer). (2014). Weekly lecture notes, Week 8 – Part 2 [Video file]. Baltimore, MD: Author.

 

SPSSVideoTutor.com (Producer). (n.d.). Multiple regression [Video file]. Deerfield Beach, FL: ConsumerRaters. Retrieved 28 October 2014.

 

Tiyuri, A., Saberi, B., Miri, M., Shahrestanaki, E., Bayat, B. B., & Salehiniya, H. (2018). Research self-efficacy and its relationship with academic performance in postgraduate students of Tehran University of Medical Sciences in 2016. Journal of Education and Health Promotion, 7, 11. http://doi.org/10.4103/jehp.jehp_43_17

Here it is, the track title: Manarola

The beautiful tune is available to download free from the following link

https://itunes.apple.com/gb/podcast/manarola-beautiful-track-by-jennifer-lorelai-lee-jimmy/id579916647?i=1000426035138&mt=2

jennifer lee jimmy petruzzi 1

jennifer lorelai lee

The Track was inspired by reflecting on the life-changing events people experience, and that love, harmony, peace can co-exist in the modern world.: The goal of the track is to capture a sentiment that  a person has on their interpretation of  Buddhist philosophy, that the past  is an illusion and the future is an illusion, the only moment we truly have in the now: The style meets at a juncture of many different genres with the vocalisation of the angelic voice of  British Soprano Jennifer Lorelai Lee shining throughout:

Enjoy:

Love and Light!

jimmy petruzzi

jimmy petruzzi kimberley