PBBSC SY INTRODUCTION TO NURSING RESEARCH AND STATISTICS UNIT 4
Sampling techniques and methods of data collection
Sampling Techniques and Methods of Data Collection
1. Sampling Techniques
Sampling is the process of selecting a subset of individuals or items (a sample) from a larger population to make inferences about the entire population. Proper sampling ensures that the data collected is representative and reliable.
Types of Sampling Techniques
A. Probability Sampling Each member of the population has a known, non-zero chance of being selected. It ensures randomization and reduces bias.
Simple Random Sampling:
Every individual in the population has an equal chance of being selected.
Example: Using a random number generator to select participants.
Stratified Sampling:
The population is divided into subgroups (strata) based on specific characteristics, and samples are randomly selected from each stratum.
Example: Dividing a population into age groups and randomly selecting participants from each group.
Systematic Sampling:
Selects participants at regular intervals from a list.
Example: Choosing every 5th patient from a hospital admission list.
Cluster Sampling:
Divides the population into clusters, randomly selects some clusters, and collects data from all individuals in those clusters.
Example: Randomly selecting villages in a region for a rural health survey.
Multistage Sampling:
Combines multiple sampling methods (e.g., cluster sampling followed by simple random sampling).
Example: Selecting hospitals first, then randomly choosing patients from each hospital.
B. Non-Probability Sampling Not all individuals have an equal chance of being selected. This is often used when probability sampling is impractical.
Convenience Sampling:
Selects participants who are easily accessible.
Example: Interviewing patients available in a clinic on a specific day.
Purposive Sampling:
Participants are chosen based on specific criteria or purpose.
Example: Selecting only diabetic patients for a study on diabetes management.
Quota Sampling:
Ensures a specific number of participants from each subgroup, but without random selection.
Example: Surveying 50 male and 50 female nurses.
Snowball Sampling:
Existing participants recruit others who meet the study criteria.
Example: Gathering participants for a study on rare diseases.
Judgmental Sampling:
The researcher uses their judgment to select participants.
Example: Choosing experienced nurses for a study on advanced nursing practices.
2. Methods of Data Collection
Data collection involves gathering information to address the research objectives. It can be categorized into primary (directly from the source) and secondary (from existing sources).
A. Primary Data Collection Methods
Observation:
Systematically recording behaviors, events, or conditions as they occur naturally.
Types:
Participant Observation: The researcher becomes part of the group.
Non-Participant Observation: The researcher observes without interaction.
Example: Observing handwashing practices in a hospital.
Interviews:
Collecting detailed information through verbal communication.
Types:
Structured: Predetermined questions.
Semi-Structured: Some flexibility in questions.
Unstructured: Open-ended and conversational.
Example: Interviewing patients about their experiences with telehealth.
Questionnaires:
A set of written questions used to collect information.
Types:
Open-Ended: Participants write their responses.
Close-Ended: Participants choose from provided options.
Example: A survey on job satisfaction among nurses.
Focus Group Discussions:
Group interviews with a small number of participants to explore specific topics.
Example: Discussing barriers to accessing mental health services.
Experiments:
Manipulating variables in a controlled setting to observe effects.
Example: Testing the effectiveness of a new wound care protocol.
Case Studies:
In-depth study of a single individual, group, or event.
Example: Studying the recovery process of a stroke patient.
B. Secondary Data Collection Methods
Document Analysis:
Reviewing existing documents such as reports, policies, or medical records.
Example: Analyzing patient discharge summaries for patterns in readmissions.
Literature Review:
Synthesizing findings from previously published research.
Example: Reviewing studies on infection control measures.
Database Analysis:
Using existing databases like census data, hospital records, or online repositories.
Example: Using hospital records to analyze trends in maternal mortality.
Comparison of Sampling and Data Collection Methods
Aspect
Sampling
Data Collection
Definition
Selecting a subset of the population.
Gathering information from selected samples.
Purpose
Ensures representativeness of the sample.
Collects relevant data for analysis.
Types
Probability and Non-Probability Sampling.
Primary (e.g., interviews) and Secondary (e.g., documents).
Example
Randomly selecting patients from a hospital.
Conducting interviews with the selected patients.
