NURSING RESEARCH & STATISTICS-SEPTEMBER 2021
⏩SECTION – A NURSING RESEARCH⏪
⏩I. Elaborate on:(1 x 15 = 15)
🔸1.Explain the Components of a good research report.
A good research report is a comprehensive document that systematically presents the research process and findings. It is structured to communicate the research effectively, ensuring clarity, coherence, and comprehensiveness. Here are the key components of a good research report, explained in detail:
The introduction sets the stage for the research. It provides the necessary background and context, highlights the significance of the research, and clearly states the research problem, objectives, and hypotheses. This section should engage the reader and explain why the research is important and worth conducting.
A thorough literature review demonstrates the researcher’s knowledge of the field. It synthesizes existing studies, identifies gaps, and places the current research within the context of what is already known. This section should critically analyze previous research rather than just summarize it.
The methodology section is critical for the reproducibility of the research. It should provide enough detail for another researcher to replicate the study. This includes a clear description of the research design, population and sample, data collection methods, and data analysis procedures. Ethical considerations ensure that the research was conducted responsibly.
In the results section, data should be presented clearly and logically. Use tables, charts, and graphs to illustrate key findings. This section should focus on presenting the data without interpretation—save the analysis and implications for the discussion section.
The discussion interprets the results, explaining their meaning and significance. It connects the findings to the research objectives and existing literature. This section should also acknowledge any limitations and suggest areas for future research.
The conclusion provides a concise summary of the research findings and their implications. It reiterates the importance of the research and offers practical or theoretical recommendations.
References are crucial for academic integrity. They allow readers to verify sources and follow up on the research. Proper citation ensures that the work of other researchers is appropriately credited.
⏩II. Write notes on:(5 x 5 =25)
🔸1.What is simple random sampling?
Simple random sampling is a method of selecting a sample from a population in such a way that every possible sample of a given size has an equal chance of being selected. It is one of the most straightforward and commonly used techniques in probability sampling.
Key Characteristics of Simple Random Sampling:
1.Random Selection
Every individual in the population has an equal probability of being selected for the sample. This ensures that the sample is representative of the population.
2.Equal Probability
Each possible sample of the desired size has an equal chance of being chosen. This randomness minimizes bias and ensures unbiased estimation of population parameters.
3.Independence
The selection of each unit (person, item, etc.) is independent of the selection of other units. This means that the inclusion or exclusion of one unit does not influence the selection of another unit.
Steps Involved in Simple Random Sampling:
1.Define the Population
Clearly define the population from which the sample will be drawn.
2.List Population Elements
Create a list (sampling frame) of all elements (individuals, items, etc.) in the population.
3.Assign Numbers or Labels
Assign a unique number or label to each element in the population.
4.Random Selection
Use a random method (e.g., random number generator, lottery method) to select samples from the sampling frame.
5.Sample Collection
Include the elements corresponding to the selected numbers or labels in the sample.
Advantages of Simple Random Sampling:
It is straightforward and easy to understand.
It ensures each member of the population has an equal chance of selection.
Results are often highly representative of the population when properly conducted.
Limitations of Simple Random Sampling:
Requires a complete list of all population members (sampling frame), which may be difficult or impossible to obtain in some cases.
It may be inefficient when the population is large and dispersed.
It does not account for specific characteristics or strata within the population, which could be important for certain research purposes.
Simple random sampling is foundational in statistical research and forms the basis for many other sampling methods. When executed properly, it provides a reliable method for obtaining a representative sample and drawing valid conclusions about a population.
🔸2.What are the guidelines to design a good question?
Designing a good question involves several key guidelines to ensure clarity, relevance, and effectiveness:
1.Clarity
Ensure the question is clear and easily understandable. Avoid ambiguity or complex sentence structures that might confuse the respondent.
2.Relevance
The question should be directly related to the topic or purpose of the conversation or survey. Irrelevant questions can lead to confusion or frustration.
3.Specificity
Be specific in what you’re asking for. Vague questions can result in vague answers. Provide enough context if necessary but avoid overwhelming the respondent with unnecessary details.
