PARAMETRIC
STATISTICS
ASSIGNMENT
SUBMITTED BY : VANSHIKA
MADAAN(046)
COURSE : MSc. CLINICAL
PSYCHOLOGY
SUBMITTED TO: DR. NADEEM
LUQMAN
ASSIGNMENT 1
TOPIC: WRITE A RESEARCH ON PERSONALITY TRAITS ,
PSYCHOLOGICAL WELL BEING AND COGNITIVE DISTORTIONS AS
FACTOR OF PSYCHOLOGCAL DISTRESS AMONGST STUDENTS.
Abstract
This study investigates the relationship between personality traits,
psychological well-being, and cognitive distortions in predicting
psychological distress among students. Using standardized questionnaires,
the study assesses how personality dimensions (using the Big Five traits)
and cognitive distortions contribute to overall psychological well-being and
distress levels. The findings reveal that neuroticism and cognitive
distortions significantly increase psychological distress, while traits like
openness and emotional regulation enhance well-being. Implications for
student mental health support are discussed.
Keywords: personality traits, psychological well-being, cognitive distortion,
psychological distress, students
Introduction
The Psychological Well-Being Scale (Ryff, 1989) is intended to measure
aspects of positive functioning, namely Self Acceptance, Positive Relations
with Others, Autonomy, Environmental Mastery, Purpose in Life, and
Personal Growth. These six dimensions were derived from theoretical
formulations (Ryff, 1989a).
In contemporary academic settings, psychological distress among students
is a prevalent concern, exacerbated by academic pressure, social
dynamics, and personal challenges (Sreeramareddy et al., 2021).
Psychological distress refers to an emotional state characterized by
anxiety, depression, and emotional turmoil (Lovibond & Lovibond, 1995).
This paper examines how individual personality traits, psychological well-
being, and cognitive distortions contribute to this distress.
Personality traits, particularly those defined by the Big Five model (i.e.,
neuroticism, openness, conscientiousness, extraversion and
agreeableness) are significant predictors of psychological well-being (John
& Srivastava, 1999).
Cognitive distortions, which refer to irrational thought patterns, are also
known to amplify psychological distress, as they often skew the individual's
perception of stressors (Beck, 1976).
Literature Review
1. Personality Traits and Psychological Distress
Research has shown that neuroticism is a strong predictor of psychological
distress, while traits like conscientiousness and openness are generally
associated with better mental health outcomes (Lischetzke et al., 2012).
Neuroticism reflects a tendency toward negative emotional states and
heightened stress responses (Costa & McCrae, 1992), while openness to
experience and emotional stability are linked to adaptive coping and
resilience (Suls & Martin, 2005).
2.Cognitive Distortions and Their Impact
Cognitive distortions, such as catastrophizing and overgeneralization, have
been associated with increased levels of anxiety and depression in
students (Beck, 1976; Burns, 1980). These thought patterns tend to
magnify stressors and negatively influence students' ability to adapt to
academic or personal challenges (Sullivan & Watkins, 2008).
3. Psychological Well-being in Academic Settings
Psychological well-being involves an individual's sense of purpose,
autonomy, and personal growth (Ryff, 1989). Students with high
psychological well-being tend to experience lower levels of distress and
perform better academically (Keyes et al., 2002). However, cognitive
distortions and maladaptive personality traits can erode well-being, leading
to higher psychological distress (Lamers et al., 2011).
Methodology
1. Objective
To explore how personality traits, psychological well- being and cognitive
distortions contribute to psychological distress amongst students.
2. Hypothesis
1. H1 : Students with higher neuroticism will experience more psychological
distress.
2. H2 : Students who are more conscientious, open, extraverted, and
agreeable will experience less psychological distress and better
psychological well-being.
3. H3 : Cognitive distortions, like negative thinking patterns, will lead to
higher psychological distress.
4. H4: Students with better psychological well-being will have lower levels
of psychological distress.
5. H5: A combination of personality traits, cognitive distortions, and
psychological well-being will predict the overall level of psychological
distress among students.