Sampling techniques ensure that the chosen participants accurately represent the population, reducing bias and enhancing the reliability of the study.
Data collection methods gather the necessary information systematically and comprehensively to address the research questions.
Sampling
Sampling in Research
Sampling refers to the process of selecting a subset of individuals, events, or objects (a sample) from a larger group (population) to represent the whole. It is a crucial step in research to ensure the study’s findings are reliable, valid, and generalizable.
Key Concepts in Sampling
Population:
The entire group of individuals or items the researcher is interested in studying.
Example: All patients in a hospital.
Sample:
A smaller group selected from the population for the study.
Example: 100 patients selected from a hospital’s patient list.
Sampling Frame:
A list or database from which the sample is drawn.
Example: Hospital admission records.
Sampling Unit:
The individual item or person that is selected.
Example: Each patient in the hospital.
Sampling Error:
The difference between the characteristics of the sample and the population.
Example: If the sample does not include enough elderly patients, it might not reflect the population’s age distribution accurately.
Types of Sampling
A. Probability Sampling
In this method, every member of the population has a known and equal chance of being selected. It reduces bias and enhances generalizability.
Simple Random Sampling:
Participants are selected randomly, ensuring equal chances for all.
Example: Using a random number generator to select participants from a patient list.
Stratified Sampling:
The population is divided into subgroups (strata) based on characteristics like age, gender, or profession. A random sample is taken from each subgroup.
Example: Dividing a hospital staff by departments and randomly selecting from each.
Systematic Sampling:
Selecting every nth individual from a list.
Example: Choosing every 5th name on a patient admission register.
Cluster Sampling:
The population is divided into clusters (e.g., schools, villages), and entire clusters are randomly selected.
Example: Randomly selecting five hospitals from a region and surveying all nurses in those hospitals.
Multistage Sampling:
Combines several sampling methods, often used for large-scale studies.
Example: Selecting cities (cluster sampling), then schools within those cities (random sampling), and finally students (systematic sampling).
B. Non-Probability Sampling
In this method, not all members of the population have a chance of being selected. It is often used in exploratory or qualitative research.
Convenience Sampling:
Selecting individuals who are easily accessible.
Example: Interviewing patients present in the outpatient department.
Purposive Sampling:
Selecting participants based on specific criteria relevant to the research.
Example: Choosing only diabetic patients for a study on diabetes management.
Quota Sampling:
Selecting a fixed number of participants from specific subgroups, but without random selection.
Example: Interviewing 50 male and 50 female nurses.
Snowball Sampling:
Existing participants recruit others for the study, often used for hard-to-reach populations.
Example: Collecting data from people with rare medical conditions by referrals.
Judgmental Sampling:
The researcher uses their judgment to select participants.
Example: Choosing experienced healthcare providers for a study on critical care protocols.
Steps in Sampling Process
Define the Population:
Clearly identify the group you want to study.
Example: “All nurses working in urban hospitals.”
Develop a Sampling Frame:
Create a list or database of the population.
Example: A list of registered nurses in a specific city.
Select the Sampling Technique:
Choose between probability or non-probability sampling based on the research objective.
Determine Sample Size:
Use statistical methods to calculate the number of participants needed.
Example: A sample size of 300 for a population of 10,000 may be sufficient for a survey.
Implement Sampling:
Select participants using the chosen technique.
Example: Use a random number table to pick participants.
Collect Data:
Gather information from the selected participants.
Factors Affecting Sampling
Purpose of the Study:
Exploratory studies may use non-probability sampling, while experimental studies require probability sampling.
Population Size:
Larger populations may need more sophisticated sampling techniques.
Resource Availability:
Time, budget, and manpower can influence sampling decisions.
Desired Accuracy:
Probability sampling is more accurate but may require more resources.
Accessibility of Participants:
Some populations may be hard to reach, requiring purposive or snowball sampling.
Advantages and Disadvantages of Sampling
Advantages:
Cost and Time Efficiency: Sampling is quicker and cheaper than studying the entire population.
Feasibility: Allows research on populations that are too large to study in entirety.
Flexibility: Sampling techniques can be adapted to different research needs.
Disadvantages:
Sampling Bias: Poor sampling techniques can lead to non-representative samples.
