NCM 111 Midterm iyak iyak MOD 69

2 Classifications of study designs are

Descriptive and Analytical

Descriptive

Describes a disease or health condition, phenomenon or intervention

• Is more exploratory

• focuses on WHAT

• Profiles group characteristics

• Assumes no hypothesis

• Does not require 2 groups to compare on over time

Analytic

Examines association (tests the hypothesis)

• Is more Explanatory

• Analyzes WHY the group has characteristics

• Focuses on WHY

• Assumes a hypothesis

• Requires comparison groups

Types of Descriptive study designs

1 Case reports/Case series

2 Prevalence survey

3 Ecologic Study

2 Types of Analytics

Observational

• Exposure/Outcome variables are just observed

Experimental

• Exposure variables are assigned

Types of Analytical Observational studies

1 Cross sectional

2 Case control

3 Cohort

4 Ecologic

Types of Experimental Analytical studies

1 Clinical Trials (RCT)

2 Field Trials

3 Community intervention Trials

Design Hierarchy

1) Experiments / Interventions studies

2) Case-control studies

3) Cross sectional studies

4) Prevalence studies / surveys

5) Ecologic studies

6) Case series

7) Case reports

Case study and Case series

An account of interesting characteristics observed in a group of subjects

• The subject does not need to be a person

  • Organizations

  • Institutes

  • Political units

• The subjects must be seen over a relatively short period of time

  • Why short period? they basically want to determine if anything interesting is worth studying
    

• Does not include control subjects

  • researchers examine one single group with the specific characteristic or condition they're interested in

• Does not involve any research hypothesis

  • The findings will lead to the formulation of a hypothesis

Independent variable - Cause/Predict/Explain - Dependent variable

Descriptive Cross-Sectional Studies/Prevalence Survey

The study looks at how often (occurence) and where (distribution) or a particular phenomenon, disease or event happens within a population

Prevalence is computed through

• Person

• Place

• Time

The KAP survey describes pattern of health service utilization and compliance

• Knowledge

• Attitude

• Practice

“Able to catch in time, descriptive in nature”

They are designed to describe the current state of affairs without diving into the cause-and-effect relationships

Ecological Studies

The focus of observation and analysis is on a group or population as a whole, rather than on individuals. (Aggregate) it attempts to describe and analyze the characteristics of the group

• Exposure Homogenous within Population: This means that within any given population (like a specific city), the exposure levels to something (like air pollution) are similar for everyone in that group.

• Exposure Differs between Populations: Different populations (like different cities) can have different exposure levels. For example, one city might have high air pollution, while another has low.

• Individual Measurements Impossible: It’s difficult or impractical to measure exposure levels for each individual person in the study. Instead, researchers look at the average exposure for the whole group.

• Quick Method of Examining Associations: Ecological studies can quickly show if there’s a relationship between exposure (like air pollution) and outcomes (like lung disease) across different populations.

The most serious flaw of Ecological studies

Ecological Fallacies, meaning It happens when researchers mistakenly assume that what is true for the group is also true for each individual within the group. For example, if a city has high smoking rates and high lung cancer rates, it’s incorrect to assume that every individual smoker has a high risk of lung cancer without considering other factors.

Cohort Studies

A cohort study involves following a group (or population) of people over time to observe how certain factors influence outcomes. It's a specific type of population study that focuses on how exposures (like smoking) impact health outcomes (like lung disease)

ADVANTAGES

It may yield information of the incidence of disease/phenomenon/event

It is possible to compute for the relative risk

• It is the strongest observational design for establishing cause-effect

• Efficient for studies with rare exposure factors, temporal relationship between exposure and disease is clearly defined in

  Cohort studies

DISADVANTAGES

• It is time consuming

• It often requires a large sample size

• It is expensive

• It is not efficient for the study of rare diseases/phenomenon/events

• It Losses to follow-up may diminish validity

• Changes over time in diagnostic methods may lead to biased results

Two Types of Cohort studies

Prospective (A prospective study is about following a group of participants into the future to see how exposure to certain factors affects outcomes.)

• Assessed at the start of the study

• Outcome is followed up into the future

Retrospective (A retrospective study is about reassessing past records to understand how exposure to certain factors affected outcomes)

• Assessed at some point in the past for which records are available

• Outcome has already occurred

Case Control Study

compares two groups of people: those with the disease (cases) and those without the disease (controls) you’re trying to figure out what might have caused the disease by comparing the history of those affected with those who aren't. They assess the risk factors in both groups to identify what might have contributed to the disease in the cases

• Cases: People who have the disease.

• Controls: People who do not have the disease.

• Comparison: Examine past exposures or risk factors to see what might differ between the two groups.

• Exposure data will be collected retrospectively by interviews and/or records review

ADVANTAGES

• It is feasible when dealing with rare disease

• It is required a smaller sample size than a cohort study

• There is little problem with attrition ( u wont lose participants over time since we are looking at past data)

DISADVANTAGES

• Incidence Rates and Attributable Risks: Cannot be computed because these studies are retrospective and don’t measure the occurrence of new cases over time.

