B.Sc. (Nursing)-THIRD YEAR PAPER IV – NURSING RESEARCH & STATISTICS-MAY 2022
⏩SECTION-A NURSING RESEARCH⏪
⏩I. Elaborate on: (1 x 15 = 15)
🔸1.Explain the qualitative research design with suitable examples.
Qualitative research design focuses on understanding phenomena from a holistic, contextual, and subjective perspective. It aims to explore the meanings, concepts, definitions, characteristics, metaphors, symbols, and descriptions of things. Qualitative research is typically used in the social sciences and humanities to gather deep insights into human behavior, emotions, and experiences.
⏩II. Write notes on: (5 x 5 = 25)
🔸1.Steps of scientific method.
The scientific method is a systematic approach used to investigate phenomena, acquire new knowledge, or correct and integrate previous knowledge. It is characterized by empirical and measurable evidence subject to specific principles of reasoning. Here are the steps involved in the scientific method:
🔸2.Type I and Type II error.
In statistical hypothesis testing, errors can occur when making decisions about rejecting or failing to reject a null hypothesis. These errors are classified into two types: Type I and Type II errors. Understanding these errors is crucial for interpreting the results of hypothesis tests correctly.
Definition: A Type I error occurs when the null hypothesis (H₀) is rejected when it is actually true. It represents a false positive result, indicating that there is an effect or difference when none exists.
Significance Level (α): The probability of committing a Type I error is denoted by α (alpha), also known as the significance level. Common choices for α are 0.05, 0.01, or 0.10.
Example: Suppose a new drug is being tested to see if it reduces blood pressure more effectively than a placebo. The null hypothesis (H₀) is that the drug has no effect on blood pressure. If a statistical test leads to rejecting H₀ (concluding that the drug is effective) when in reality, the drug does not reduce blood pressure, a Type I error has occurred.
Definition: A Type II error occurs when the null hypothesis (H₀) is not rejected when it is actually false. It represents a false negative result, indicating that there is no effect or difference when one actually exists.
Power of the Test (1 – β): The probability of committing a Type II error is denoted by β (beta). The power of a test, which is 1 – β, is the probability of correctly rejecting a false null hypothesis. Higher power means a lower chance of a Type II error.
Example: Continuing with the drug example, the null hypothesis (H₀) is that the drug has no effect on blood pressure. If the statistical test fails to reject H₀ (concluding that the drug is not effective) when in reality, the drug does reduce blood pressure, a Type II error has occurred.
There is a trade-off between Type I and Type II errors. Reducing the probability of one type of error usually increases the probability of the other. Here are some ways to manage this balance:
Imagine a court trial scenario:
In this context, setting a high threshold for evidence (a low α) reduces the risk of convicting an innocent person (Type I error) but increases the risk of letting a guilty person go free (Type II error).
By understanding and managing these errors, researchers and practitioners can make more informed and reliable decisions based on statistical tests.
🔸3.Barriers of research utilization.
Research utilization refers to the process of applying research findings to improve practice, policies, or decision-making in various fields such as healthcare, education, social sciences, and more. Despite the benefits of integrating research into practice, several barriers often hinder this process:
1.Lack of Awareness
Professionals may not be aware of relevant research findings due to limited access to journals, databases, or time constraints. This ignorance prevents them from considering evidence-based practices.
2.Access to Research
Difficulty accessing full-text articles, databases, or research summaries can impede research utilization. High subscription costs or restrictive access policies in academic journals are common obstacles.
3.Research Complexity
Some research studies are highly technical or specialized, making it challenging for practitioners to interpret and apply findings to their specific contexts.
4.Time Constraints
Professionals often face heavy workloads and time pressures, leaving little time to search, evaluate, and apply research findings effectively.
5.Skills and Training
Insufficient training in critical appraisal skills or research methodology can hinder practitioners’ ability to understand and implement research findings.
6.Organizational Culture
Organizational cultures that prioritize tradition over innovation or lack a supportive environment for research utilization may discourage practitioners from adopting new evidence-based practices.
7.Resistance to Change
Individuals and organizations may resist change due to fear of uncertainty, perceived risks, or inertia, even when evidence suggests better outcomes with new practices.
8.Resource Constraints
Limited funding, staffing, or infrastructure may prevent organizations from implementing changes recommended by research findings.
9.Lack of Collaboration
Poor communication and collaboration between researchers, practitioners, policymakers, and other stakeholders can hinder the dissemination and adoption of research findings.
10.Political and Policy Barriers
Policy decisions or political influences may not align with research evidence, leading to discrepancies between recommended practices and actual implementation.
