THIRD YEAR PAPER IV-NURSING RESEARCH & STATISTICS-NOVEMBER 2022
⏩ SECTION-A NURSING RESEARCH ⏪
⏩I. Elaborate on: (1 x 15 = 15)
1.🔸a) What is Problem statement?
A problem statement succinctly describes the issue or challenge that a project, research study, or initiative aims to address. It serves as a clear and concise statement of the problem that needs to be solved or investigated. Key elements of a problem statement typically include:
1.Description of the Problem:
Clearly articulates the nature and scope of the problem or issue being addressed. It defines the gap or the specific area where improvement or resolution is needed.
2.Importance and Relevance:
Explains why the problem is significant and why it needs attention. It may highlight the consequences of not addressing the problem or the potential benefits of solving it.
3.Contextual Background:
Provides background information or context to help stakeholders understand the problem’s origins, contributing factors, or previous attempts to address it.
4.Specific Objectives:
States the specific objectives or goals that the project or study aims to achieve in relation to solving the problem.
5.Scope and Limitations:
Defines the boundaries of the problem statement, including what is included and what is excluded from consideration.
A well-crafted problem statement is essential for guiding the direction of a project or research study, ensuring that efforts are focused on addressing the core issues effectively. It helps stakeholders, researchers, or project teams understand the purpose and rationale behind the initiative and serves as a basis for developing strategies and solutions.
🔸b) What are the Criteria of a good research Problem?
The criteria for a good research problem are essential to ensure that the research is focused, relevant, and feasible. Here are the key criteria:
1.Significance:
The research problem should address an important issue or gap in knowledge that has practical relevance and implications. It should contribute to advancing understanding, theory, practice, or policy in the relevant field.
2.Feasibility:
The problem should be manageable within the resources (time, funding, expertise) available to the researcher. It should be realistic in terms of data collection, analysis, and interpretation.
3.Originality:
The research problem should be novel and innovative. It should not simply replicate previous studies but should contribute new insights, perspectives, or methods to the field.
4.Clarity:
The problem statement should be clear, specific, and well-defined. It should avoid ambiguity and clearly articulate the nature and scope of the problem being investigated.
5.Researchability:
The problem should be amenable to empirical investigation using appropriate research methods. There should be available data sources, access to participants (if applicable), and suitable methodologies for data collection and analysis.
6.Relevance:
The problem should be relevant to the current state of knowledge in the field and address questions that are of interest to stakeholders, practitioners, or policymakers.
7.Ethical Considerations:
The research problem should consider ethical principles and guidelines, ensuring that research is conducted in an ethical and responsible manner, particularly when involving human subjects.
8.Theoretical Framework:
Ideally, the research problem should be grounded in relevant theoretical frameworks or concepts that provide a foundation for understanding and interpreting findings.
By adhering to these criteria, researchers can ensure that their research problems are well-defined, meaningful, and capable of generating valuable contributions to their respective fields of study.
🔸c) Write the types of statement of problem with example and formulate the objectives and hypothesis for it.
different types of research problems, provide examples for each, and then formulate objectives and hypotheses for each type:
Example Problem Statement:
“To investigate the prevalence of smartphone addiction among college students.”
Objectives:
To determine the percentage of college students who exhibit signs of smartphone addiction.
To identify demographic factors (age, gender, academic year) associated with smartphone addiction.
Hypotheses:
Null Hypothesis (H0):
There is no significant difference in smartphone addiction prevalence across different demographic groups.
Alternative Hypothesis (H1):
Smartphone addiction prevalence varies significantly across different demographic groups.
Example Problem Statement:
“To explore the factors influencing employee job satisfaction in the IT industry.”
Objectives:
To identify key factors that contribute to employee job satisfaction in the IT industry.
To uncover potential relationships between job satisfaction and organizational factors (e.g., leadership, work environment).
Hypotheses:
Null Hypothesis (H0):
There is no significant relationship between leadership style and employee job satisfaction.
Alternative Hypothesis (H1):
Different leadership styles significantly impact employee job satisfaction.