• Temporal Sequence: It's challenging to determine if the exposure truly preceded the disease since both are looked at retrospectively.

• Selection Bias: There's a significant chance of bias when selecting cases and controls, which can affect the study’s validity.

• Recall Bias: Participants might struggle to accurately remember and report past exposures, especially if the recall period is long.

• Selective Survival: Those who survive to be included in the study may not be representative of all individuals with the disease, skewing the comparison.

Two kinds of Case Control Studies

1. Population-Based Case-Control Study:

   • Sample: Cases and controls are chosen from a defined population.

   • Source Population: is better defined

   • Same Source: Both cases and controls come from the same population.

   • Exposure Reflection: Control exposures more likely represent those without the disease.

2. Hospital-Based Case-Control Study:

   1. Setting: The study is conducted in a specific hospital.

      • Cases: Patients admitted with the disease of interest.

      • Controls: Patients admitted with other conditions but without the disease in question.

   2. Accessibility: Patients are easier to access since they are already in the hospital.

      • Cooperation: Patients tend to be more willing to participate because they're already receiving medical care.

   3. Balanced Background: Cases and controls may have similar socioeconomic and demographic characteristics.

      • Example: Similar age groups, lifestyles, or backgrounds.

   4. Data Collection: Easier to collect information from medical records and biological tests.

      • Efficiency: Medical records provide detailed exposure and health history, making data collection more straightforward.

Cross Sectional Study

A cross-sectional study captures a snapshot of a population at a specific point in time. It examines the prevalence of certain characteristics, such as diseases or behaviors, without tracking changes over time.

ADVANTAGES

• It is less time-consuming and less costly

• It serves as the starting point in prospective cohort studies

• It allows the management of risk, although the estimate is not precise

DISADVANTAGES

• No Direct Risk Estimation: They can't directly measure the risk of developing a disease because they only provide a snapshot of a single moment, not changes over time.

  • Example: You can't determine how likely someone is to develop diabetes in the future.

• Bias from Selective Survival: Those who survive long enough to be included might not be representative of the whole population.

  • Example: If only healthier individuals live to an older age, the data might skew towards less disease.

• Temporal Sequence Issues: It's hard to determine if the exposure happened before the disease or vice versa.

  • Example: Did high blood pressure cause heart disease, or did heart disease lead to high blood pressure?

Experimental studies

They provide evidence for the cause-effect relationship

• they require follow-up. It involves action, manipulation, or intervention by the investigator

Why Pre and Post Treatment Measurements are Needed:

• Measure Changes: Pre-treatment measurements provide a baseline, while post-treatment measurements show the effects of the treatment. This enables researchers to see if any changes occurred because of the treatment.

• Effectiveness Indicator: Changes between pre and post-treatment measurements are often used to gauge the treatment’s effectiveness.

Why a Control Group is Needed:

• Baseline Comparison: The control group doesn’t receive the treatment, so researchers can compare it to the treatment group to see if changes occur due to the treatment or just over time.

• Validity: Helps determine if the observed changes are truly due to the treatment and not other variables.

• Pre-treatment: Measure breastfeeding rates before the program.

• Post-treatment: Measure breastfeeding rates after the program.

• Control Group: Compare with a similar group that didn’t receive the program to see if the increase is due to the program itself.

Experimental studies main characteristics

- Pre and post treatment measurements

- Control group

- Random selection of subject

- Random assignment of subjects

- High degree of control

Quasi Experimental design

In quasi-experimental designs, subjects are not randomly assigned to experimental and control groups. This could be due to practical or ethical reasons.

Variations of Experimental designs

1. Long-Term Effects:

   • Used to study sustained impacts of an intervention over a long period.

2. Multiple Interventions:

   • Evaluates the effects of more than one intervention simultaneously.

Pre-ClinicalStudies

• Conducted before testing drugs in humans:

  • Active Compounds: Isolating and characterizing.

  • Animal Testing: Pharmacology and toxicology.

  • ADME/Tox: Testing absorption, distribution, metabolism, excretion, and toxicity.

  • Dosage: Determining adverse effects levels.

ClinicalTrials

• Used by clinicians and epidemiologists to evaluate:

  • Drugs: Effectiveness and safety.

  • Medical Devices: Functionality and reliability.

  • Procedures: Clinical or healthcare methods.

Randomized,Controlled,Double-BlindClinicalTrial

• Randomized: Participants are randomly assigned to different groups.

• Controlled: There is a control group for comparison.

• Double-Blind: Neither participants nor researchers know who is in which group, reducing bias.

Phases of Clinical Trials

Phase 1

• Participants: Small group of healthy volunteers (20-100).

• Goal: Determine if the drug is safe in humans.

Phase 2

• Participants: Small group of patients (100-500).

• Objective: Identify short-term side effects, risks, and assess if the drug works as expected.

Phase 3

• Participants: Large group of patients (1000-5000).

• Goal: Demonstrate the drug’s safety and efficacy.

