Research Question
Do students with ADHD perform poorer than normal students?
Background and Significance to Psychology
Attention Deficit Hyperactive Disorder (ADHD) is a debilitating disorder that negatively affects different aspects of an individual’s life. Social skills and interactions, parent-child relationships, and cognitive and academic skills are the aspects of life that profoundly manifest the effects of ADHD. Previous studies suggested that ADHD is highly prevalent in children and teens, after which the affected persons would eventually outgrow the condition. These research findings caused a laxity in the determination of effective therapeutic and management strategies for the disorder. Consequently, the prevalence of the disorder has increased in recent years. Recent studies indicate that ADHD symptoms persist in approximately 50% of the patients who are diagnosed with the disorder in their childhood (Flood et al., 2016). There is, therefore, a propensity of children manifesting significant symptoms of the condition in adulthood. Parents and teachers in different schooling levels have raised concerns regarding the cognitive abilities of some of their children or students. A follow up of the concerns could most often indicate that the students may or may not have been previously diagnosed with ADHD.
Pertinent effects of ADHD on an individual’s social life include strained family interactions and the inability to develop or maintain friendships and social relationships. The severity of the effect is contingent on the environment which the individual is exposed to. The demands made by the environment may add pressure to the individual, making them either cope with their condition to catch up with their peers. In other instances, environment-induced pressure may worsen the situation because the individual may not be able to perform to the standards required of them. This environment-performance balance describes the psychological patterns observed in individuals with ADHD (Flood et al., 2016). The psychological effects of ADHD involve an intricate cascade of social and cognitive developmental patterns as one grows from infancy into adulthood.
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Cognitive psychology is a field of psychology that focuses on the mental processes of perception, problem-solving, attention, thinking, language use, and memory. Individuals’ cognitive functions become enhanced as one develops through the different stages of life. Schools provide an apt environment for the nourishment, development, assessment, and evaluation of the aspects of cognition. Usually, the assessment and determination of cognitive competency are based on testing the levels of attention and understanding of curriculum-provided knowledge. Critical thinking and problem-solving are also assessed. There exists a distinction between the levels of competency among the students, based on their cognitive abilities. Often, students of higher understanding and better performance are perceived as normal, while their colleagues of cognitive impairments tend to perform poorer. These unfortunate students may be subjected to ridicule, which has a negative psychological impact on their subsequent performance. ADHD thus predisposes the affected individuals to a cascade of psychological torture for which effective interventions are required.
To determine the effective strategies of managing ADHD and their effects on cognitive skills and academic performance, it is imperative to evaluate the performance of the two categories of students, i.e., students with ADHD and normal students. This requires collection and analysis of statistical inferences from the school population, parents, and psychological therapists.
Hypothesis
Null Hypothesis H 0 : Students with ADHD have a statistically significant lower academic performance that the students without ADHD.
Alternative Hypothesis H 1 : There is no statistical difference in significance in the performance of students with ADHD and students without ADHD.
Statistical Test
The T-test is a statistical inference that is used to analyze the relationship between two related groups and determine whether there is a significant statistical difference or similarity between the two groups. The analysis requires determining the means of the two groups (Chatfield, 2018). This is established by finding the averages of the scores of individuals in the two groups. The difference in the means of the two group is then determined. In this analysis, the individual scores the students drawn from various schools across the study area is used as the benchmark for the statistical inferences.
There occurs variability between the two groups of students. Other factors that might affect the student’s scores need to be taken into consideration. These include the students’ physiologic conditions, mental stability, socio-economic factors, the prevailing political atmosphere, and the student’s attitude towards the subject being tested. In this case, these variables will result in a positive or negative deviation of the scores from the actual score, thereby positively or negatively affecting the means of the two groups. This variability in the scores constitutes the standard error. The standard error describes the deviation of the individual scores from the given group mean. A low standard error means that there is a large deviation from the mean, whereas a high standard error implies that the scores have not fluctuated far from the mean.