Example Problem Statement:
“To determine the effect of mindfulness meditation on reducing stress levels among university students.”
Objectives:
To assess the impact of mindfulness meditation interventions on stress levels among university students.
To measure changes in stress levels before and after participating in a mindfulness meditation program.
Hypotheses:
Null Hypothesis (H0):
There is no significant reduction in stress levels among university students after participating in mindfulness meditation.
Alternative Hypothesis (H1):
Mindfulness meditation significantly reduces stress levels among university students.
Example Problem Statement:
“To compare the academic performance of students in traditional classroom settings versus online learning environments.”
Objectives:
To evaluate differences in academic achievement between students in traditional classrooms and those in online learning environments.
To analyze factors that may influence academic performance across these two settings.
Hypotheses:
Null Hypothesis (H0):
There is no significant difference in academic performance between students in traditional classrooms and online learning environments.
Alternative Hypothesis (H1):
Students in traditional classrooms perform better academically compared to those in online learning environments.
Formulation of Objectives and Hypotheses
For each type of problem statement, formulating objectives involves specifying what the research aims to achieve or investigate. Hypotheses are statements that propose a relationship or difference between variables, which will be tested through data analysis. It’s crucial to ensure that objectives are clear and achievable, while hypotheses are testable and grounded in the research problem.
These examples illustrate how problem statements, objectives, and hypotheses are interconnected in guiding research design and analysis. Adjustments may be necessary based on specific research contexts and goals.
⏩II. Write notes on: (5 x 5 = 25)
🔸1.Types of Assumptions.
Assumptions in research are foundational beliefs or propositions that are taken for granted or accepted as true, often without empirical evidence. They can influence how research is conducted, interpreted, and applied. Here are several types of assumptions commonly encountered in research:
1.Theoretical Assumptions:
These are assumptions based on theoretical perspectives or frameworks guiding the research. For example, a study on human behavior might assume that individuals act rationally based on self-interest, drawing from economic theories.
2.Methodological Assumptions:
These assumptions pertain to the methods and techniques used in research. For instance, a survey-based study assumes that participants accurately report their opinions or behaviors.
3.Statistical Assumptions:
These assumptions involve the statistical techniques applied to analyze data. Examples include assumptions of normality, independence of observations, and homogeneity of variances in statistical tests.
4.Contextual Assumptions:
These assumptions relate to the specific context or setting in which the research takes place. Researchers may assume certain cultural norms, organizational practices, or historical events relevant to their study.
5.Logical Assumptions:
These assumptions are about logical reasoning or argumentation within the research. For instance, a deductive study assumes that if the premises are true, then the conclusion logically follows.
6.Ontological and Epistemological Assumptions:
These assumptions concern the nature of reality (ontological) and the nature of knowledge and how it is acquired (epistemological). For example, a qualitative study may assume that reality is socially constructed, and knowledge is subjective and context-dependent.
7.Practical Assumptions:
These assumptions pertain to practical considerations in research design and implementation. They include assumptions about resources, time constraints, and ethical considerations.
8.Implicit Assumptions:
These are assumptions that are not explicitly stated but are implied or inferred from the research design, methodology, or interpretation of findings.
Awareness of assumptions is crucial in research to critically evaluate their potential impact on the validity and reliability of findings. Researchers should explicitly identify and justify assumptions to enhance transparency and rigor in their studies.
🔸2.Critique of nursing research studies.
Critiquing nursing research studies involves systematically evaluating various aspects of the study to assess its strengths, limitations, and overall credibility. Here are key areas to consider when critiquing nursing research:
1.Research Problem and Purpose:
Is the research problem clearly identified and justified?
Does the study address a significant issue in nursing practice, theory, or policy?
Are the research objectives or aims clearly stated and aligned with the problem?
2.Literature Review:
Has the literature review provided a comprehensive overview of relevant research and theoretical perspectives?
Are the sources current and credible?
Does the literature review support the rationale for the study?
3.Theoretical or Conceptual Framework:
Is there a theoretical or conceptual framework guiding the study?