Phase 4

• Post-Marketing Surveillance: Monitor long-term safety and reassess effectiveness, acceptability, and continued use in real-world settings.

Qualitative Research

• Systematic Collection and Analysis: Involves gathering and examining narrative materials that describe life experiences, often with minimal researcher control.

• Exploration of Depth and Complexity: Aims to uncover the richness and intricacies inherent in phenomena, guiding social science practice and theory development.

Emphasis:

• Dynamic, Holistic, and Individual Aspects: Focuses on capturing the full context of human experiences, viewing them through the eyes of those experiencing them.

• Subjective Narratives: Relies on personal stories and perspectives to give meaning to experiences.

Goal:

• Understanding Experiences: Seeks to understand life experiences in their entirety within the context of the individuals involved, offering insights that can inform broader social knowledge and practices.

Key Characteristics of Qualitative Research

1. Multiple Realities:

   • Belief that there's no single reality or truth. Each person's experience creates a unique reality.

2. Discovery of “Truth”:

   • Uses various methods to understand the truth. The question guides the choice of method.

   • Includes data collection and triangulation.

3. Participant’s Point of View:

   • Prioritizes the perspectives of study participants over the researcher’s.

   • Researchers are co-participants in understanding reality.

4. Natural Context:

   • Research is conducted without disturbing the natural setting of the phenomenon being studied.

5. Researcher as Instrument:

   • The researcher plays multiple roles: observer, interviewer, and interpreter.

   • Acknowledges and embraces bias, commits to being reflexive, and describes experiences faithfully.

6. Rich Reporting Style:

   • Findings are reported with quotes, commentaries, and stories to enhance understanding.

Quali VS Quanti

Purpose:

• Quantitative: Confirms hypotheses about phenomena by quantifying variation and predicting causal relationships.

• Qualitative: Explores phenomena, describing variations, relationships, individual experiences, and group norms.

Analytical Objectives:

• Quantitative: To quantify variation, predict relationships, and describe population characteristics.

• Qualitative: To describe variation, explain relationships, individual experiences, and group norms.

Question Format:

• Quantitative: Closed-ended questions.

• Qualitative: Open-ended questions.

Data Format:

• Quantitative: Numerical data (e.g., scores, measurements).

• Qualitative: Textual data (e.g., interview transcripts, field notes).

Sampling Method:

• Quantitative: Probability sampling (e.g., random sampling).

• Qualitative: Non-probability sampling (e.g., purposive, quota, snowball sampling).

Saturation:

• Quantitative: Achieved when results can be generalized with known reliability and margins of error.

• Qualitative: Achieved when no new information is obtained from additional data.

External Validity:

• Quantitative: High generalizability to larger populations.

• Qualitative: Limited generalizability; focuses on transferability to other contexts.

Data Analysis:

• Quantitative: Classified into predetermined categories (deductive), succinct, and statistically analyzed.

• Qualitative: Classified into categories identified in the data (inductive), extensive, descriptive, and interpretation is more subjective.

Common Features in Conduct of Qualitative Research

1. Philosophical Position:

• Purpose: Description and understanding, not prediction.

• Belief: Multiple realities exist and shape human inquiry.

2. Use of Literature Review:

• Timing: Done after data collection and analysis to avoid bias.

• Purpose: Show how findings fit with existing knowledge, not to confirm it.

3. Clarifying the Researcher's Beliefs:

• Reflexivity: Critical self-reflection on how personal views may affect the research.

• Ethical Considerations: Maintaining authentic relationships with participants and reflecting on power dynamics.

4. Setting for Data Collection:

• Fieldwork: Conducted in natural settings where participants live and experience life.

• Ethics: Obtaining consent and ensuring participants are aware of how data will be used.

5. Selection of Participants:

• Non-Random: Participants chosen based on their experience with the phenomenon.

• Purposeful Sampling: Aimed at getting a deep understanding, not generalizing findings.

6. Saturation:

• Definition: Point at which no new information is found.

• Goal: Confirm and repeat findings to ensure they are robust.

7. Analysis of Data:

• Hands-On Process: Researchers deeply immerse themselves in the data.

• Coding and Categorization: Inductive process to identify themes and subthemes.

• Data Reduction: Simplifying extensive data to make sense of it and develop meaningful insights.

8. Demonstrating trustworthiness and Rigor

   • Challenge: Qualitative research is often criticized for lack of rigor using quantitative standards.

   • Solution: Develop and adhere to guidelines suitable for qualitative research.

     • This could be established through: prolonged engagement with the subject

       matter and return to the participants and see whether they recognize the

       finding to be true to their experiences

Goals of Coding

1. Reflect on Meaning: Delve deeply into the significance of each piece of qualitative data.

2. Construct Patterns: Identify and build patterns within the data.

3. Develop Categories and Themes: Create categories, concepts, themes, assertions, theories, and other analytic findings.

Categories and Categorizing

• Definition: Categories are groups of codes that seem similar.

• Purpose: They encapsulate and label patterns found in the data.