4.Avoid leading questions
Leading questions suggest a particular answer or bias the respondent’s perspective. Keep questions neutral to get unbiased responses.
5.Use simple language
Tailor the language of your question to your audience. Avoid jargon or technical terms unless you’re sure respondents will understand them.
6.Open-ended vs. closed-ended
Choose the question type based on what kind of information you need. Closed-ended questions (e.g., yes/no, multiple-choice) are good for quantifiable data, while open-ended questions allow for more detailed responses and insights.
7.Consider context and order
Place questions in a logical order that flows naturally. Sometimes starting with broader questions and moving to more specific ones can be effective.
8.Balance and variety
If designing a survey or interview, ensure a mix of question types (e.g., demographic, opinion-based, factual) to gather comprehensive information.
9.Length and format
Keep questions concise and avoid unnecessary complexity. Long questions can be overwhelming and may deter respondents from providing thoughtful answers.
10.Pilot testing
Before finalizing questions, pilot test them with a small group to identify any potential issues with clarity, relevance, or format.
By adhering to these guidelines, you can create questions that effectively gather the information you need while ensuring a positive experience for respondents.
🔸3.List any five differences between basic research and applied research?
Here are five key differences between basic research and applied research:
1.Purpose and Goals
Basic Research
The primary goal is to expand knowledge and understanding of fundamental principles and theories without any immediate application in mind. It seeks to uncover new knowledge and theories.
Applied Research
Focuses on solving specific practical problems or answering specific questions with immediate applications in mind. It aims to solve real-world issues or improve existing processes, products, or services.
2.Nature of Knowledge
Basic Research
Generates theoretical knowledge that contributes to the understanding of underlying principles. It often explores concepts and phenomena without concern for immediate application.
Applied Research Produces practical knowledge aimed at addressing specific problems or needs. It focuses on the application of theories and principles to real-world scenarios.
3.Time Horizon
Basic Research
Typically has a long-term perspective, as the discoveries may not have immediate practical applications. It lays the foundation for future applied research and innovations.
Applied Research
Has a shorter time horizon because it aims to address immediate issues or problems. Results are expected to be applied relatively quickly to improve processes, products, or services.
4.Research Methods
Basic Research
Often employs experimental methods, theoretical models, and exploratory approaches to uncover new theories or principles. It focuses on understanding phenomena in-depth.
Applied Research
Utilizes practical research methods such as field studies, surveys, case studies, and simulations to solve specific problems or improve existing practices.
5.Outcome and Impact
Basic Research
The outcomes contribute to the body of scientific knowledge and may lead to unexpected discoveries or future applications. Its impact is often long-term and may not be immediately apparent.
Applied Research
The outcomes are directly applicable to practical situations and can lead to tangible improvements in products, processes, policies, or services. Its impact is more immediate and measurable in terms of practical solutions.
These differences highlight how basic research and applied research serve distinct purposes and contribute differently to knowledge and practical applications.
🔸4.What are the characteristics of relevant review of literature?
A relevant review of literature, whether for academic research, policy analysis, or any other purpose, should exhibit the following characteristics to ensure it effectively supports the objectives of the study:
1.Focus on the Research Topic
The literature review should be directly related to the research topic or question under investigation. It should clearly define the scope and boundaries of what is being reviewed.
2.Inclusion of Recent and Pertinent Sources
It should include recent and pertinent sources that are relevant to the current state of knowledge in the field. This ensures that the review reflects the most up-to-date understanding and research trends.
3.Critical Evaluation of Sources
A good literature review critically evaluates the quality, reliability, and validity of the sources included. It should assess the methodologies, theoretical frameworks, and findings of the studies reviewed.
4.Synthesis and Integration
The review should go beyond summarizing individual studies by synthesizing and integrating findings across different sources. This involves identifying common themes, contradictions, gaps, and areas of consensus or debate.
5.Contextualization
It should provide context for the reader by explaining how each source contributes to understanding the research topic. This may involve discussing the historical development of the topic, key debates, or theoretical perspectives.