The T-value is obtained by taking the difference between the means and taking into consideration the standard error. The T-value is the premise for arriving at the conclusion of the statistical analysis and thereby the conclusion of the study. A higher t-value will imply that the null hypothesis is true (Chatfield, 2018). In this case, a high t-value implies that there are normal students perform better than students with ADHD. A t-value closer to zero would mean that the alternative hypothesis is true. In this case, the study conclusion would be that there is no significant difference in between the performance of students with ADHD and normal students. A probability distribution plot can be used to ascertain how the t-value from the sample population compares with the expected or previously analyzed t-value.
Type of Data
The data to be collected for the test is the student scores of individual students in different curriculum subjects. Scores represent nominal data and are in percentages. The other set of data to be collected is the number of students involved in the study. This number is obtained from student records in the learning institutions, healthcare facilities, and psychological and mental wellness counselors.
Data collection tools
To generate data and evaluate on performance between students with ADHD and other students without the condition, development of appropriate data tools is imperative to capture correct data which can be comparable (Gentil et al., 2017)
Questionnaires
A questionnaire will be administered to a selected sample of students from the groups, i.e. students with ADHD and those without. The advantage of a questionnaire is that responses can be analyzed with quantitative methods by assigning numerical values to the responses (Gentil et al., 2017) . Also, the results are easier to analyze than qualitative techniques. The questionnaire will entail both a yes or no response and those with scaled responses. To compare the performance between the two groups the, some of the pertinent questions that will be included are do the students feel they are given enough time to ask questions after lectures? Are the lectures in a class well taught? What is the average time for lessons are taught in a normal class day?
The responses will be a YES, NO for the first two questions. The third question will have a scale in which the student can pick one response. The analysis of the data obtained from the questionnaire will be used to compare the performance to their dependent factors that determine their scores (Schiekirka, Feufel, Herrmann-Lingen, & Raupach, 2015)
Interviews
Interviews with the individual students or with their respective teachers is an important data collection tool. Data obtained can be evaluated using quantitative or qualitative techniques. The advantage of interviews is that it can be conducted in person or over the telephone hence eliminating the need for physical presences, which is beneficial in terms of cost of the study. (Moser & Korstjens, 2018) . Although interviews are usually qualitative, it can be performed formally (structured), semi-structured, or informally. The interviewer, in this case, can both interview the students or teachers. The lectures interview can entail the following: The response rate in class among the ADHD students compared to other students and the average concentration span between the ADHD students and other students.
The interview for students should consider how the ADHD students feel in terms of how they are treated and what they think might be done to improve their academic scores.
For the ADHD students; do they feel they are given the same treatment in class as compared to other students? Are there additional activities scheduled for them to help them improve their grades.
Analysis of Records
Another important data collection tool is documents and records. This consist of examining existing databases, reports, attendance logs, and newsletters. The advantage of this method is it can be inexpensive easy of collecting and gather information (Schiekirka et al., 2015) . In this case, the study will focus on collecting data on both sets of students. The data to be collected are their attendance logs to check on the average attendance of lectures, individual class reports on grades and the performance curve of both for a given period. The data that will be obtained from above will be evaluated and compared between the two groups in relation to their individual performance index.
References
Gentil, M. L., Cuggia, M., Fiquet, L., Hagenbourger, C., Le Berre, T., Banâtre, A., Chapron, A., (2017). Factors influencing the development of primary care data collection projects from electronic health records: A systematic review of the literature. BMC Medical Informatics and Decision Making , 17 (1), 1–21. https://doi.org/10.1186/s12911-017-0538-x
Moser, A., & Korstjens, I. (2018). Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. European Journal of General Practice , 24 (1), 9–18. https://doi.org/10.1080/13814788.2017.1375091
Schiekirka, S., Feufel, M. A., Herrmann-Lingen, C., & Raupach, T. (2015). Evaluation in medical education: A topical review of target parameters, data collection tools and confounding factors. GMS German Medical Science , 13 , 1–19. https://doi.org/10.3205/000219
Flood, E., Gajria, K., Sikirica, V., Dietrich, C. N., Romero, B., Harpin, V., & Chen, K. (2016). The Caregiver Perspective on Paediatric ADHD (CAPPA) survey: Understanding sociodemographic and clinical characteristics, treatment use and impact of ADHD in Europe. Journal of affective disorders , 200 , 222-234.
Chatfield, C. (2018). Introduction to multivariate analysis . Routledge.