Mechanics of Coding

• Process: Coding and recoding data is iterative. Expect to refine codes and categories over time.

• Metaphor: Think of it like decorating a room—constant adjustment and refinement.

Synthesizing Qualitative Data

Five Fundamental Analytic Skills:

1. Condensing Data: Summarizing large amounts of information.

2. Noticing Patterns: Finding recurring themes in textual and visual materials.

3. Unifying Different Things: Seeing how different pieces of data connect.

4. Understanding Social Processes: Grasping how people act, react, and interact.

5. Interpreting Social Life: Making sense of routines, rituals, rules, roles, and relationships.

Analytic memos

• Relationship with Coding: Coding prompts deeper reflection, leading to memos.

• Purpose: Memos are like conversations with yourself about the data. They document and refine your analytic thoughts.

Trustworthiness

Definition:

• The extent to which research findings are believable and reliable.

Systematic Collection and Analysis:

• Qualitative research, like quantitative, requires systematic data collection using accepted procedures and open to critical analysis.

Guba’s Model

Used in qualitative research across various fields, such as social sciences, healthcare, and education. It helps researchers demonstrate the rigor and trustworthiness of their studies,

1. Credibility: Confidence in the truth of the findings.

2. Dependability: The consistency of the findings over time.

3. Confirmability: The degree to which the findings can be confirmed by others.

4. Transferability: The extent to which findings apply to other contexts.

Authentic understanding

Readers are able to live their way into an experience that has been described and interpreted

Trustworthiness

Key Practices to Ensure Trustworthiness:

• Theoretical Saturation: Continue data collection and analysis until no new information emerges, confirming the depth of the findings.

• Triangulation: Use multiple data collection methods, contexts, and investigators to ensure comprehensive understanding.

• Peer Debriefing: Share data and analyses with colleagues for feedback, ensuring transparency and openness to scrutiny.

• External Verification: Have an outside person verify the research process and logic, ensuring the systematic approach.

• Member Checks: Return to informants to verify that the findings accurately reflect their experiences.

Types of study designs in Qualitative research

1. Phenomenology:

   • Focuses on understanding the lived experiences of individuals.

2. Ethnography:

   • Studies cultures and communities through immersion and observation.

3. Case Study:

   • In-depth analysis of a single case or a small number of cases.

4. Grounded Theory:

   • Develops theories grounded in data collected from participants.

Modes of data collection in Qualitative research

1. articipant Observation:

   • Researcher immerses themselves in the setting to observe and participate.

2. Key Informant Interviews:

   • In-depth interviews with individuals who have expert knowledge of the subject.

3. Focus Group Discussion:

   • Group discussions to gather diverse perspectives on a topic.

4. Analysis of Secondary Data:

   • Examining existing data like documents, records, and previous studies.

Modes of data analysis in Qualitative research

1. Content Analysis:

   • Systematic coding and interpretation of textual data.

2. Constant Comparison Technique:

   • Comparing data continuously to identify patterns and categories.

3. Thematic Network Analysis:

   • Organizing themes into networks to explore connections.

4. Word Counting:

   • Counting the frequency of specific words or phrases to identify trends.

Phenomenology Qualitative Research

Phenomenology is all about understanding the essence and meaning of lived experiences from the perspectives of those involved.

Goals

• Essence and Meaning: Discover the core essence of a phenomenon as experienced by people.

• Description over Explanation: Focus on describing experiences rather than explaining or quantifying them.

• Lived Experiences: Explore spatiality (space), corporeality (body), temporality (time), and relationality (human relations).

Approaches

• Descriptive Phenomenology:

  • Aim: Describe human experiences.

  • Methods: Bracketing (setting aside biases), intuiting (open to meanings), analyzing (categorizing statements), and describing the phenomenon.

• Interpretive Phenomenology:

  • Aim: Interpret and understand human experiences.

  • Hermeneutics: The philosophy of interpreting meanings.

  • Methods: In-depth interviews, supplementary texts (novels, poetry), and participant conversations.

Key Steps in Descriptive Phenomenology

1. Bracketing: Identifying and setting aside preconceived beliefs.

2. Intuiting: Remaining open to participants' meanings.

3. Analyzing: Categorizing and making sense of significant statements.

4. Describing: Defining the phenomenon based on participants' experiences.

Interpretive Phenomenology

• Focuses on understanding and interpreting experiences, not just describing them.

• In-depth interviews with those who have experienced the phenomenon.

• Includes analysis of supplementary texts to deepen understanding.

Essential Questions in Phenomenology

1. What have you experienced in terms of the phenomenon?

2. What contexts or situations have typically influenced or affected your experience of the phenomenon?

Ethnography Qualitative Research

Definition:

• Ethnography is the systematic study of the culture and story of a group of people.

• It explores cultural phenomena from the point of view of the subjects.

Types:

• Macroethnography: Broadly defined cultures.

• Microethnography: Detailed studies of small units within a group or culture.

Purpose:

• Develop cultural awareness and sensitivity.