6.Clear Structure and Organization
The literature review should have a clear structure and organization that guides the reader through the different sections or themes being discussed. This helps in presenting a coherent narrative of the existing knowledge.
7.Identification of Gaps and Future Directions
It should identify gaps in the existing literature that the current study aims to address. Additionally, it may suggest future research directions or areas where further investigation is needed.
8.Avoidance of Bias
The review should strive to be objective and unbiased in presenting different perspectives and findings from the literature. It should avoid cherry-picking studies that only support a specific viewpoint.
9.Citations and References
Proper citations and references should be provided for all sources reviewed, following the required citation style (e.g., APA, MLA). This ensures transparency and allows readers to locate the original sources.
10.Contribution to the Research Objectives
Ultimately, a relevant literature review should contribute to framing the research questions, hypotheses, or objectives of the study. It should establish the rationale and theoretical foundation for the research.
By embodying these characteristics, a literature review enhances the credibility, comprehensiveness, and relevance of the research study by situating it within the broader body of existing knowledge.
🔸5.List five sources of hypothesis?
Sources of hypotheses can come from various sources depending on the nature of the research and the field of study. Here are five common sources from which hypotheses can be derived:
1.Theory
Hypotheses often originate from existing theoretical frameworks or models. Theories provide systematic explanations of phenomena and can suggest relationships between variables that can be tested through hypotheses. For example, in psychology, Freud’s psychoanalytic theory could lead to hypotheses about the relationship between childhood experiences and adult behavior.
2.Previous Research
Reviewing existing literature and empirical studies can generate hypotheses based on findings or gaps identified in previous research. For instance, if previous studies consistently show a correlation between two variables, a hypothesis could be formulated to test a causal relationship.
3.Observation
Hypotheses can emerge from direct observations or anecdotal evidence of phenomena in real-world settings. Researchers may notice patterns or relationships that prompt them to formulate hypotheses for systematic investigation. For example, observing that students who study longer tend to perform better on exams could lead to a hypothesis about the relationship between study time and academic performance.
4.Problem Statement
When researchers identify a specific problem or issue that needs investigation, they can formulate hypotheses to address the problem. This often occurs in applied research contexts where the goal is to find solutions to practical problems. For example, in public health, a problem statement about the increasing rates of obesity could lead to hypotheses about the effectiveness of different interventions.
5.Expert Opinion
In some cases, hypotheses can arise from expert opinions or insights from practitioners in the field. Experts may have valuable experience and knowledge that can inspire hypotheses for further investigation. For instance, a hypothesis about the impact of a new technology in improving workplace productivity might stem from insights shared by industry professionals.
⏩III. Short answers on:(5 x 2 = 10)
🔸1.What Is Mean and Median?
Mean and median are two fundamental measures of central tendency used in statistics to describe the distribution of data:
In the mean represents the average value of a dataset, while the median represents the middle value. They are both useful for summarizing the central tendency of data, but they can give different insights, especially when dealing with skewed distributions or outliers.
🔸2.What Is cluster sampling?
Cluster sampling is a sampling method where the population is divided into clusters or groups, and then a random sample of these clusters is selected. Instead of sampling individuals directly, all individuals within the chosen clusters are included in the sample. It’s cost-effective and logistically efficient for large, dispersed populations, but requires careful consideration of cluster homogeneity and potential intra-cluster correlation in analysis.
🔸3.Mention the two forms of question?
Here are two common forms of questions:
1.Open-ended Questions
These questions allow respondents to answer in their own words without being restricted to specific choices or options. Open-ended questions typically begin with words like “what,” “why,” “how,” or “describe.” They are valuable for gathering detailed and qualitative information, as they encourage respondents to provide explanations and insights.
Example: “What are your thoughts on the recent changes in the company’s policies?”
2.Closed-ended Questions
These questions provide respondents with specific options or categories to choose from. They often start with words like “do,” “does,” “is,” “are,” “can,” “will,” etc. Closed-ended questions are useful for collecting quantitative data and facilitating analysis because responses can be easily categorized and summarized.