11.Publication Bias
Research findings that show statistically significant results are more likely to be published, leading to potential biases in the evidence base available for decision-making.
Addressing these barriers requires concerted efforts from researchers, practitioners, organizations, and policymakers to promote a culture of research utilization, improve access to evidence, provide training in critical appraisal, and foster collaborative partnerships for effective knowledge translation and implementation.
🔸4.Methods of sampling technique.
Sampling techniques are methods used to select a subset of individuals or units from a larger population for the purpose of conducting research or gathering information. The choice of sampling method depends on factors such as the research objectives, the nature of the population, and practical considerations. Here are some common methods of sampling:
1.Simple Random Sampling:
Description:
Each member of the population has an equal chance of being selected. This is done without any bias or preference.
Example:
Using a random number generator to select households from a list of all households in a city.
2.Stratified Sampling:
Description:
The population is divided into homogeneous subgroups (strata), and then random samples are taken from each subgroup.
Example:
Dividing students in a school into different grade levels (strata) and then randomly selecting students from each grade level.
3.Systematic Sampling:
Description:
Selecting every nth individual from the population after a random starting point has been identified.
Example:
Choosing every 10th patient who arrives at a clinic for a survey on healthcare satisfaction.
4.Cluster Sampling:
Description:
Dividing the population into clusters, randomly selecting some clusters, and then sampling all individuals or units within those selected clusters.
Example:
Selecting several classrooms (clusters) in a school at random and surveying all students within those classrooms.
5.Convenience Sampling:
Description:
Choosing individuals or units that are easiest to access or readily available to participate in the study.
Example: Surveying shoppers in a mall or interviewing people passing by on the street.
6.Snowball Sampling:
Description:
Initial participants refer others who fit the criteria for the study, and the sample grows like a rolling snowball.
Example:
Studying a rare disease by asking initial patients to refer other patients they know who have the same condition.
7.Quota Sampling:
Description:
Ensuring that the sample has the same proportions of individuals as the entire population with respect to known characteristics.
Example:
Ensuring that a survey of university students reflects the gender and age distribution of the entire student body.
8.Purposive Sampling (or Judgmental Sampling):
Description:
Selecting individuals or units based on the researcher’s judgment about which ones will best serve the research purposes.
Example:
Selecting key informants who have specific knowledge or experiences related to the research topic.
9.Multistage Sampling:
Description:
Combines two or more sampling techniques. It involves multiple stages of sampling, such as using cluster sampling to select clusters and then using simple random sampling to select individuals within those clusters.
Example:
Surveying households in different neighborhoods (clusters) using cluster sampling and then selecting individuals within those households using simple random sampling.
Each sampling method has its strengths and limitations, and the choice of method depends on the specific research objectives, population characteristics, available resources, and practical constraints.
🔸5. Sources of research problem.
Research problems arise from various sources, reflecting both theoretical and practical concerns that researchers seek to address through systematic investigation. Here are five common sources of research problems:
1.Literature Gaps and Reviews:
Researchers often identify gaps or inconsistencies in existing literature during literature reviews. These gaps suggest areas where further research is needed to clarify, expand, or reconcile conflicting findings or theories.
Example:
After reviewing studies on the effectiveness of a particular teaching method, a researcher notices a lack of research focusing on its application in a specific demographic group.
2.Practical Issues and Real-World Challenges:
Practical problems encountered in professional practice, policy-making, or everyday life can inspire research questions. These issues may arise from observed trends, problems, or opportunities that require investigation to find effective solutions.
Example:
A healthcare provider notices a rise in medication non-adherence among elderly patients and wants to explore factors contributing to this issue.
3.Theoretical Frameworks and Paradigms:
Theoretical frameworks and paradigms can suggest new research questions by prompting researchers to explore phenomena from different perspectives or to test theoretical propositions.
Example:
A sociologist influenced by feminist theory might investigate how gender roles influence career choices among college graduates.
4.Personal Experience and Curiosity:
Researchers may be inspired by personal experiences, interests, or curiosity about a particular topic. These motivations can lead to the formulation of research questions that reflect the researcher’s passion or desire to explore a specific area.
Example:
A biologist who grew up near a polluted river might investigate the effects of contaminants on local wildlife.
5.Policy and Societal Concerns:
Research problems may also stem from societal issues, policy debates, or public concerns that require empirical investigation to inform decision-making or to understand social phenomena.
Example:
Government policymakers may commission research to evaluate the impact of a new healthcare policy on access to healthcare services in underserved communities.
Importance of Identifying Research Problems:
Relevance and Significance:
Addressing a research problem ensures that the study is relevant to current knowledge gaps or practical needs.