3. Sample
A sample of 300 undergraduate students from various disciplines at a large
university participated in the study. The average age was 21 years with a
nearly equal distribution of males and females collected through non
probability convenience sampling technique. The scale of measurement
used here is interval scale .
4. Tool used
- Personality Traits: The Big Five Inventory (BFI) (John & Srivastava, 1999)
was used to assess personality traits on a Likert scale ranging from
1(strongly disagree)-5(strongly agree).
- Cognitive Distortions: The Cognitive Distortion Scale (CDS) (Covin et al.,
2011) was used to measure maladaptive thinking patterns on a scale of
1(never)-5(always).
- Psychological Well-being: Ryff's Psychological Well-being Scale (Ryff,
1989) was used to assess various dimensions of well being on a 6 point
Likert scale ( strong disagreement to strong agreement).
- Psychological Distress :The Depression Anxiety Stress Scales (DASS-21)
(Lovibond & Lovibond, 1995) was used to measure distress levels(0= didn’t
apply to me at all; 3 = applied to me most of the time).
5. Procedure
Participants completed the questionnaires in a supervised setting. Data
were analyzed using regression analyses to examine the relationship
between personality traits, cognitive distortions, and psychological distress.
Results
The results of the regression analyses indicated that neuroticism
significantly predicted higher levels of psychological distress (β = .45, p
< .001), while openness to experience was associated with higher
psychological well-being (β = -.23, p < .05). Cognitive distortions were also
a significant predictor of distress (β = .36, p < .01). Together, personality
traits and cognitive distortions explained 40% of the variance in
psychological distress (R² = .40, p < .001).The significant association
between openness to experience and higher psychological well being
lowers distress.
Discussion
The findings highlight the importance of considering both personality traits
and cognitive distortions when assessing psychological distress in
students. Students high in neuroticism and cognitive distortions are more
prone to distress supporting H1&H3 , suggesting the need for interventions
that target maladaptive thought patterns and promote emotional stability
(Lamers et al., 2011). Programs designed to enhance openness, emotional
regulation, and positive thinking could improve overall well-being and
reduce psychological distress.
Implications
These findings underscore the need for student support services to
incorporate personality assessments and cognitive behavioral techniques
into their counseling programs. Addressing cognitive distortions through
cognitive-behavioral therapy (CBT) may be a key strategy in reducing
distress among vulnerable students.
Conclusion
Personality traits, psychological well-being, and cognitive distortions are
significant factors contributing to psychological distress among students.
Interventions that target maladaptive personality traits and cognitive
distortions may improve students' mental health and academic success.
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References
Beck, A. T. (1976). Cognitive therapy and the emotional disorders
International Universities Press.
Burns, D. D. (1980). Feeling good: The new mood therapy. William Morrow
and Company.
Covin, R., Dozois, D. J., Ogniewicz, A., & Seeds, P. M. (2011). Measuring
cognitive errors: Initial development of the Cognitive Distortions Scale
(CDS). International Journal of Cognitive Therapy, 4(3), 297–
322..2011.4.3.297
Costa, P. T., & McCrae, R. R. (1992). *Revised NEO Personality Inventory
(NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional
manual. Psychological Assessment Resources.
John, O. P., & Srivastava, S. (1999). The Big Five trait taxonomy: History,
measurement, and theoretical perspectives. In L. A. Pervin & O. P. John
(Eds.), Handbook of Personality: Theory and Research (pp. 102–138).
Guilford Press.
Keyes, C. L., Shmotkin, D., & Ryff, C. D. (2002). Optimizing well-being: The
empirical encounter of two traditions. Journal of Personality and Social
Psychology, 82(6), 1007–1022.
Lamers, S. M. A., Westerhof, G. J., Kovacs, V., & Bohlmeijer, E. T. (2011).
Differential relationships in the association of the Big Five personality traits
with positive mental health and psychopathology. Journal of Research in
Personality, 46(1), 92–98
Lovibond, S. H., & Lovibond, P. F. (1995). *Manual for the Depression
Anxiety Stress Scales* (2nd ed.). Psychology Foundation of Australia.
Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the
meaning of psychological well-being. Journal of Personality and Social
Psychology, 57(6), 1069–1081.