Does the framework provide a clear basis for understanding the study’s variables and relationships?
4.Research Design and Methodology:
What is the study design (e.g., quantitative, qualitative, mixed methods)?
Is the design appropriate for addressing the research question?
Are sampling methods clearly described and justified?
Are data collection methods appropriate and well-described?
5.Data Analysis:
Are data analysis methods clearly described?
Are the statistical or qualitative analyses appropriate for the data collected?
Are findings presented objectively and supported by the data?
6.Results and Findings:
Are the results clearly presented and relevant to the research question?
Are statistical or qualitative findings interpreted accurately?
Are limitations of the study acknowledged and discussed?
7.Ethical Considerations:
Has the study received ethical approval?
Are ethical considerations (e.g., consent, confidentiality) adequately addressed in the study design and conduct?
8.Implications and Recommendations:
Are implications of the study’s findings discussed?
Are recommendations for practice, policy, or further research supported by the findings?
9.Overall Evaluation:
What are the strengths and weaknesses of the study?
How does the study contribute to nursing knowledge, practice, or policy?
What are the implications for future research or practice based on the study’s findings?
Critiquing nursing research involves a balanced assessment of these components to evaluate the study’s rigor, relevance, and contribution to the field. It helps to enhance understanding of the research process and informs evidence-based nursing practice and decision-making.
🔸3.Stages of systemic review.
A systematic review follows a structured process to comprehensively synthesize and analyze existing research on a specific topic or question. The stages involved in conducting a systematic review typically include:
1.Formulating the Research Question:
Clearly define the research question or objective that the systematic review aims to address. This should include specifying the population, intervention or exposure, comparison (if applicable), and outcomes of interest (often abbreviated as PICO).
2.Developing Inclusion and Exclusion Criteria:
Establish criteria for selecting studies to be included in the review. These criteria may specify study designs, types of participants, interventions or exposures, outcomes, and publication date ranges.
3.Conducting a Systematic Search:
Perform a systematic and comprehensive search of relevant literature databases (e.g., PubMed, Cochrane Library, PsycINFO) to identify potentially eligible studies. Use predefined search terms and strategies based on the research question.
4.Screening and Selection of Studies:
Independently screen titles and abstracts of identified studies against the inclusion and exclusion criteria. Full-text articles of potentially relevant studies are then assessed for eligibility based on the criteria.
5.Data Extraction:
Develop a standardized data extraction form to systematically extract relevant information from included studies. This typically includes study characteristics (e.g., study design, sample size), participant characteristics, intervention or exposure details, outcomes measured, and results.
6.Assessing Risk of Bias:
Evaluate the methodological quality and risk of bias of included studies. This may involve using tools such as the Cochrane Risk of Bias Tool for randomized controlled trials or the Newcastle-Ottawa Scale for observational studies.
7.Data Synthesis and Analysis:
Synthesize the findings from individual studies using appropriate methods (e.g., meta-analysis for quantitative data, thematic synthesis for qualitative data). Quantitative data may involve pooling results to calculate summary effect sizes, whereas qualitative data may involve identifying themes or patterns.
8.Interpreting Findings:
Interpret the synthesized findings in relation to the research question. Discuss the implications of the findings, including strengths, limitations, and potential biases of the review.
9.Reporting the Review:
Prepare a structured report of the systematic review following established guidelines (e.g., PRISMA – Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The report should include details on the review methods, findings, conclusions, and recommendations for practice or future research.
10.Updating and Iterative Process:
Systematic reviews may need updating periodically to incorporate new evidence. It is an iterative process that involves critical appraisal and refinement of methods based on feedback and further research.
By following these stages, researchers ensure a rigorous and transparent approach to synthesizing evidence, which is crucial for informing healthcare practice, policy-making, and further research.
🔸4.Bio Physiological parameters.
Bio-physiological parameters refer to measurable characteristics of biological systems or organisms that reflect their physiological status, functioning, or health. These parameters are essential for understanding and monitoring various aspects of biological processes. Here are some commonly measured bio-physiological parameters:
1.Heart Rate:
The number of heartbeats per minute, which can indicate cardiovascular health, stress levels, and physical fitness.
