Lecture 9; Sample Size Determination practice scenario question

 Scenario:

Karen Kabwe,  a female public health researcher studying the vaccination rates in a small town with a total population of 2,500 people. She wants to conduct a survey to estimate the proportion of the population that has been vaccinated against influenza this year.

To ensure her survey results are accurate and reliable, she needs to determine an appropriate sample size. She consults you and you decide to advise her to use the following parameters for her survey:

- You want the estimate to be within a 5% margin of error (precision).

- You are aiming for a 95% confidence level.

- From previous studies, you estimate the vaccination rate to be approximately 40%.

Question:

Calculate the sample size needed to achieve Karen's objectives. Show your calculations ;

a) When total population is not Known

b) When total population is known

 Good luck on your practice

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Lecture 9; Sample Size determination/Calculation

       Sample Size Calculation:

• Depends on population size, variability, and research design.
• Formula for simple random sampling:

where:

• (n): Sample size
• (Z): Z-score for desired confidence level
• (p): Estimated proportion
• (E): Margin of error¹.
• Online sample size calculator. Check it Here!

Remember that selecting an appropriate sampling method and size is crucial for valid research

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Lecture 7: Terminologies Used in Research: Study Area & Target Population

 

Study Area

1.      Definition:

• A study area refers to a specific geographic location or region where a researcher plans to conduct an in-depth study. It serves as the focal point for data collection, analysis, and investigation.
• Researchers choose a study area based on their research objectives and the specific topic they are exploring.

2.      Components of Describing a Study Area:

• Location: Clearly define the geographical boundaries of your study area. Specify coordinates, administrative divisions (such as city, state, or province), and any relevant landmarks.
• Geography: Describe the physical features of the area, including terrain, landforms, and natural resources.
• Climatic Conditions: Discuss the prevailing weather patterns, temperature ranges, humidity levels, and any seasonal variations.
• Social Infrastructure: Highlight amenities such as schools, hospitals, transportation networks, and community centers.
• Vegetation: Mention the types of vegetation (forests, grasslands, etc.) present in the area.
• Population Density: Provide information on population density and demographics.
• Topography: Explain the elevation, slopes, and contours of the land.
• Maps: Include color maps that visually represent the study area.

3.      Importance:

• Describing the study area is crucial for understanding the context of your research. It helps readers visualize the environment in which your study takes place.
• Whether you’re investigating urban planning, environmental issues, or social dynamics, a well-defined study area enhances the validity and relevance of your findings.

Remember that a thoughtful and detailed description of the study area contributes to the overall quality of your research.

Target Population

1.      Definition:

• The target population refers to the group of individuals from which a researcher intends to draw conclusions. It is the specific population that the research intervention aims to study.
• Also known as the target audience, this group possesses particular characteristics that distinguish them from the general population.
• In business and marketing contexts, understanding the target population is essential for effective market segmentation strategies.

2.      Determining the Target Population:

• Identifying the target population involves careful planning, precise research questions, and a robust study design.
• Researchers often choose the target group based on characteristics such as age, gender, employment status, income level, or health condition.
• The research findings are then extrapolated to the broader population from which the target sample was selected.

3.      Steps to Choose Your Target Population:

• Set Business Goals: Clearly define your business or brand goals. These goals guide your content strategy and marketing plan.
• Define Study Objectives: State your research motivation and objectives clearly.
• Determine Population Features: Understand the characteristics of your intended audience.

Remember that a well-defined target population ensures that your research aligns with your objectives and provides accurate information to address your study questions or concerns¹. 🎯🔍.

Watch the Video Here!

Lecture 4; Research Questions Terms used in the field of Research - Patri...

Lecture 5: Terms Used in the field of Research- Research Hypothesis

 

ü  Research hypothesis

Let’s explore the concept of a research hypothesis. A hypothesis is a concise statement that predicts your research paper’s findings, data, and conclusion based on scientific evidence and logic. Here are the key points about research hypotheses:

1.      Definition:

• A research hypothesis (or scientific hypothesis) is a statement about the expected outcome of a study.
• It is based on known facts but has not yet been tested.
• The hypothesis needs to be specific, clear, and testable.