• Gain an Emic (insider) perspective to uncover deeply embedded knowledge.

• Understand social and psychological phenomena from the participants' perspectives.

Role in Research:

• Examines how cultural systems (beliefs, behaviors, institutions) influence and are influenced by the phenomena being studied.

• Provides a holistic approach, examining the complex interplay between humans, culture, and environment.

Disciplinary Origin: Anthropology.

Case study/ Case series Qualitative research

• An in-depth account of interesting characteristics observed in a group (series) of subjects to address a research problem.

• Subjects can be people, organizations, institutions, or political units.

Purpose:

• Selected for their unique characteristics, not because they are typical of the target population.

• Findings often generate hypotheses for further, more complex quantitative studies.

Disciplinary Origin: Multidisciplinary (including business, law, social sciences, medicine, and education).

Grounded Theory Qualitative Research

• A qualitative strategy where a general, abstract theory of a process, action, or interaction is derived from the views of participants.

Objectives:

• Develop New Theory: Create a new theory about a phenomenon based on data collected from study subjects.

KeyFeatures:

• Interrelated and Iterative: Data collection and analysis happen simultaneously, influencing each other continuously.

• Constant Comparison: Categories from data are constantly compared with earlier data to find patterns and variations.

Disciplinary Origin: Sociology.

Approach:

• Theory Generation: Explains behaviors that are problematic and relevant to participants.

• Data Comparison: Shared patterns and variations are identified and refined through continuous comparison.

Process:

• In-depth Interviews, Observation, and Existing Documents: Typical methods used, involving 25-50 informants.

• Focus Shifting: As data collection proceeds, the study focuses more on emerging theoretical concerns.

Types of Grounded Theory:

1. Substantive Theory: Based on data from a specific area (e.g., post-disaster depression) and can lead to broader theories.

2. Formal Theory: Higher-level, abstract theory developed from multiple substantive studies.

Example Metaphor: Think of it as creating custom-tailored clothing rather than ready-to-wear—each theory is uniquely crafted from the data.

Components of Grounded Theory Practice(GlaserandStrauss):

1. Simultaneous Data Collection and Analysis.

2. Analytic Codes from Data: Not from preconceived hypotheses.

3. Constant Comparative Method: Comparing data at each stage.

4. Theory Development: Ongoing at each data collection step.

Qualitative study designs samples

Phenomenology

• Study: "How do first responders during disasters experience and perceive their work?"

  • Goal: Understand the lived experiences and perceptions of first responders, capturing the essence of their experiences.

Ethnography

• Study: "What is the socio-cultural context of post-traumatic stress disorder (PTSD) among indigenous populations in the Cordilleras?"

  • Goal: Explore the cultural context and how it influences the experience of PTSD among indigenous people, using an insider perspective.

Case Study

• Study: "What are the peculiar characteristics of Albay province which have contributed to its zero-casualty record during disasters in the past years?"

  • Goal: Provide an in-depth analysis of Albay's unique characteristics that have led to its successful disaster management.

• Additional Case Study: "To describe PTSD in children who lost their immediate family members due to the Marawi siege and its effects on child development, relationships, and self-perception."

  • Goal: Understand the impact of the Marawi siege on children's mental health and development through detailed case descriptions.

Grounded Theory

• Study: "What is the role and significance of social capital in building community resilience during disasters? How does social capital become a factor in community resilience?"

  • Goal: Develop a theory on how social capital influences community resilience based on data from affected communities.

• Additional Grounded Theory: "To develop a greater understanding of the roles of resilience, coping, and identity among families with several members who developed PTSD after the Marawi siege."

  • Goal: Generate a grounded theory about the interaction of resilience, coping mechanisms, and identity in families affected by PTSD.

Commonly used data collection methods in Qualitative Research

1. In-Depth Interviews / Key Informant Interviews

• Purpose: To capture detailed personal perspectives, experiences, opinions, and feelings on a specific research topic, especially for sensitive issues.

• Selection: Participants are purposively chosen based on their knowledge, position, or specific characteristics relevant to the study.

• Format: Typically one-on-one, face-to-face interviews, but can also be conducted via phone or with small groups. Lasts about 1-2 hours.

2. Focus Group Discussion

• Purpose: To gather diverse perspectives and insights on a research topic through group interaction.

• Format: Consists of 8-12 similar individuals participating in a moderated discussion.

• Selection: Participants are chosen for their specialized knowledge or insights into the issue.

• Benefits: Effective for understanding social norms and capturing a variety of opinions. The group dynamic enriches the data as participants influence each other’s responses.

Phenomenology explained

Definition:

• Phenomenology is both a philosophy and a research method focused on describing experiences or phenomena as they are lived by individuals.

KeyPoints:

• Philosophical Roots: Studies structures of experience, consciousness, and the meanings of phenomena as they appear in our experience.

• Subjective Perspective: Studies conscious experiences from the first-person point of view.

• Fields of Philosophy: Related to ontology (study of being), epistemology (study of knowledge), logic (study of valid reasoning), and ethics (study of right and wrong).