Example: “Do you agree with the new project timeline? (Yes/No)”
These two forms of questions serve different purposes in research, surveys, interviews, and everyday conversations, depending on whether you need detailed qualitative insights or structured quantitative data.
🔸4.Write any two advantages of observational method?
The observation method in research offers several advantages, including:
1.Naturalistic Environment
Observation allows researchers to study behavior in natural settings without artificial interference. This helps in capturing authentic and spontaneous behaviors that may be altered in a more controlled environment or through self-reporting.
2.Rich and Detailed Data
Observational methods often provide rich and detailed data about behaviors, interactions, and contexts. Researchers can directly witness and record behaviors as they occur, gaining insights that may not be captured through other methods like interviews or surveys.
These advantages make observation particularly valuable in fields such as anthropology, psychology, sociology, and education, where understanding behavior in its natural context is essential.
🔸5.Write the types of sampling technique?
There are several types of sampling techniques used in research, each with its own advantages and applicability depending on the study’s objectives and population characteristics. Here are some common types of sampling techniques:
1.Simple Random Sampling
Every member of the population has an equal chance of being selected. This is typically done using random number generators or lottery methods.
2.Stratified Sampling
The population is divided into homogeneous subgroups (strata) based on certain characteristics (e.g., age, gender, income), and then random samples are taken from each subgroup in proportion to their size in the population.
3.Systematic Sampling
Researchers choose every nth individual from the population after selecting a random starting point. For example, if every 5th person is selected from a list after a random starting point is chosen.
4.Cluster Sampling
The population is divided into clusters (e.g., geographical areas), and then clusters are randomly selected. All individuals within the selected clusters are included in the sample.
5.Convenience Sampling
Also known as availability sampling, this involves selecting individuals who are easiest to access or who meet the criteria for inclusion in the sample without any randomization.
6.Snowball Sampling
Used when it’s difficult to find participants. Researchers ask subjects to refer other subjects they may know.
7.Quota Sampling
Researchers select participants based on predetermined criteria.
⏩SECTION-B STATISTICS⏪
⏩I. Elaborate on:(1 x 15 = 15)
🔸1.Explain the measures of central tendency.
Measures of central tendency are statistical metrics used to identify the center point or typical value of a dataset. The three primary measures are the mean, median, and mode. Each provides different insights and is useful in various situations.
Definition: The mean, also known as the average, is the sum of all values in a dataset divided by the number of values.
Formula: Mean=∑XN\text{Mean} = \frac{\sum X}{N}Mean=N∑X
Where ∑X\sum X∑X is the sum of all values, and NNN is the number of values.
Example: Consider the following dataset of the number of patients seen by a nurse each day over a week: [10,15,20,25,30,35,40][10, 15, 20, 25, 30, 35, 40][10,15,20,25,30,35,40].Mean=10+15+20+25+30+35+407=1757=25\text{Mean} = \frac{10 + 15 + 20 + 25 + 30 + 35 + 40}{7} = \frac{175}{7} = 25Mean=710+15+20+25+30+35+40=7175=25
So, the mean number of patients seen per day is 25.
Definition: The median is the middle value in a dataset when the values are arranged in ascending or descending order. If the number of values is even, the median is the average of the two middle numbers.
Steps:
Example: Using the same dataset: [10,15,20,25,30,35,40][10, 15, 20, 25, 30, 35, 40][10,15,20,25,30,35,40],
The values are already in ascending order. The middle value (4th position) is 25.
So, the median is 25.
For an even-numbered dataset: [10,15,20,25,30,35][10, 15, 20, 25, 30, 35][10,15,20,25,30,35],Median=20+252=22.5\text{Median} = \frac{20 + 25}{2} = 22.5Median=220+25=22.5
So, the median is 22.5.
Definition: The mode is the value that appears most frequently in a dataset. A dataset may have one mode, more than one mode (bimodal or multimodal), or no mode at all if all values are unique.
Example: Consider the dataset: [10,15,20,20,25,30,35][10, 15, 20, 20, 25, 30, 35][10,15,20,20,25,30,35],
The value 20 appears twice, more frequently than any other value.