2.      Purpose:

• A hypothesis serves as a guide for your research.
• It predicts what you expect to find or observe during your study.
• It forms the basis for designing experiments and collecting data.

3.      Components:

• Independent Variable (IV): The cause or factor you manipulate or study.
• Dependent Variable (DV): The effect or outcome that changes in response to the independent variable.
• Example: If you’re studying the effect of fertilizer on plant growth, the independent variable is the type of fertilizer, and the dependent variable is the plant’s growth rate.

4.      Types of Hypotheses:

• Null Hypothesis (H0):
• Proposes no relationship between two variables.
• Denoted as H0.
• Example: “Attending physiotherapy sessions does not affect athletes’ on-field performance.”
• Alternative Hypothesis (Ha):
• The opposite of the null hypothesis.
• Denoted as Ha or H1.
• Example: “Physiotherapy sessions positively impact athletes’ on-field performance.”

5.      Writing a Strong Hypothesis:

• Be specific and clear about the relationship you’re testing.
• Ensure it is testable through empirical research.
• Distinguish it from a prediction (which is speculative).

Remember, a well-crafted research hypothesis sets the stage for meaningful investigation and scientific discovery! 🌟🔍 For more examples and guidance, you can explore resources like Grad Coach and Scribbr.¹²³

Watch the Video Here!

TERMS USED IN RESEARCH-RESEACH HYPOTHESIS

 “Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis

1. What is a hypothesis?
2. What is a research hypothesis (scientific hypothesis)?
3. Requirements for a research hypothesis
4. Definition of a research hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested. For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions, underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper. In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis.

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity, clarity and testability.

Let’s take a look at these more closely.

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’s being assessed (who or what variables are involved) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague, and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night. 

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

LECTURE 3: TERMS USED IN THE FIELD OF RESEARCH- RESEARCH VARIABLES

Research Variables

Let’s explore the concept of research variables. These are fundamental elements in any scientific experiment or research study. Variables are characteristics or attributes that can be measured, manipulated, or controlled. They play a crucial role in understanding relationships and outcomes. Here are the key types of research variables:

1.      Quantitative Variables:

• These variables represent amounts and can be measured numerically.
• There are two subtypes:
• Discrete Variables (Integer Variables):
• Represent counts of individual items or values.
• Examples: Number of students in a class, different tree species in a forest.
• Continuous Variables (Ratio Variables):
• Measure continuous or non-finite values.
• Examples: Distance, volume, age.

2.      Categorical Variables:

• These variables represent groupings rather than specific amounts.
• They can be further classified into three types:
• Binary Variables (Dichotomous Variables):
• Have yes or no outcomes.
• Examples: Heads/tails in a coin flip, win/lose in a football game.
• Nominal Variables:
• Represent groups with no rank or order between them.
• Examples: Species names, colors, brands.
• Ordinal Variables:
• Groups that are ranked in a specific order.
• Examples: Finishing place in a race, rating scale responses (e.g., Likert scales).

3.      Independent Variable:

• Also known as the predictor variable.
• Manipulated by the researcher to observe its effect on other variables.
• Examples: Age, gender, dosage, treatment type.

4.      Dependent Variable:

• The outcome variable that changes in response to the independent variable.
• It is what researchers measure to assess the impact of the independent variable.
• Example: Plant health (growth and wilting) in a salt-tolerance experiment.

5.      Confounding Variable:

• Affects the relationship between the independent and dependent variables.
• Researchers must control for confounding variables to ensure accurate results.

6.      Latent Variable:

• Not directly observed but inferred from other variables.
• Often used in psychological or social research.

Remember, understanding these variables is essential for designing rigorous experiments and interpreting research findings.

For more details and examples, you can explore resources like Scribbr and Explorable.¹²³

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