Psychology Assignment: Statistical Anxiety Based On Traditional & Non-Traditional Students

Question

Task:
Write a psychology assignment presenting a research report on the topic “Statistical anxiety based on traditional and non-traditional students”.

Answer

Abstract

Objective: The study prepared within this psychology assignment involves assessment and comparison of statistical anxiety in two unique groups of students, traditional and non-traditional. The different aspects of statistical anxiety have been discussed in this paper along with analysis of different previous research made in this field. 

Methods: The study aims to apply the Statistics Anxiety Rating Scale (STARS) to evaluate statistical anxiety in the two chosen populations, traditional and non-traditional students. The scale uses a six-factor analysis which scores participants which indicates their level of statistical anxiety. A higher score indicates a higher level of anxiety. 

Results: The findings suggested that non-traditional students have a higher level of statistical anxiety than traditional students, based on the evaluation of the two participant populations. Analysis of the findings supported by scientific reasoning have also been conducted in this paper. 

Introduction

Statistical anxiety can be defined as the anxiety feeling which is encountered by taking a statistics course as well as by doing statistical analysis (Arjomandi, 2018). Most of the students suffer from statistical anxiety to some extent. The students who are over the age of 22 have greater anxiety issues than the young generation (Arjomandi, 2018). Statistics anxiety is extremely common in undergraduate psychology students. Psychology is an applied science which involves collection of individual data to derive conclusions. Conclusions cannot be derived from raw data and therefore requires data analysis. Statistics involves data analysis. Therefore, statistics is an unavoidable tool for psychology students as well as researchers. This is why it is important for psychology students to learn psychology which has often shown the prevalence of statistical anxiety in these students more prominently. 

The Statistics Anxiety Rating Scale (STARS) is currently the most extensively employed scale to measure statistics anxiety. This analysis includes data gathering, data processing as well as interpreting of the data (Arjomandi, 2018). The scale is based on the measurement of six different scales, interpretation of anxiety, evaluation and lesson anxiety, fear of statistics instructors, fear of seeking help, value of statistics, and self-perception of computational capacities. The Statistics Anxiety Rating Scale (STARS) can be administered to a selected sample population of statistics students, dividing them into a sample population of the traditional student participants and the non-traditional student participants. A significant difference is found in the six factors which are identified by the chosen test. A lot of significant differences can be noted while comparing the grades finally of the two groups as well as with the correlation coefficients (Carreira, 2021). 

The STAR method which is the statistics anxiety rating scale can be used on a whole variety of students who are beginner business students. Two experimental groups, non-traditional students and traditional students, are chosen for the current study. Both groups are compared based on six factors. The first is worth of statistics, the interpretation of the anxiety, the third is testing the class anxiety, the next will be computation of anxiety, the next will be fear of asking the questions, and the last will be fear of statistics teachers (Chung, 2017). The two groups are compared on the final course grades and the correlation coefficient present between the various factors as well as the final grades. 

The independent variable (IV) is a parameter that is controlled by the investigator to see if it affects the behavior of another parameter. The dependent variable (DV) refers to the other parameters that is evaluated and projected to be reliant on the IV. For the current study, the IV would be a population that are not affected by the test conditions, in this case, exposure to statistics or computational courses. The DV would be the students with exposure to statistics and computational courses. This study deals with statistical anxiety which is based on two types of student: traditional or non-traditional. This paper aims to investigate several variables that believe to influence the achievement. The chosen sample populations of student participants are to be investigated on the affective measures like the attitude on the learning statistics, the motivational intensity as well as the anxiety which is experienced in the statistics course. With the measures of achievement present in the course, the performance measures will be based on the quantitative as well as qualitative psychological parameters (Wardley, 2013). The structural equation model which is related to the achievement in the course related to inventory can be tested. The structural model is based on the Gardner socio-educational model of second language learning. Both the motivation as well as the aptitude will combine to predict the achievement of the statistics and can also be suggested by the Lalonde and Gardner (2000). There will be a direct link present between the anxiety as well as the achievement in the statistics (Brändle, 2017). This will have an understanding of how different types of students have different anxiety levels. This will also help in figuring out factors responsible for anxiety levels and what can be done to reduce the anxiety levels. It will also help in studying the anxiety level of international students. 

This study is of essence as there is a visible ongoing issue involving the comfort of students of psychology with statistics. As discussed previously, statistics is crucial for the study of psychology and therefore in order to ensure that students are capable of being skilled in statistics at the end of their courses. This would involve alterations in teaching styles or other interventions to mitigate statistical anxiety in students. Such interventions can be undertaken only upon receiving quantitative data about the prevalence of anxiety in students. In this study, a unique perspective was undertaken in including non-traditional students. Encouraging non-traditional students is crucial for development of the academia and the society in general. Non-traditional students have been known to be afflicted with statistical anxiety more than traditional students. This study aims to identify this bias. 