2.Blood Pressure:
The force of blood against the walls of arteries during and between heartbeats, providing information about cardiovascular function and risk of cardiovascular diseases.
3.Body Temperature:
The degree of heat in the body, which reflects metabolic activity and can indicate fever or hypothermia.
4.Respiratory Rate:
The number of breaths taken per minute, which is important for assessing respiratory function and oxygenation.
5.Blood Oxygen Saturation (SpO2):
The percentage of hemoglobin in the blood that is saturated with oxygen, crucial for assessing respiratory function and oxygen delivery to tissues.
6.Blood Glucose Level:
The concentration of glucose in the blood, which is essential for energy metabolism and is used to diagnose and monitor diabetes.
7.Electrocardiogram (ECG or EKG):
A graphical representation of the electrical activity of the heart over time, used to diagnose heart conditions and monitor cardiac function.
8.Electroencephalogram (EEG):
A recording of electrical activity along the scalp, used to assess brain function and diagnose neurological disorders.
9.Galvanic Skin Response (GSR):
Changes in the electrical conductivity of the skin, often used as a measure of emotional arousal or stress.
10.Body Mass Index (BMI):
A calculation based on height and weight, used to assess body composition and risk of obesity-related health conditions.
These bio-physiological parameters are fundamental in clinical practice, biomedical research, and health monitoring, providing valuable insights into the state of health and functioning of individuals across different physiological systems.
🔸5.Dependent Variable.
A dependent variable in research is the variable that is observed, measured, or affected in response to changes in another variable, known as the independent variable. It represents the outcome or effect that researchers are interested in understanding, explaining, or predicting. Here are key characteristics and considerations regarding dependent variables:
1.Definition:
The dependent variable is the variable being tested and measured in a scientific experiment. It is expected to change when the independent variable is manipulated.
2.Role:
It is used to assess the impact or influence of the independent variable(s) on a particular phenomenon or outcome of interest.
3.Types:
Dependent variables can be categorized into several types:
Continuous Variables:
Variables that can take on any value within a range (e.g., height, weight, reaction time).
Categorical Variables:
Variables with distinct categories or groups (e.g., presence/absence of a condition, yes/no responses).
Ordinal Variables:
Variables with ordered categories (e.g., Likert scales ranging from strongly agree to strongly disagree).
4.Example:
In a study examining the effect of a new medication (independent variable) on blood pressure (dependent variable), blood pressure measurements would be the dependent variable. Researchers would measure changes in blood pressure to determine the medication’s effectiveness.
5.Measurement:
Dependent variables should be operationally defined and measured using valid and reliable methods to ensure accurate and meaningful results.
6.Control:
Researchers often control for extraneous variables (confounding variables) that could influence the dependent variable to isolate the effects of the independent variable(s).
7.Relationship:
The relationship between the independent and dependent variables is typically analyzed using statistical methods to determine the strength and direction of the association.
Understanding the dependent variable is crucial for designing research studies, formulating hypotheses, selecting appropriate research methods, and interpreting findings accurately. It represents the outcome or response that researchers seek to explain or predict through their investigations.
⏩III. Short answers on: (5 x 2 = 10)
🔸1.Define plagiarism.
Plagiarism is the act of using someone else’s words, ideas, or work without proper acknowledgment or attribution, presenting them as one’s own. It involves copying or closely imitating another person’s work and passing it off as original, whether intentionally or unintentionally. Plagiarism can occur in various forms, such as directly copying text, paraphrasing without citation, or using someone else’s ideas or concepts without giving credit. It is considered unethical and is often a violation of academic integrity and intellectual property rights.
🔸2.Write the types of closed ended question.
Closed-ended questions are structured questions that provide respondents with a limited set of response options to choose from. They are typically used in surveys, questionnaires, and structured interviews to gather specific, quantifiable data. Here are the types of closed-ended questions:
1.Dichotomous Questions:
These questions offer two response options, usually “yes” or “no,” or “true” or “false.”