So, the mode is 20.
For a dataset with no repeating values: [10,15,20,25,30,35,40][10, 15, 20, 25, 30, 35, 40][10,15,20,25,30,35,40],
There is no mode, as all values occur only once.
⏩II. Short answers on:(5 x 2 = 10)
🔸1.Explain the two types of Hypothesis?
Hypotheses are statements or assumptions about the relationship between variables that are tested in research studies. There are two main types of hypotheses:
1.Null Hypothesis (H₀)
The null hypothesis is a statement of no effect or no relationship between variables. It suggests that any observed differences or relationships in the data are due to chance or random sampling variability.
Symbolically, the null hypothesis is denoted as ( H₀ ).
Example: “There is no significant difference in mean test scores between Group A and Group B.”
2.Alternative Hypothesis (H₁ or Hₐ)
The alternative hypothesis is the opposite of the null hypothesis. It asserts that there is a relationship between variables, suggesting that the observed differences or relationships in the data are not due to chance.
Symbolically, the alternative hypothesis is denoted as ( H₁ ) or ( Hₐ ).
Relationship between Null and Alternative Hypotheses
In hypothesis testing, researchers first assume the null hypothesis is true and then collect data to either reject or fail to reject it.
If the data provide strong evidence against the null hypothesis, researchers reject it in favor of the alternative hypothesis.
If there is not enough evidence to reject the null hypothesis, it is retained, although this does not mean the null hypothesis is proven true.
Types of Alternative Hypotheses
One-sided (or directional) alternative*: Specifies the direction of the effect (e.g., greater than, less than). For example, “The mean test score for Group A is greater than the mean test score for Group B.”
Two-sided (or non-directional) alternative
Does not specify the direction of the effect, only that there is a difference or relationship. For example, “There is a difference in mean test scores between Group A and Group B.”
These types of hypotheses are fundamental in hypothesis testing, where researchers use statistical methods to determine whether the observed data provide enough evidence to reject the null hypothesis in favor of the alternative hypothesis.
🔸2.What is Standard Deviation?
Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of values. It indicates how much individual data points differ from the mean (average) of the dataset. A low standard deviation suggests that data points are close to the mean, while a high standard deviation indicates that data points are spread out over a wider range of values. It is widely used to understand the distribution and variability within data, helping to assess consistency or variability in different datasets or populations.
🔸3.Explain Reliability?
Reliability refers to the consistency or stability of a measure, test, or research finding. It indicates the extent to which a measurement tool or procedure produces consistent results under consistent conditions. High reliability means that the measurement is dependable and trustworthy, with minimal variability or error. Reliability is essential in research to ensure that data are consistent and replicable, thereby enhancing the validity and credibility of study findings.
🔸4.Write any two guidelines for making frequency table?
Creating a frequency table is a fundamental step in organizing and summarizing categorical data. Here are two guidelines to consider when making a frequency table:
1.Define Clear Categories
Ensure that categories in the frequency table are mutually exclusive and collectively exhaustive. This means every data point should fit into one and only one category, and all possible categories should cover all data points.
Avoid overlapping categories or leaving out potential categories that could apply to the dataset.
2.Organize Data Clearly
Arrange categories in a logical order that facilitates understanding and interpretation of the data. For nominal data (categories without inherent order), alphabetical order is often used.
For ordinal data (categories with a natural order), arrange categories from lowest to highest or according to their natural sequence.
By following these guidelines, you can create a frequency table that effectively summarizes categorical data and provides clear insights for analysis and interpretation.
🔸5.What is Nominal scale?
A nominal scale is a type of measurement scale in statistics and research where variables are categorized into distinct, non-ordered groups or categories. These categories are used solely for identification purposes and do not have any inherent numerical value or ranking among them. Nominal scales are used to classify data into mutually exclusive and exhaustive categories, such as gender (male, female, other), marital status (single, married, divorced, widowed), or types of vehicles (sedan, SUV, truck). They are essential for organizing and summarizing qualitative data without implying any quantitative relationships between categories.