The author appreciates that there is a need to conduct empirical research to address the influence of student expectation related to cognitive demands due to the previous findings that an expectation about item format affects study strategies used.
The author used the theoretical framework of metacognitive self-regulation to guide the study. The author adjusted the study strategies so that they are in line with the process required by the meta-cognitive self-regulatory framework (Ross et al., 2006). For instance, the authors reviewed existing research and applied the effects of testing students’ study strategies.
The study used a randomized study design where students who enrolled and were seeking certification in the School of Education fifth-year program were included.
The study had several limitations; first, the data were drawn from college students, and the measurement course included a homogenous population. Thus, the generalization of the results might give false results. Second, the time spent on the exam was short. Therefore, the reliability of the data collected is low. Lastly, the study was conducted in an experimental set-up which provided limited ecological validity due to the prepared mindset of the participants (Ross et al., 2006). However, the students recruited in the study were not debriefed.
It will be essential to enroll a larger sample size and age/sex march the participants into cohorts to ensure that the conclusion drawn is scientific and reliable if given a chance to replicate this study. Besides, it is essential to survey actual classrooms using actual course exams to validate results (Ross et al., 2006) quickly. Lastly, the study should incorporate a more naturalistic inquiry where the participants are studied in their natural environment to avoid biased information and false correspondence.
The author used random selection to obtain the study participants. He included students who enrolled and were seeking certification in the School of Education fifth-year program. The author excluded participants who did not consent to use their information in data analysis and those who did not submit adequate information. It is essential to include the social background of the participants because it affects the way they interact and give information (Ross et al., 2006). Also, it is important to stratify the data based on social, age, sex, and other social frameworks to ease the data analysis and comparison of results.
The potential external validity threats in this study included selection bias, testing effect, and aptitude treatment. Selection bias is addressed by having all the population in the study area to give a clear indication of other people’s personalities and other social disorders. Testing effects are minimized by blinding the participants. They are not familiar with the pre-tests question and reduce anxiety and preparedness; hence, the most accurate data is obtained (Ross et al., 2006). Lastly, the aptitude treatment is avoided by repeating the experiment several times while changing one variable at a time to determine its effect on the other variables.
The purpose of the study is to determine the correlation between cigarette smoking and low CD4 count and how it leads to an inadequate immune response to specific opportunistic diseases. First, the study will aim to answer how cigarette smoking correlates with the level of CD4 count. Secondly, the study will determine how low CD4 count due to cigarette smoking affects immunity and response to specific diseases.
CD4 T cells play a crucial role in body’s immune defense against pathogens. They help to recognize, neutralize and eliminate pathogens that enter the body system. Lower levels of CD4 T cells positively correlate with poor immunity and susceptibility to diseases. However, little is known about how cigarette smoking affects their stories in the body and if it is a factor that leads to the poor immune response to opportunistic diseases. Therefore, it is essential to conduct a non-experimental study to determine the correlation and how each variable affects another.
This study will be done retrospectively using the data collected at the Comprehensive care unit. Patients who reported to the hospital and are smokers are included in the study. The patient’s history will be followed in the hospital records to determine their CD4 levels when reported and follow-up to determine how frequently they visited the hospital. Patients will be assigned unique study identification numbers to avoid using their names. Ethical approval will be obtained from the scientific review body to use the data. An equal number of non-smokers will be recruited as negative control.
The dependent variable will be the CD4 count because they vary between participants based on their social life. The study hypothesizes that smokers will have a lower CD4 count than non-smokers (Parcel et al., 2021). CD4 count is determined by drawing blood, staining CD4-positive cells with fluorescent dyes, and acquiring the result using a flow cytometer.
The primary independent variable is smoking because it distinguishes between the control group and the test group. The physician will indicate upon asking the patients whether they are smokers or non-smokers during the medical screening process.
The extraneous variables that the study will try to control include age, diet, and physical activities of the participants. It is essential to maintain the nutrition style of the participants during the study because it can skew the results. For instance, some food tends to increase circulating CD4 T cells that are not necessarily responsible for an immune response. The study will try to age-match the participants for easy comparison during the data analysis process. Lastly, the study will control physical activities by recruiting participants to ensure that the data collected is uniform.
The study will use a randomized sampling technique where all patients within the age limit of 20-35 years and who are smokers will be included in the study. The study targets 150 women and 150 smokers, and 150 non-smokers who will act as the control. All participants must be between 20-35 years because studies indicate that this age group is active smokers. Samples will be obtained from hospital records after acquiring administrative permission. Patients who have TB, are pregnant, HIV positive, and have other underlying conditions will be excluded from the study
Sample size calculation will be done based on the accuracy of the measure rates in the target population. For instance, this study will estimate the proportion (p) of smokers who attended the hospital and within the desired age group in the population. If the calculated ratio is 50%, the estimate will be +/- 1% as an actual value, making the proportion fall between 49%-51% with 95% confidence. From the 50% proportion and a population accuracy of +/- 1% sample size will be about 450 respondents out of a sample population of 1000 people within the hospital (Parcel et al., 2021). However, a larger sample size is preferable to increase the statistical power and validity of the study.
Parcel, T., Bauldry, S., Mickelson, R., Smith, S., Riel, V., & Boden, M. (2021). Using opinion polling data to replicate non-experimental quantitative results across time and space: an exploration of attitudes surrounding school desegregation and resegregation policies. American Behavioral Scientist, 000276422110332.
Ross, M., Green, S., Salisbury-Glennon, J., & Tollefson, N. (2006). College students study strategies as a function of testing: an investigation into metacognitive self-regulation.