2.Multiple Choice Questions:
These questions provide respondents with several predefined options to choose from.
Example: “Which of the following social media platforms do you use? (a) Facebook, (b) Twitter, (c) Instagram, (d) LinkedIn”
3.Ordinal Scale Questions:
These questions ask respondents to rank or rate items based on a specified ordinal scale.
Example: “Please rate your satisfaction with our service: (1) Very Dissatisfied, (2) Dissatisfied, (3) Neutral, (4) Satisfied, (5) Very Satisfied”
4.Likert Scale Questions:
These questions present respondents with a statement and a range of response options indicating their degree of agreement or disagreement.
Example: “Please indicate how strongly you agree or disagree with the following statement: The new policy improves efficiency. (1) Strongly Disagree, (2) Disagree, (3) Neither Agree nor Disagree, (4) Agree, (5) Strongly Agree”
5.Semantic Differential Scale Questions:
These questions ask respondents to rate an object or concept on a series of bipolar adjectives.
Example: “Please rate the product on the following dimensions: Cheap – Expensive, Simple – Complex, Reliable – Unreliable”
Closed-ended questions are effective for collecting quantitative data quickly and efficiently, providing structured responses that are easy to analyze. However, they may limit respondents’ ability to express nuanced or complex opinions compared to open-ended questions.
🔸3.Expand PICO.
PICO is an acronym used in evidence-based practice and research to formulate a well-defined clinical or research question. It stands for:
P:
Population or Patient – Describes the specific group of individuals or patients being studied.
I: Intervention – Specifies the intervention, treatment, exposure, or diagnostic test being considered.
C: Comparison – States the alternative intervention, treatment, or comparison group (if applicable).
O: Outcome – Identifies the outcome or result that is measured or observed.
Expanding PICO involves detailing each component to precisely define the research question or clinical query, ensuring clarity and specificity in the investigation. This structured approach helps researchers and clinicians develop focused questions that guide the search for evidence and inform decision-making in healthcare settings.
🔸4.Define null hypothesis.
A null hypothesis (H0) is a precise statement that suggests there is no significant difference, effect, or relationship between variables being studied. It represents the default assumption or status quo, suggesting that any observed differences or effects in data are due to random chance or sampling variability. In statistical hypothesis testing, researchers typically aim to test the null hypothesis against an alternative hypothesis (H1), which proposes that there is indeed a relationship, effect, or difference between variables. The outcome of hypothesis testing allows researchers to make conclusions about the presence or absence of significant findings in their study.
🔸5.Define Target population.
The target population refers to the specific group of individuals or elements that a researcher intends to study and draw conclusions about. It represents the larger population to which the research findings are meant to be applicable or generalized. Researchers define the target population based on specific characteristics or criteria relevant to their study objectives, and they typically select a sample from this population for practical reasons. The goal is to ensure that findings from the study can be reasonably extended or applied to the broader target population.
⏩ SECTION-B STATISTICS⏪
⏩I. Elaborate on:(1 x 15 = 15)
🔸1.What is a statistical average? Describe the characteristics of a good statistical average.
A statistical average, also known simply as an average, is a measure of central tendency that represents the typical or central value in a set of data. There are several types of statistical averages:
1.Mean:
The arithmetic mean is calculated by summing all values in a dataset and dividing by the number of observations.
2.Median:
The median is the middle value when data is ordered from smallest to largest. It divides the dataset into two equal halves.
3.Mode:
The mode is the most frequently occurring value in a dataset.
Characteristics of a good statistical average include:
1.Representativeness:
A good average should accurately represent the central tendency or typical value of the dataset. It should reflect the distribution of data points effectively.
2.Sensitivity to Extreme Values:
An ideal average should be less affected by extreme values (outliers) in the dataset. The median and mode are less sensitive to outliers compared to the mean.
3.Ease of Interpretation:
The average should be easy to understand and interpret. It should provide meaningful information about the dataset without requiring complex calculations.