Method

Participants 

Participants were chosen from a business school student. This was done as business school courses invariably involves a number of coursework’s involving Statistics and Computational Sciences. The participant students were all beginner students. A control group was chosen of 50 students who do not take statistics or mathematics in their courses. 100 traditional students who had statistics and/or mathematics as a part of their coursework and 21 non-traditional students with statistics and/or mathematics as part of their coursework were obtained to participate in this study. 

Design

The design of this study is purely quantitative. A quantitative assessment based on the STAR scale was conducted on the participants and their scores were noted. Analysis of this data was then performed to compare the Statistical Anxiety scores of the participants. 

Materials

This study required access to the Statistics Anxiety Rating Scale (STARS). The scale measures six factors on a 5-point scale. Higher score indicates higher levels of anxiety (Chew, et al., 201). The SPSS tool was used to analyse the data obtained. 

Procedure

Two experimental groups, non-traditional students and traditional students, are chosen for the current study. The comparison of the groups has been done based on specifically “two major factors”. The first factor is considered to be the “worth of statistics,” which is further followed by “interpretation of relevant anxiety” and the last one to be considered as “testing of the class anxiety”. Furthermore, this is followed by “computation of anxiety” and “fear of asking the questions” as well as and the “fear of statistics teachers”. Both of the groups would be compared and estimated throughout the “final course grades”. This will further be followed by representation of appropriate “correlation coefficient” existent amongst several other factors and variable involved as well as the final grades. 

Results

Discussion

One of the significant results of this study has indicated towards the fact that “non - traditional students” are distinct from “the traditional students”. This is further in retrospection of “one of six factors, revealed by the STARS.” Application of “the STAR method” has been pervasively used through integration of “statistics anxiety rating scale” which can be further used for a number of “business students” in order to reach optimal solution. The specific groups to be taken into account are “experimental groups, traditional students and non-traditional students”. All of the students have been chosen for reaching to an optimal conclusion in this study. 

Six major factors have been taken into consideration, namely, “worth of statistics”, “testing the class anxiety”, “the interpretation of the anxiety”, “computation of anxiety, fear of asking the questions, and fear of statistics teachers” (Chung, 2017). All the existing groups have to be compared on the basis of their “final grades” as well as the “resulting correlation coefficient”. The coefficient existing amongst various factors and final grades would be taken into consideration. Additionally, the teachers have been already made accustomed with the differences between grades obtained by “traditional and non-traditional students”. This has enabled them to gain a prior perception regarding thought process of the teachers. Consideration of a “collaborative testing” has been taken into account for optimising appropriate “statistical analysis” along with providing a scope for feedback (Francois, 2014). The account of a “fast feedback process” has proven to be highly positive while “concretely dealing in terms of statistical analysis.” The factor of distinguished gender has also played a huge role in undermining results within “statistical anxiety.” It has been evident that “Females” within the study tend to flourish in terms of academic performance, given the ambience to be composite of a singular gender or all-female classroom. Other than that, considering the scope of “mathematical self-concept” within ambience of a “single sex classroom”, it has been evident that the result increases and successively have decreased in case of a “co Ed classroom.”

The score for “non-traditional students” have been seen high which represents maximum anxiety in this group. On the other hand, “non-traditional students” had scored high on specific four frontiers. The significant factor where traditional students have scored comparatively higher and thereby “showing more anxiety” is 1 out of 6. This is in general plausible as non-traditional students are already under major anxiety about their overall capacities in the educational field. Statistics and computational skills also have added pressure as the current society chooses to have the perspective that having computational skills is equivalent to having elevated intelligence. It has been now proved through extensive neuro-cognitive research that this perspective is flawed. Human cognitive capacities are currently divided into several forms of capacities of which on is heighted computational analysis capabilities. This indicates that computational capacities do not necessarily indicate the intelligence quotient (IQ) of an individual. However, the societal implication and conditioning of minds over several generations create this insecurity among individuals regarding their cognitive capacities. The self-assurance often depends on whether an individual is fairly capacitated with computational analysis. In the absence of the optimum capacity, individuals often go through self-depreciation as well as social disapproval. This also adds to stress. It must also be noted that human cognitive development continues during the young adult age. skills obtained during adolescence till the early twenties are better learned and grasped. This is because the cognitive development during this phase of life accommodates for the learning of new skills. In non-traditional students, the primary issue could be that most of them belong to an age where cognitive development has either stalled or continues at a slower pace. This leads to such students grasping information or new skills at a slower pace than traditional students. This added to the fact that in the case a student does not have the optimum computational capacities, adds to the students performing generally poorer in their courses. This leads to further statistical anxiety. This results in increased stress involving an “additional experience of the non-traditional students”. This will further help in contribution to a nominal and small difference into the overall result.