Focus:

• Individual Interpretation: Focuses on individuals' interpretations of their experiences and the ways they express them.

• Researcher's Role: Describes phenomena as experienced and expressed by participants.

CharacteristicFeature:

• Bracketing: Suspending the researcher’s preconceptions, beliefs, and biases to avoid influencing the description of the respondent’s experience.

VariationsofPhenomenology:

1. Descriptive Phenomenology (Edmund Husserl):

   • Focus: On the experience itself.

   • Bracketing: Main feature.

2. Interpretive Phenomenology (Martin Heidegger):

   • Focus: On the meaning of the experience.

   • Rejection of Bracketing: Believes it is not possible to separate description from interpretation.

ProcessofPhenomenology:

1. Identification of the Phenomenon:

   • Define the purpose and philosophical underpinnings.

2. Identification of the Setting and Participants:

   • Select participants and obtain consent. Sample size based on saturation.

3. Data Gathering:

   • Use interviews as the primary tool to gain insights into respondents’ experiences.

4. Analysis and Discussion:

   • Identify themes, patterns, and trends. Describe findings using textual descriptions.

ApproachestoDataAnalysis:

1. Amadeo Giorgi: Importance judged by researcher’s intuition, not frequency.

2. Adrian Van Kaam: Classifies and ranks data.

3. Paul Collaizzi: Observes and analyzes human behavior.

4. Max Van Manen: Uses personal experience and literature.

5. Rosemarie Parse: Unstructured discussions about lived experiences.

TheEssence:

• Generate narratives that present the fundamental nature of the experience, strengthened by contextual information, quotes, tables, and figures.

WritingPhenomenology:

• Practice of Writing: Qualitative inquiry involves writing to represent phenomena.

• Language: Describes and represents what is absent.

Rigor:

• The goal is to help readers understand what it is like for someone to experience the phenomenon.

EssentialQuestionsAskedinPhenomenology:

1. What have you experienced in terms of the phenomenon?

2. What contexts or situations have typically influenced or affected your experience of the phenomenon?

Summary:

Phenomenology seeks to discover the essence and meaning of experiences as lived by individuals. It provides powerful insights into subjective experiences and is well-suited for studying intense human emotions and experiences.

Ethnography explained

Definition:

• Ethnography: Describing and understanding another way of life from the native point of view.

• It documents the daily lives, cultural practices, beliefs, and interactions of a group of people.

CoreElements:

• Fieldwork: Researchers spend considerable time with the people they are studying, often living among them.

• Guiding Question: A flexible research question that evolves as the study progresses.

• Cultural Description: Detailed representation of a culture or a selected aspect of it, including lifestyle, beliefs, and behaviors.

Disciplinary Origin: Anthropology.

Purpose:

• Develop cultural awareness and sensitivity.

• Capture the insider (emic) perspective and understand deeply embedded knowledge.

• Overcome ethnocentrism to see the world from another's point of view.

CharacteristicsofEthnography:

• Holistic: Deals with all social, cultural, and psychological aspects of the community.

• Immersive: Requires spending significant time in the field.

• Descriptive and Analytical: Provides a crafted, detailed depiction of the culture.

Uses of Ethnography:

• Cultural Anthropology: Understanding human cultures.

• Sociology: Exploring social interactions and structures.

• Business: Studying organizational culture.

• Organizational Psychology: Examining workplace behaviors.

Aim:

• Unobtrusive: Identifies how a group manages time and organizes activities.

• Insider View: Understands the group’s perspective from the inside.

• Behavior Patterns: Identifies and anticipates behavior patterns.

• Social Processes: Understands the context, complexity, and politics of social processes.

Key Concepts:

• Emic: Inside perspective.

• Etic: Outside perspective.

• Key Informant: A knowledgeable individual within the group who provides insight.

Methods:

• Participant Observation: Engaging and observing participants in their natural setting.

• Interviews: Conversations with key informants and group members.

• Document Analysis: Reviewing existing documents related to the culture.

History:

• Early ethnographers like Lewis Morgan and Bronislaw Malinowski set the foundation for modern ethnographic methods, emphasizing accurate observation, immersion, and use of native languages.

Modern Ethnography:

• Holistic and Evolutionary: Adapted to consider external structures and power dynamics.

• Post-Modern Influence: Focuses on reflexivity and the influence of power structures.

Attributes of Thick Description(CliffordGeertz):

• Hermeneutics: Interpretation of cultural meanings.

• Semiotics: Study of symbols and meanings.

• Cultural Context: Culture as a context for understanding social events, behaviors, and institutions.

Ethnography Methodology

Outline of Process:

1. Identifying Problem or Topic: Start with a clear research problem or topic of interest.

2. Fieldwork – Data Collection and Analysis: Engage in direct observation and interaction.

   • Participant Observation: Observe individuals and groups.

   • Analysis: Holistic approach to interpreting data.

StagesofFieldwork:

1. Negotiating Entry:

   • Gatekeeper: Gain access through someone trusted within the community.