4.Applicability:
The type of average chosen should be appropriate for the type of data being analyzed (e.g., mean for interval or ratio data, median for ordinal data).
5.Robustness:
A good average should be robust against variations in sample size or sampling method. It should provide consistent results across different subsets of the data.
6.Mathematical Properties:
The average should have desirable mathematical properties, such as being unbiased (mean) or robust to changes in data values (median).
Choosing the appropriate average depends on the nature of the data and the specific research or analytical objectives. Each type of average has its strengths and limitations, and researchers select the most suitable measure based on the characteristics of their dataset and the purpose of their analysis.
⏩II. Short answers on: (5 x 2 = 10)
🔸1.Define probability.
Probability is a numerical measure that quantifies the likelihood or chance of a specific event occurring. It ranges from 0 (indicating impossibility) to 1 (indicating certainty). Probability theory is fundamental in mathematics and statistics, used to analyze uncertainty, make predictions, and inform decision-making in various fields.
🔸2.Write the types of ‘t’ test.
There are two main types of t-tests commonly used in statistical analysis:
1.Independent Samples t-test (Student’s t-test):
Used to compare the means of two independent groups to determine if there is a significant difference between them.
Assumptions include independence of observations within and between groups, approximately normal distribution of data within each group, and homogeneity of variances between groups.
Example: Comparing the mean test scores of students from two different schools.
2.Paired Samples t-test (Dependent t-test):
Used to compare the means of two related groups or conditions (paired observations) to determine if there is a significant difference between them.
Participants or subjects are measured twice (before and after intervention, or under two different conditions).
Assumptions include paired observations, approximately normal distribution of differences between pairs, and absence of systematic differences between pairs.
Example: Comparing the mean scores of students on a test before and after a tutoring program.
Both types of t-tests are widely used in research to assess whether the difference observed between groups or conditions is statistically significant, helping researchers draw conclusions about the effects of interventions, treatments, or other factors of interest.
🔸3.What is Cohort study?
A cohort study is a type of observational study where a group of individuals, known as a cohort, is followed over a period of time to investigate how certain factors (exposures or interventions) affect their outcomes. In brief:
Definition: Cohort studies observe a group of people (cohort) without altering their environment or exposure and track them longitudinally to assess how specific factors influence their health outcomes.
Purpose:
They are used to identify and measure the association between exposure to a risk factor or intervention and the development of diseases or health outcomes over time.
Design:
Participants are initially free of the outcome being studied and are categorized based on their exposure status. They are then followed forward in time to see if they develop the outcome of interest.
Types:
Cohort studies can be prospective (where participants are enrolled and followed forward in time) or retrospective (where data on exposures and outcomes are collected from past records).
Advantages:
They allow researchers to study multiple outcomes, examine rare exposures, establish temporal relationships between exposures and outcomes, and calculate incidence rates.
Challenges:
Cohort studies can be time-consuming, expensive, and susceptible to loss to follow-up or attrition over the study period.
🔸4.Find the Mode of following data 2,3,3,5,3,4,3,3,3,4,2,2.
To find the mode using a formula, we can identify the value that occurs most frequently in the dataset. Here’s a step-by-step approach:
1.List the Data: ( 2, 3, 3, 5, 3, 4, 3, 3, 3, 4, 2, 2 )
2.Count the Frequency of Each Value:
3.Identify the Value with the Highest Frequency:
Among these, 3 has the highest frequency, which is 6 times.
Therefore, using the formulaic approach:
[ \text{Mode} = \text{Value with the highest frequency} ]
In this case, the mode of the dataset ( 2, 3, 3, 5, 3, 4, 3, 3, 3, 4, 2, 2 ) is 3.
🔸5.Define Incidence Rate.
Incidence rate is a measure used in epidemiology to quantify the occurrence of new cases of a disease, injury, or other health-related event within a specific population during a defined period of time. It represents the rate at which new cases develop and is calculated as the number of new cases divided by the population at risk, multiplied by the time period. This metric helps researchers and public health officials understand the risk and burden of diseases and inform preventive strategies and healthcare planning.