Contribution to Knowledge:
By addressing a research problem, researchers contribute new insights or evidence to the field, advancing theoretical understanding or informing practice.
Guidance for Methodology:
The research problem guides the selection of appropriate research methods and design, ensuring the study’s validity and reliability.
Identifying a clear and well-defined research problem is crucial for setting the direction of the study, framing research questions, and guiding the entire research process from literature review to data analysis and interpretation.
⏩III. Short answers on: (5 x 2 = 10)
🔸1.Write the characteristics of good research.
Good research is characterized by several key attributes:
1.Clear Purpose
It should have a well-defined objective or question that it seeks to answer.
2.Logical Design
The research should be carefully planned and structured, with a clear methodology and approach.
3.Thorough Literature Review
It should build upon existing knowledge and demonstrate familiarity with relevant prior research.
4.Robust Methodology
The methods chosen should be appropriate for the research question, reliable, and capable of producing valid results.
5.Accurate Data Collection
Data should be gathered meticulously, using reliable techniques and tools.
6.Impartial Analysis
Analysis of data should be objective and unbiased, ensuring the findings are credible.
7.Clear Presentation
Results should be presented clearly and logically, with appropriate supporting evidence.
8.Ethical Considerations
The research should adhere to ethical guidelines, ensuring fairness, respect for participants, and transparency.
9.Contribution to Knowledge
Good research should add to the existing body of knowledge, advancing understanding or offering practical benefits.
10.Peer Review
It should be subject to scrutiny and evaluation by peers or experts in the field to ensure quality and reliability.
By embodying these characteristics, good research becomes a valuable contribution to its field of study.
🔸2.Write the types of research objectives.
Research objectives can be broadly categorized into three main types based on their scope and purpose:
1.Exploratory Objectives
These objectives aim to explore a subject, phenomenon, or area where little is known or understood. Exploratory research seeks to generate insights, ideas, or hypotheses rather than providing definitive answers. It is often used at the beginning of a research project to clarify concepts, identify potential variables, or understand the scope of a problem. Example: “To explore the attitudes of teenagers towards social media usage.”
2.Descriptive Objectives
Descriptive research objectives aim to describe characteristics of a population or phenomenon being studied. This type of research focuses on answering questions such as who, what, when, where, and how. It often involves collecting data through surveys, observations, or existing datasets to provide a detailed picture of the subject under investigation. Example: “To describe the demographics and purchasing behavior of customers in a specific market segment.”
3.Explanatory (or Causal) Objectives
Explanatory research objectives seek to establish causal relationships between variables. This type of research goes beyond describing or exploring phenomena; it aims to identify causes and effects. Explanatory research often involves conducting experiments or longitudinal studies to determine how changes in one variable lead to changes in another. Example: “To examine the effect of sleep duration on academic performance among university students.”
These types of objectives guide the research process and help researchers focus their efforts on achieving specific goals. Depending on the nature of the study, research objectives may overlap or evolve throughout the research journey.
🔸3.Expand MCR, RCT.
here are the expansions for MCR and RCT:
1.MCR
MCR stands for Multiple Correspondence Analysis.
Multiple Correspondence Analysis is a statistical technique used for analyzing the relationships between categorical variables. It is particularly useful when dealing with datasets where there are multiple categorical variables, and it aims to uncover patterns or associations between these variables in a multidimensional space.
2.RCT
RCT
stands for Randomized Controlled Trial.
Randomized Controlled Trial
is a type of scientific experiment, often used in medical and social sciences research, where participants are randomly assigned to different groups. One group receives the intervention (such as a new treatment or therapy), while the other group (the control group) receives either no intervention or a standard treatment. RCTs are designed to measure the effectiveness of interventions by comparing outcomes between the groups, while controlling for potential biases that could affect the results. They are considered the gold standard for evaluating the efficacy of new treatments or interventions.
🔸4.Difference between Assumption and Hypothesis.
Assumptions and hypotheses are both important concepts in research, but they serve different purposes and are used in different contexts:
1.Assumption
Definition
An assumption is a statement that is considered to be true without any proof or evidence. It is often taken for granted or accepted as a basis for reasoning or argumentation.
Purpose
Assumptions are used to simplify the complexity of a problem or situation. They provide a starting point or a foundation upon which further reasoning or research can be built.
Role in Research
In research, assumptions can be explicit or implicit. Researchers may make assumptions about the reliability of certain data sources, the behavior of study participants, or the generalizability of findings. These assumptions help frame the research question and guide the methodology.