   • Key Actors and Informants: Identify individuals who provide valuable insights.

2. Introductory Period:

   • Learn routines, roles, and relationships within the group.

3. Participatory Observation:

   • Engage actively to gain deeper insights.

FieldworkMethods:

• Selection and Sampling

• Participant Observation

• Interviewing and Autobiographical Interviewing

• Questionnaires and Projective Techniques

• Participant’s Classification

• Outcropping and Document Analysis

• Proxemics and Kinesics

• Folktales

• Detailed Note-taking

Analysis:

• Evaluating Relevance and Looking for Patterns

• Considering Cultural Perspectives

• “Thick Description”: Detailed and nuanced descriptions.

• Use of Classifications, Parameters, and Etic Observations

• Visual Aids: Maps, drawings, charts.

Writing:

• Ensure clarity from field notes to final report.

• Write for your audience and the objective, bridging insider and outsider perspectives.

Examples:

• A Mission Found: Describes the author’s journey from an engineering career to social work in India, driven by a mission to address poverty and ignorance.

AdvantagesandDisadvantages:

Advantages:

• In-depth cultural understanding.

• Gives a voice to underrepresented groups.

• Influences outsiders’ understanding.

• Reveals hidden cultural values.

Disadvantages:

• Time-consuming and expensive.

• Risk of altering natural behaviors.

• Limited generalizability.

• Challenges in addressing complex, external environmental issues.

• Balancing engagement with critical reflection.

Ethnography Overview:

• Purpose: Explore cultural phenomena, focusing on developing cultural awareness and sensitivity.

• Perspective: Strive for an Emic (insider) perspective while acknowledging Etic (outsider) views.

• Disciplinary Origin: Anthropology.

Role in Research:

• Examines how cultural systems influence and are influenced by phenomena.

• Provides a holistic approach to human, cultural, and environmental interactions.

• Offers critical analysis of social, cultural, and psychological aspects.

Narrative Inquiry explained

Definition:

• Narrative inquiry involves describing the lives of individuals, collecting and telling their stories, and writing narratives about their experiences.

• It aims to gather stories as data, focusing on past events to help individuals make sense of their experiences and identity.

ImportanceinHealthResearch:

• Professional Knowledge: Enhances understanding and care practices.

• Deep Understanding: Provides insights into patients' experiences.

• Healing Functions: Explores caring in nursing, generates hypotheses, and sets patient-centered agendas.

DimensionsofNarrative:

1. Everyday Stories: Descriptions of daily activities and tasks.

2. Autobiographical Stories: Personal accounts linking past, present, and future.

3. Biographies: Stories about other people's life trajectories.

4. Cultural Stories: Demonstrate meanings in cultural contexts.

5. Collective Stories: Retell multiple individual stories.

6. Illness Narratives: Patients share their experiences of illness.

FormsofNarrative:

1. Restitution Narrative: Focuses on recovery and the desire to get well.

2. Chaos Narrative: Describes ongoing suffering with no clear resolution.

3. Quest Narrative: Emphasizes the journey of learning and personal growth from experiences.

StructureofNarratives:

1. Abstract: Introduces and summarizes the story.

2. Orientation: Provides details of characters, time, place, and events.

3. Complication: Describes critical events.

4. Evaluation: Explores the meaning of actions and events.

5. Result: States the outcome of the story.

6. Coda: Links the past story to the present.

Three-DimensionalSpaceNarrativeStructure:

1. Interaction: Personal experiences and interactions with others.

2. Continuity: Past, present, and future actions.

3. Situation: Locations and context of the story.

EvaluatingNarrativeResearch:

• Focus on one or two individuals.

• Report life experiences in a narrative chronology.

• Include detailed context and thematic analysis.

• Collaborate with participants to verify accuracy.

StepsinNarrativeInquiry:

1. Identify Problem or Phenomenon: Provides purpose and understanding.

2. Select Participants: Choose individuals who can provide insights into the issue.

3. Collect the Story: Gather information through interviews and field texts (diaries, letters, photos).

4. Restory or Retell: Organize and sequence the data into a coherent story.

5. Collaborate: Ensure accurate portrayal of participants' experiences.

6. Write the Story: Highlight themes and the importance of narrative research.

7. Validate Accuracy: Verify through participant feedback and disconfirming evidence.

Ethics in Narrative Research:

1. Relational Responsibility: Researchers must build and maintain respectful and genuine relationships with participants.

2. Sensitivity and Respect: Being fully present and considerate during interactions with participants.

3. Emotional Awareness: Recognizing that the interview process can lead to personal and emotional changes.

4. Comfort with Intimacy: Being prepared to engage deeply with participants' personal stories.

5. Standard Ethical Procedures: Ensuring fully informed consent, confidentiality, and participant protection.

6. Integrity of the Study: Protecting participants by camouflaging identifiable contextual details.

PROBLEMATIC ISSUES IN NARRATIVES

“TRUTH” is the truth being told or the truth as the participants see it?