Errors in Generalization: Results may not fully represent the population.
Resource Dependence: Some techniques require extensive resources for implementation.
Applications of Sampling in Nursing Research
Patient Satisfaction Surveys:
Use systematic or random sampling to select patients for feedback.
Intervention Studies:
Use stratified sampling to compare outcomes among different demographic groups.
Workforce Studies:
Use quota sampling to study nursing staff from diverse departments.
Instruments-questionnarie. Interview
Instruments: Questionnaire and Interview
Instruments such as questionnaires and interviews are essential tools for data collection in research. They help gather information systematically and are tailored to suit the research objectives and study design.
1. Questionnaire
Definition
A questionnaire is a structured set of written questions designed to collect data from respondents. It can include open-ended or close-ended questions and is usually self-administered.
Characteristics of a Good Questionnaire
Clear and Concise: Avoids ambiguity or complex language.
Relevant: Focused on research objectives.
Logical Sequence: Questions flow logically from general to specific.
User-Friendly: Easy to understand and complete.
Types of Questions
Close-Ended Questions:
Respondents select from predefined options.
Example: “How satisfied are you with the nursing care received?
(a) Very satisfied
(b) Satisfied
(c) Neutral
(d) Dissatisfied”
Open-Ended Questions:
Respondents provide their own answers.
Example: “What improvements would you suggest in nursing care?”
Likert Scale Questions:
Measures attitudes or perceptions on a scale.
Example: “Rate your agreement: ‘The staff were attentive to my needs.’
1 (Strongly Disagree) to 5 (Strongly Agree)”
Dichotomous Questions:
Offers two response options.
Example: “Did you receive adequate information about your medication? Yes/No”
Multiple-Choice Questions:
Respondents choose one or more options.
Example: “What services did you use?
(a) Emergency care
(b) Outpatient services
(c) Inpatient care”
Advantages of Questionnaires
Efficient: Collects data from many respondents quickly.
Cost-Effective: Especially for large-scale studies.
Standardized: Ensures consistency in data collection.
Disadvantages of Questionnaires
Limited Depth: Open-ended responses may lack detail.
Non-Response Bias: Some participants may not respond.
Misinterpretation: Questions may be misunderstood without clarification.
Example in Nursing Research:
A questionnaire to assess patient satisfaction with hospital services.
2. Interview
Definition
An interview is a method of collecting data through direct interaction between the researcher and the participant. It can be structured, semi-structured, or unstructured.
Types of Interviews
Structured Interview:
Predefined set of questions with no deviations.
Example: A nurse interviewing patients about specific symptoms.
Semi-Structured Interview:
Combines structured questions with flexibility for follow-ups.
Example: A healthcare manager discussing staffing challenges.
Unstructured Interview:
Open conversation with no predefined questions.
Example: Exploring patient experiences during recovery.
Characteristics of a Good Interview
Preparedness: Clear understanding of the topic and questions.
Effective Communication: Good listening and questioning skills.
Ethical Conduct: Ensures confidentiality and respect.
Advantages of Interviews
In-Depth Data: Provides detailed and nuanced responses.
Clarification: The interviewer can explain or probe further.
Flexibility: Adapts to the participant’s responses.
Disadvantages of Interviews
Time-Consuming: Especially for large sample sizes.
Potential Bias: The interviewer’s presence may influence responses.
Example in Nursing Research:
Conducting interviews with nurses to understand challenges in end-of-life care.
Comparison: Questionnaire vs. Interview
Aspect
Questionnaire
Interview
Format
Written, self-administered.
Verbal, researcher-led.
Depth of Information
Limited depth, standardized responses.
Greater depth, personalized responses.
Cost and Time
Cost-effective and time-efficient.
More expensive and time-consuming.
Clarification
No opportunity for clarification.
Immediate clarification possible.
Response Rate
May be lower due to non-responses.
Typically higher due to personal interaction.
Bias
Less prone to interviewer bias.
May involve interviewer bias.
When to Use
Questionnaire: When the research involves large samples, standardized data, or when participants are geographically dispersed.
Interview: When exploring detailed personal experiences, perceptions, or qualitative data.
