a) The type of observational study that I would adopt in this case is a cross-sectional observational study. This involves collecting data from a sample population at a specific point in time. This study design would be appropriate because it allows for the collection of data regarding lecture session attendance and factors influencing it, within a specific timeframe. It would provide a snapshot of the current situation and enable the examination of various factors that affect attendance among students in higher learning public institutions in Tanzania.
b) Strengths and weaknesses of observational research:
1. Strength: Naturalistic Observation - Observational research allows researchers to observe behavior in its natural setting without interfering or manipulating any variables. In this case, attendance at lecture sessions can be observed without any external influence, providing insights into the actual behavior of students.
2. Strength: Ecological Validity - Observational research has high ecological validity as it takes place in real-world environments, such as lecture halls, where the behavior of interest naturally occurs. This increases the generalizability of the findings to the target population of students in higher learning public institutions in Tanzania.
3. Weakness: Observer Bias - Observational research is prone to observer bias, where the researcher's own biases and perceptions may influence the interpretation of behavior. To mitigate this, multiple observers can be trained and inter-rater reliability can be established to ensure consistent and unbiased observations.
4. Weakness: Limited Control - Unlike experimental designs, observational studies lack control over variables, making it difficult to establish causal relationships between factors and attendance. For example, while observational research can identify factors such as timing of classes or distance to lecture halls, it cannot determine if these factors directly cause low attendance.
5. Strength: Cost-effective - Observational research generally requires fewer resources and is less time-consuming compared to experimental research. It allows researchers to collect data without the need for extensive equipment or experimental manipulations, making it a cost-effective method for studying lecture session attendance.
6. Weakness: Lack of Privacy - Depending on the nature of the observational study, privacy concerns may arise. It is important to obtain ethical approval and ensure the anonymity and confidentiality of the participants' data. Observational research of lecture session attendance may require consent from the institutions and students involved, which could affect the overall validity of the findings.
Overall, while observational research has its strengths and weaknesses, it can provide valuable insights into the factors that affect lecture session attendance among students in higher learning public institutions in Tanzania. By managing potential biases and ensuring ethical considerations, the findings can contribute to informing interventions and policies aimed at improving attendance rates.
Imagine that you were asked to find out factors affect lecture session attendance among
students in higher learning public institutions in Tanzania. Assume also that you have been
instructed to use observational study designwhen undertaking this study. For you to come up
with reliable and at least valid findings:
a)Which type of observational study would you adopt? why?.
b)Withvivid examples (where necessary) discussstrengths and weaknesses of
observational research
3 answers
Mvomero District Council has 500 employees. As a young researcher you want to extract a
sample of 100 employees from them using simple random sampling. Explain steps you will
follow to come up with this sample size.
sample of 100 employees from them using simple random sampling. Explain steps you will
follow to come up with this sample size.
To extract a sample of 100 employees from the 500 employees of Mvomero District Council using simple random sampling, the following steps can be followed:
1. Define the population: Identify the population of interest, which in this case is all the 500 employees of Mvomero District Council.
2. Determine the desired sample size: Decide on the desired sample size, which in this case is 100 employees. This number should be based on statistical considerations and the amount of precision required for the study.
3. Assign numbers: Assign a unique number to each employee in the population. Each employee should have an equal chance of being selected.
4. Generate random numbers: Use a random number generator (such as a software program or a table of random numbers) to select the sample.
5. Randomly select the sample: Start by selecting the first random number and choose the employee with that corresponding number from the population list. Repeat this process until you have selected the desired sample size of 100 employees. Ensure that no employee is selected more than once.
6. Validate the sample: Once the sample is selected, double-check to ensure that it truly represents a random sample. Verify that each selected employee has been chosen correctly and that no bias has been introduced.
It is important to note that the steps above assume that a complete and accurate list of all 500 employees is available. Additionally, simple random sampling assumes that the population is homogenous, meaning that there are no distinct subgroups within the population. If there are subgroups of interest, it may be necessary to use stratified random sampling to ensure representation from each subgroup.
1. Define the population: Identify the population of interest, which in this case is all the 500 employees of Mvomero District Council.
2. Determine the desired sample size: Decide on the desired sample size, which in this case is 100 employees. This number should be based on statistical considerations and the amount of precision required for the study.
3. Assign numbers: Assign a unique number to each employee in the population. Each employee should have an equal chance of being selected.
4. Generate random numbers: Use a random number generator (such as a software program or a table of random numbers) to select the sample.
5. Randomly select the sample: Start by selecting the first random number and choose the employee with that corresponding number from the population list. Repeat this process until you have selected the desired sample size of 100 employees. Ensure that no employee is selected more than once.
6. Validate the sample: Once the sample is selected, double-check to ensure that it truly represents a random sample. Verify that each selected employee has been chosen correctly and that no bias has been introduced.
It is important to note that the steps above assume that a complete and accurate list of all 500 employees is available. Additionally, simple random sampling assumes that the population is homogenous, meaning that there are no distinct subgroups within the population. If there are subgroups of interest, it may be necessary to use stratified random sampling to ensure representation from each subgroup.