2.Hypothesis
Definition
A hypothesis is a specific statement or prediction that is tested through scientific research. It is formulated based on existing theories, observations, or preliminary data.
Key Differences
Basis
Assumptions are accepted as true without proof, while hypotheses are testable assertions based on existing knowledge or theories.
Purpose
Assumptions simplify and provide a starting point for research, whereas hypotheses guide the empirical testing and validation process.
Nature
Assumptions are often broader and foundational, whereas hypotheses are specific and focused on testing relationships or predictions.
In assumptions are foundational beliefs or premises that guide research, whereas hypotheses are specific predictions that are systematically tested through empirical investigation. Both are essential for conducting rigorous and meaningful research.
🔸5.What are the methods of communication in research?
In research, communication methods refer to the ways in which researchers disseminate their findings, collaborate with peers, and interact with the broader scientific community. Here are the key methods of communication in research:
1.Research Articles and Papers
Publishing research findings in peer-reviewed journals is the primary method of communication in academia. Articles and papers undergo rigorous review by experts in the field before publication to ensure quality and validity.
2.Conferences and Symposia
Researchers often present their work at academic conferences and symposia. These events provide opportunities to share findings, receive feedback, and network with other researchers in the same or related fields.
3.Theses and Dissertations
Graduate students and researchers often communicate their research through theses and dissertations, which are formal documents presenting original research conducted as part of academic degrees.
4.Books and Book Chapters
Researchers may also communicate their work through books or book chapters. Books typically cover broader topics or provide comprehensive reviews of research in a particular field.
5.Posters and Presentations
In addition to conferences, researchers may present their work in poster sessions or through oral presentations at academic institutions, research seminars, or workshops.
6.Preprints and Working Papers
Some researchers share early versions of their work through preprints (manuscripts made publicly available before peer review) or working papers (drafts circulated among peers for feedback).
7.Online Platforms and Blogs
With the rise of digital communication, researchers may use online platforms, academic social networks (e.g., ResearchGate, Academia.edu), or personal blogs to share insights, updates, and preliminary findings.
8.Media and Press Releases
Researchers may engage with the media to communicate their findings to the general public or policymakers, especially when their research has implications beyond academic circles.
9.Collaborative Tools and Networks
Communication in research also involves collaboration through online platforms like Slack, Microsoft Teams, or Google Workspace, which facilitate real-time communication, file sharing, and collaborative writing among research teams.
10.Peer Review
Peer review is a critical aspect of scholarly communication where experts in the field evaluate the quality and validity of research before it is published, ensuring rigorous standards of scientific integrity.
Effective communication in research is essential for advancing knowledge, fostering collaboration, and ensuring the credibility and impact of research findings within the scientific community and beyond.
⏩SECTION-B STATISTICS⏪
⏩I.Elaborate on:(1 x 15 = 15)
🔸1.Explain the measures of central tendency with suitable examples.
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: (\text{Mean} = \frac{\sum X}{N})
Where (\sum X) is the sum of all values, and (N) 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]).
[
\text{Mean} = \frac{10 + 15 + 20 + 25 + 30 + 35 + 40}{7} = \frac{175}{7} = 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]),
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]),
[
\text{Median} = \frac{20 + 25}{2} = 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]),
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]),
There is no mode, as all values occur only once.
Understanding and using these measures of central tendency helps in summarizing and interpreting data effectively, providing insights into the typical values and the overall distribution of the data.
⏩II. Short answers on: (5 x 2 = 10)
🔸1.What is degree of freedom?
n statistics, the degree of freedom (often abbreviated as df) refers to the number of independent pieces of information available to estimate or calculate a parameter. It is a concept that varies depending on the type of statistical analysis being conducted:
1.In Sample Statistics
Degree of freedom in the context of sample statistics (such as mean, variance, etc.) is typically ( n – 1 ), where ( n ) is the number of observations in the sample. This adjustment accounts for the fact that one degree of freedom is lost when estimating a sample statistic because the sample mean (or other statistic) must adhere to the constraint that the sum (or sum of squares) equals the specified value.
2.In Hypothesis Testing
In hypothesis testing, the degree of freedom refers to the number of values in the final calculation of a statistic that are free to change.
🔸2.Define histogram.
A histogram is a graphical representation of the distribution of data. It consists of bars where each bar represents the frequency or count of data values falling within specific intervals (bins) of the data range. The horizontal axis (x-axis) shows the intervals or categories of the data, and the vertical axis (y-axis) represents the frequency of occurrence of data values within each interval. Histograms are used to visualize the shape, central tendency, spread, and skewness of datasets, providing insights into the underlying data distribution.