Observation schedule, records, measurements
Observation Schedule, Records, and Measurements
These tools and methods are vital for data collection in research, particularly when the focus is on observing behaviors, recording data systematically, and taking precise measurements. Below is an explanation of each:
1. Observation Schedule
Definition
An observation schedule is a systematic tool used to record specific behaviors, events, or conditions during an observation. It ensures consistency and objectivity in collecting data.
Types of Observation
Participant Observation:
The researcher becomes part of the group being observed.
Example: A nurse observing hygiene practices in a ward while actively participating in patient care.
Non-Participant Observation:
The researcher observes without interacting.
Example: Watching how patients interact with a hospital’s automated check-in system.
Structured Observation:
Predefined criteria or checklist guide the observation.
Example: Using a checklist to record the frequency of handwashing by staff.
Unstructured Observation:
No predefined criteria; the researcher records observations as they occur.
Example: Observing general patient-staff interactions in a hospital.
Advantages of Observation Schedules
Captures real-time data on behaviors and events.
Useful for studying non-verbal behaviors or unconscious actions.
Ensures systematic and unbiased recording when structured.
Disadvantages
Observer bias may influence data interpretation.
Time-intensive and requires skilled observers.
Limited to visible behaviors; does not capture internal states.
Example in Nursing:
An observation schedule to record the adherence of nurses to infection control protocols.
2. Records
Definition
Records refer to documents or files that contain relevant information collected over time. These can be used as a secondary source of data in research.
Types of Records
Patient Records:
Include medical history, treatment plans, and progress notes.
Example: Using patient records to analyze trends in hypertension management.
Administrative Records:
Include hospital policies, staffing data, or financial records.
Example: Analyzing staff-to-patient ratios in a hospital.
Incident Reports:
Document specific events such as accidents or medication errors.
Example: Reviewing incident reports to identify patterns in falls among elderly patients.
Educational Records:
Include student performance data, attendance, or curriculum details.
Cost-effective and time-saving for secondary analysis.
Allows large-scale data collection without direct engagement.
Disadvantages
Data may be incomplete or outdated.
Access to records may be restricted due to confidentiality.
Requires careful interpretation to avoid bias.
Example in Nursing:
Analyzing patient discharge summaries to study readmission rates.
3. Measurements
Definition
Measurement involves assigning numerical values to variables using standardized tools and techniques to ensure accuracy and reliability.
Types of Measurements
Direct Measurements:
Physically measured variables.
Example: Measuring blood pressure using a sphygmomanometer.
Indirect Measurements:
Assessed through proxies or derived values.
Example: Measuring patient satisfaction through a survey score.
Subjective Measurements:
Based on personal opinions or perceptions.
Example: Rating pain on a visual analog scale.
Objective Measurements:
Based on factual, quantifiable data.
Example: Measuring body temperature using a thermometer.
Tools for Measurement
Biophysical Instruments:
Devices for clinical measurements (e.g., ECG machines, glucometers).
Scales:
Likert Scale: Measures attitudes or perceptions.
Visual Analog Scale: Measures subjective experiences like pain intensity.
Tests:
Standardized tests for cognitive or skill assessments.
Example: Nursing competency assessments.
Advantages of Measurements
Provides precise and quantifiable data.
Facilitates comparisons across groups or time periods.
Standardized tools enhance reliability and validity.
Disadvantages
Requires calibration and training to use measurement tools.
Subjective measurements may vary between individuals.
Example in Nursing:
Measuring the effectiveness of a wound care intervention by recording wound healing rates.
Comparison of Observation, Records, and Measurements
Aspect
Observation Schedule
Records
Measurements
Definition
Real-time recording of events/behaviors.
Existing data from documents or files.
Assigning numerical values to variables.
Data Source
Direct observation.
Secondary sources.
Instruments or scales.
Purpose
To study behaviors or processes.
To analyze historical or documented data.
To quantify variables accurately.
Example
Observing staff compliance with protocols.
Reviewing patient discharge records.
Measuring blood pressure.
Advantages
Real-time, captures behaviors.
Cost-effective, longitudinal data.
Precision and reliability.
Disadvantages
Time-intensive, observer bias.
May be incomplete or outdated.