Sreeramareddy, C. T., Shankar, P. R., Binu, V. S., Mukhopadhyay, C.,
Ray, B., & Menezes, R. G. (2021). Psychological morbidity, sources of
stress and coping strategies among undergraduate medical students.
Medical Education Online, 12(1), 1–8.
Suls, J., & Martin, R. (2005). The daily life of the garden-variety neurotic:
Reactivity, stressor exposure, mood spillover, and maladaptive coping.
Journal of Personality, 73(6), 1485–1510
Sullivan, K., & Watkins, M. W. (2008). Cognitive distortions and anxiety in
high school students. Journal of Child Psychology, 39(1), 185–191.
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ASSIGNMENT- 2
TOPIC : EXPLAIN THE CONTRIBUTION OF VARIOUS SCALES OF
MEASURMENT IN DECISION MAKING FOR THE APPLICATION OF
STATISTICAL TECHNIQUE.
Scales of measurement play a crucial role in decision-making for the
application of statistical techniques, as they determine the type of data
being analyzed and the appropriate statistical methods that can be applied.
There are four primary scales of measurement: nominal, ordinal, interval,
and ratio. Each scale has different properties that influence the choice of
statistical techniques.
1.Nominal Scale
- Definition: This scale classifies data into distinct categories without any
order or ranking. Examples include gender (male, female), nationality, or
types of products (A, B, C).
- Properties: The data is qualitative, with no inherent numerical meaning.
- Contribution to Decision-Making:
-Techniques: Non-parametric tests such as chi-square tests or frequency
distributions are used. Measures of central tendency like the mode can be
applied.
-Example: In a survey, if you are analyzing the distribution of respondents'
preferences for a product, nominal data would guide you to use descriptive
statistics or tests of association (e.g., chi-square) rather than mean or
standard deviation.
2. Ordinal Scale
- Definition: Data is categorized, but the categories have a logical order or
ranking, though the intervals between categories are not equal. Examples
include satisfaction levels (low, medium, high) or rankings (1st, 2nd, 3rd).
- Properties: Data provides order but lacks uniform spacing between points.
- Contribution to Decision-Making:
-Techniques: Non-parametric techniques like the Mann-Whitney U test or
Kruskal-Wallis test. Measures of central tendency like the median and
percentiles are often used.
-Example: In customer satisfaction surveys, ordinal data can help
businesses rank products or services, guiding decisions on improvement
priorities using tests or ranking methods.
3. Interval Scale
- Definition: This scale has ordered categories with equal intervals between
them, but no true zero point. Examples include temperature in Celsius or
Fahrenheit.
- Properties: It allows for comparison of differences but not true ratios.
- Contribution to Decision-Making:
-Techniques: Parametric statistical methods like correlation, regression,
ANOVA, and t-tests are applicable because interval data allows for the
calculation of mean and standard deviation.
- Example: When assessing student performance based on test scores on
an interval scale, decisions regarding educational interventions can be
made using parametric techniques to identify significant differences
between groups.
4.Ratio Scale
- Definition: The ratio scale possesses all the characteristics of the interval
scale but also has a true zero point, allowing for meaningful comparisons of
ratios. Examples include weight, height, and income.
- Properties: Enables full range of arithmetic operations, including
meaningful ratios.
- Contribution to Decision-Making:
- Techniques: All parametric tests such as regression, correlation, and t-
tests can be applied. Measures like geometric means, variances, and
coefficients of variation are valid.
- Example: In financial analysis, ratio data such as income or sales figures
can guide business strategies, allowing for precise comparisons and
forecasting through regression models or growth rate analysis.
Conclusion
Each scale of measurement provides different levels of information, guiding
decision-makers to choose appropriate statistical techniques:
- Nominal and ordinal scales often lead to the use of non-parametric
statistics, focusing on frequency or rank-based methods.
- Interval and ratio scales support the use of parametric statistics, enabling
more advanced analytical techniques that assume data normality and allow
for more precise comparisons and predictions.
Understanding the scale of measurement is critical for making informed
decisions about the proper application of statistical methods.