The “traditional group” has scored high in this regard, while on the other hand, “the non-traditional students” showcased comparatively lower grades (Olokundun, 2018). The traditional students have a general advantage of being young and thereby with an elevated capacity of absorbing information. They are also in the developmental phase of their cognitive abilities which allows them to learn and crasp computational skills better. Even though the cognitive abilities of a traditional student do not incline towards computational abilities, it is easier and feasible for these students to accommodate and assimilate the skill to the point from they can exhibit fair capabilities of performing their coursework well. This primarily resulted in the reduced statistics anxiety in these students. Traditional students also have lesser general anxieties which also helps them in maintaining reduced anxiety regarding their course. This also accounts for lower statistical anxiety in traditional students.

Based on the above case study, the factor of statistical anxiety cannot be taken into account as the only significant and resultant cause of students obtaining lower grades. Additionally reconsidering the ambience of mathematics classroom, the study has also considered it to be one of the significant results of non-traditional students Tu to achieve lower grades. Other than that, factors related to family responsibilities have been taken into account as additional explanations to the lower grades of non-traditional students. This has been aligned with the comparison made through use of correlation coefficient amongst the six factors chosen. Other than that, included in our final grades have also helped ensure casing significant relationships among the two variances of grades. Based on the correlation factor 6 that has been obtained between final grades of the non-traditional students, the study has noted a moderate negative correlation. Such negative correlation indicates that the anxiety level has increased with decrease of final grade. This is a significant correlation in terms of considering the existing relationship between final grades and two of the major six factors. 

The study has included significant efforts in order to reduce the level of anxiety upon students who are witnessed to be highly threatened by causes of their low grades. Other than that identification of teachers and their appropriate concerns regarding the students have also been accounted for. A negative correlation has been found between final grades of the students and the six factors of STARS. The resultant study has also shown health in showcasing the fact that a number of students facing statistics anxiety is more in case of international students than domestic ones. Additionally, in order to improve the situation, the study has recommended the teacher's role to be in cooperation with appropriate development training strategies that can help me to get anxiety among students. Considering this, one of the major focuses would be to implant methods that would help to remove constraints of time and help the students to reach their academic goal with optimal potential. Additionally, reconsidering the factor of counteracting fear, the teachers and school ambience should provide distribution of solutions for old copies of exam and test papers in order to enhance the practicing capability of students. It is crucial that such measures are implemented in order to mitigate statistics anxiety in any student. 

References

Arjomandi, A., Seufert, J. H., O'Brien, M. J., & Anwar, S. (2018). Active teaching strategies and student engagement: A comparison of traditional and non-traditional business students.

Brändle, T. (2017). How availability of capital affects the timing of enrolment: the routes to university of traditional and non-traditional students. Studies in Higher Education, 42(12), 2229-2249.

Brändle, T., & Lengfeld, H. (2017). Drifting apart or converging? Grades among non-traditional and traditional students over the course of their studies: a case study from Germany. Higher Education, 73(2), 227-244.

Carreira, P., & Lopes, A. S. (2021). Drivers of academic pathways in higher education: Traditional vs. non-traditional students. Studies in Higher Education, 46(7), 1340-1355.

Chung, E., Turnbull, D., & Chur-Hansen, A. (2017). Differences in resilience between ‘traditional’and ‘non-traditional’university students. Active Learning in Higher Education, 18(1), 77-87.

Francois, E. J. (2014). Motivational orientations of non?traditional adult students to enrol in a degree?seeking program. Psychology assignment New Horizons in Adult Education and Human Resource Development, 26(2), 19-35.

Olokundun, M., Moses, C. L., Iyiola, O., Ibidunni, S., Ogbari, M., Peter, F., & Borishade, T. (2018). The effect of non-traditional teaching methods in entrepreneurship education on students’ entrepreneurial interest and business start-ups: A data article. Data in brief, 19, 16-20.

Patterson, C., Perlman, D., Taylor, E. K., Moxham, L., Brighton, R., & Rath, J. (2018). Mental health nursing placement: A comparative study of non-traditional and traditional placement. Nurse education in practice, 33, 4-9.

Wardley, L. J., Bélanger, C. H., & Leonard, V. M. (2013). Institutional commitment of traditional and non-traditional-aged students: a potential brand measurement? Journal of Marketing for Higher Education, 23(1), 90-112.

Bell, J, A (2003). Statistical anxiety: the nontraditional student Education ( Chula Vista), 124(1), 157.

Tremblay, P. F., Gardner, R. C., & Heipel, G. (2000). A Model of the Relationships Among Measures of Affect, Aptitude, and Performance in Introductory Statistics. Canadian Journal of Behavioural Science, 32(1), 40–48. https://doi.org/10.1037/h0087099.

Chew, P. K., Dillon, D. B., & Swinbourne, A. L. (2018). An examination of the internal consistency and structure of the Statistical Anxiety Rating Scale (STARS). PloS one, 13(3), e0194195. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0194195

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