Requires standardized tools and training.
Applications in Nursing Research
Observation Schedule:
Monitoring patient mobility post-surgery.
Records:
Analyzing trends in hospital-acquired infections.
Measurements:
Evaluating the impact of a new diet plan on diabetic patients’ blood sugar levels.
Reliability and validity or instruments
Reliability and Validity of Instruments
In research, reliability and validity are critical properties of data collection instruments. They ensure that the measurements are consistent and accurately represent the concept being studied.
1. Reliability
Definition
Reliability refers to the consistency or stability of an instrument when it measures the same concept under the same conditions.
Types of Reliability
Test-Retest Reliability:
Measures the consistency of results when the same test is administered to the same participants at two different points in time.
Example: A patient satisfaction survey produces similar results when given a week apart under unchanged conditions.
Inter-Rater Reliability:
Assesses the agreement between two or more observers or raters.
Example: Two nurses independently evaluating the same patient’s pain level using a standardized scale.
Parallel-Forms Reliability:
Measures consistency between two equivalent versions of an instrument.
Example: Comparing two forms of a nursing competency test.
Internal Consistency Reliability:
Assesses the extent to which items on a test measure the same construct.
Example: Cronbach’s Alpha is commonly used to test internal consistency.
How to Enhance Reliability
Use standardized procedures for administering the instrument.
Train observers or raters to ensure consistency.
Pre-test the instrument with a similar population.
Example in Nursing Research:
Evaluating the reliability of a patient stress assessment tool.
2. Validity
Definition
Validity refers to the accuracy or truthfulness of an instrument in measuring what it is intended to measure.
Types of Validity
Content Validity:
Ensures the instrument covers all aspects of the concept being measured.
Example: A nursing competency exam includes questions on both theoretical knowledge and clinical skills.
Construct Validity:
Determines whether the instrument truly measures the theoretical construct it claims to measure.
Types:
Convergent Validity: The instrument correlates well with other measures of the same construct.
Discriminant Validity: The instrument does not correlate with measures of different constructs.
Example: A stress scale should correlate with anxiety measures but not with unrelated constructs like physical fitness.
Criterion-Related Validity:
Assesses how well the instrument predicts or correlates with an external criterion.
Types:
Predictive Validity: The instrument predicts future outcomes.
Example: A nursing aptitude test predicting clinical performance.
Concurrent Validity: The instrument correlates with another measure taken at the same time.
Example: A blood pressure monitor correlating with a gold-standard device.
Face Validity:
The extent to which the instrument appears to measure what it claims to, based on subjective judgment.
Example: A questionnaire on patient satisfaction appears to cover relevant aspects like care, communication, and comfort.
How to Enhance Validity
Consult subject matter experts during instrument development.
Conduct pilot testing to refine the instrument.
Use multiple measures to validate findings.
Example in Nursing Research:
Validating a new pain assessment scale for postoperative patients.
Comparison of Reliability and Validity
Aspect
Reliability
Validity
Definition
Consistency of measurement.
Accuracy of measurement.
Focus
Ensures repeatability of results.
Ensures the instrument measures the correct construct.
Measurement
Assessed through test-retest, inter-rater, etc.
Assessed through content, construct, and criterion validity.
Example
A stress scale gives consistent results over time.
The stress scale accurately measures stress, not anxiety.
Relationship Between Reliability and Validity
Reliability is necessary but not sufficient for validity:
An instrument can be reliable (consistent) but not valid (measuring the wrong thing).
Example: A scale consistently measures weight but cannot measure height (reliable but not valid).
Validity often implies reliability:
If an instrument is valid, it is likely reliable as well.
Ensuring Reliability and Validity in Instruments
Pilot Testing:
Conduct a pilot study to test the instrument on a small sample.
Standardization:
Use consistent procedures for data collection.
Expert Review:
Consult experts to assess the instrument for both content and construct validity.
Statistical Testing:
Use statistical methods like Cronbach’s Alpha for reliability and factor analysis for validity.
Applications in Nursing Research
Reliability:
Testing the consistency of a fall-risk assessment tool in elderly patients.
Validity:
Ensuring a depression screening tool accurately identifies patients with depressive symptoms.