🔸3.Mention the parametric tests.
Parametric tests are statistical tests that make assumptions about the parameters (such as mean, variance, etc.) of the population from which the sample data are drawn. These assumptions typically include the data being normally distributed and having homogeneous variances. Some common parametric tests include:
1.T-tests
One-sample t-test:
Compares the mean of a single sample to a known value or hypothesized mean.
Independent samples t-test
Compares the means of two independent groups.
Paired samples t-test
Compares the means of two related groups (e.g., pre-test and post-test measurements from the same individuals).
2.Analysis of Variance (ANOVA)
One-way ANOVA Compares means of two or more independent groups.
Two-way ANOVA
Examines the effects of two categorical independent variables on a continuous dependent variable.
3.Regression Analysis
Simple Linear Regression
Examines the relationship between one predictor variable and a continuous outcome variable.
Multiple Linear Regression
Examines the relationship between multiple predictor variables and a continuous outcome variable.
4.Parametric Correlation Tests
Pearson correlation coefficient
Measures the strength and direction of the linear relationship between two continuous variables under the assumption of normality.
5.Parametric Tests for Proportions
Z-test for proportions
Tests whether the proportion of successes (or events) in a sample is significantly different from a hypothesized population proportion.
Parametric tests are powerful when their assumptions are met, allowing for precise estimation and hypothesis testing. However, if data violate these assumptions (e.g., non-normality, unequal variances), non-parametric tests may be more appropriate.
🔸4.List down the levels of measurement.
Levels of measurement, also known as scales of measurement, classify variables into categories based on the nature and characteristics of the data. There are four main levels of measurement:
1.Nominal Scale
The nominal scale categorizes data into mutually exclusive categories or groups.
Examples: Gender (male, female), marital status (single, married, divorced), types of cars (SUV, sedan, truck).
Properties: Categories have no inherent order or numerical value. Nominal data can only be classified and counted.
2.Ordinal Scale
The ordinal scale ranks data into categories that have a meaningful order or sequence.
Examples: Educational levels (high school diploma, bachelor’s degree, master’s degree), rating scales (poor, fair, good, excellent).
Properties: Categories have a relative position or rank, but the differences between ranks may not be equal or meaningful.
3.Interval Scale
The interval scale not only ranks data but also specifies the exact differences between values.
Examples: Temperature measured in Celsius or Fahrenheit, IQ scores.
Properties: Equal intervals between successive points on the scale are meaningful, but there is no true zero point (zero does not indicate absence of the quantity).
4.Ratio Scale
The ratio scale has all the properties of the interval scale, but it also has a true zero point.
Examples: Height, weight, age, income (measured in dollars).
Properties: Includes a meaningful zero point where zero indicates the absence of the quantity being measured. Ratios of values are meaningful (e.g., one value is twice as much as another).
These levels of measurement are important because they dictate the types of statistical analyses that can be applied to the data. Higher levels of measurement provide more information and allow for more sophisticated statistical techniques
🔸5.What are the limitations of statistics?
the limitations of statistics in brief:
1.Assumptions and Data Quality
Statistics relies on assumptions about data quality and representativeness, which can lead to inaccurate conclusions if data are biased or incomplete.
2.Correlation vs. Causation
Statistical analysis can establish correlations between variables but cannot prove causation without additional evidence and careful study design.
3.Interpretation Challenges
Misinterpretation of statistical results is common due to complexities in analysis, leading to errors when not considering confounding factors or biases.
4.Sample Size and Power
Small sample sizes limit generalizability, and statistical tests may lack power to detect true effects, especially with small effect sizes.
5.Over-Reliance on P-values
Misuse or over-reliance on p-values in hypothesis testing can lead to flawed decision-making and misinterpretation of statistical significance.
6.Ethical and Privacy Concerns
Handling sensitive data raises ethical concerns, necessitating careful consideration of privacy and ethical guidelines in statistical analyses.
7.Complexity and Context Dependency
Statistical models simplify real-world complexities, requiring understanding of context and assumptions underlying statistical techniques.
8.Limitations of Statistical Techniques
Different statistical methods have specific assumptions and limitations, and misapplication can lead to erroneous conclusions.
9.Temporal and Spatial Dependencies
Statistical analyses may overlook temporal trends or spatial dependencies in data, potentially biasing estimates or inferences.
10.Subjectivity in Analysis
Statistical analysis involves subjective decisions such as model selection and interpretation, influencing outcomes and conclusions.
Awareness of these limitations helps ensure appropriate application of statistical methods, accurate interpretation of results, and effective communication of findings in research and decision-making processes.