Veracity and falsehood of stories as they are retrospective and rely on

memory

Hidden motives the way the narrator tells the story

Inconsistencies and tensions

Grounded Theory Explained

Grounded Theory Explained

Core Concepts:

• Inductive Approach: Generates theory from data.

• Constant Comparison: Continually compares data to refine categories.

• Theoretical Sampling: Follows leads in data to refine and saturate categories.

Process:

1. Data Collection and Analysis:

   • Cyclical and simultaneous, using inductive and deductive reasoning.

   • Generates a theory grounded in collected data.

2. Memoing:

   • Analytic memos are written to document insights, decisions, and reflections.

Key Features:

• Theory Development: Aimed at discovering social and psychological processes.

• Codes and Categories: Created directly from data.

• Iterative Analysis: Refining and exhausting conceptual categories.

History:

• Origin: Developed by Barney Glaser and Anselm Strauss in 1967.

• Evolution: Kathy Charmaz introduced constructivist grounded theory, emphasizing that data and theories are constructed through the researcher’s experiences.

Sampling:

• Purposive Sampling: Initial data collection based on relevance.

• Theoretical Sampling: Continual sampling to refine and saturate categories.

Memoing:

• Importance: Essential for ensuring quality and building a historic audit trail.

• Function: Provides detailed records of thoughts, decisions, and analytical processes.

Example:

• Substantive Theory: A theoretical interpretation or explanation of a specific phenomenon, expressed through interrelated concepts.

Generating/Collecting Data in Grounded Theory (GT)

Concurrent Data Collection and Analysis:

• GT involves collecting and analyzing data simultaneously, refining the research process continuously.

Data Sources:

• Qualitative and Quantitative: All types of data are considered valuable.

• Elicited Data: Produced by participants (e.g., interviews, focus groups).

• Extant Data: Already available (e.g., documents, reports, literature).

Importance of Relationship:

• The researcher's interaction with the data is crucial for developing a meaningful theory.

Coding Stages:

1. Initial Coding:

   • Purpose: Begin fracturing data to compare incidents, identifying similarities and differences.

   • Process: Generate many codes, label important words or groups, and identify actions or processes.

   • Key Questions: What is the data about? What does it suggest? Whose perspective does it represent?

2. Intermediate Coding:

   • Purpose: Transform basic data into abstract concepts, refine categories, and identify core categories.

   • Process: Review and subsume categories, refine properties (common characteristics) and dimensions (variations).

   • Tools: Diagramming helps in this phase.

3. Advanced Coding:

   • Purpose: Integrate and finalize the grounded theory.

   • Techniques: Storyline technique and theoretical coding.

   • Outcome: Present findings as interrelated concepts, providing explanatory statements and conceptual integration.

Theoretical Sampling:

• Purpose: Follow leads in data, refine categories, and ensure the final theory is grounded in data.

Memoing:

• Role: Essential for quality assurance, documenting thoughts, decisions, and analysis processes.

Key Concepts:

1. Open Coding: Initial categorization of data.

2. Axial Coding: Grouping codes into categories.

3. Selective Coding: Identifying the core category.

4. Theoretical Sampling: Ongoing data collection to saturate categories.

Summary:

Grounded Theory involves an iterative and dynamic approach to data collection and analysis, where the theory emerges directly from the data. Coding stages and memoing play critical roles in refining and integrating the theory, ensuring it is robust and explanatory.

Theoretical Sensitivity

Definition:

• The ability to recognize and understand significant elements within data that are important for developing a grounded theory. It encompasses insight into what is meaningful and relevant.

KeyPoints:

• Insight: Found within the researcher, it allows the identification of critical data segments.

• Continuous Process: Takes place throughout data collection and analysis.

• Enhancement: Analytic tools and techniques help increase sensitivity to theoretical constructs.

Importance:

• Directs analytical focus towards theory development.

• Ensures a grounded theory is integrated and abstract.

LimitationsofGroundedTheory:

1. Difficulty Recruiting:

   • Iterative Process: Continuously recruiting new participants as the study evolves can be challenging.

   • Evolving Criteria: Recruitment criteria may change based on new insights.

2. Time-Consuming Data Collection:

   • Flexible Timeline: Continuous data collection until theoretical saturation is reached, often requiring multiple rounds.

3. Challenges in Analysis:

   • Constant Comparisons: Keeping track of comparisons and findings can be complex.

   • Organizational Tools: Using software like NVivo can help manage data and analysis.

QualityandRigour:

1. Researcher's Expertise: The researcher's skills and knowledge are crucial.

2. Methodological Congruence: Aligning the research question with the methodological approach.

3. Procedural Precision: Maintaining a detailed audit trail, using memos, and managing data rigorously.

WhyUseGroundedTheory?

1. Lack of Existing Theory: Ideal for areas with little or no theoretical framework.

2. Disagreement with Existing Theories: Allows for the development of new theories.

3. Ownership of Theory: Enables long-term development and refinement of theory.

4. Hypothesis Generation: Focuses on generating rather than testing hypotheses.

5. Mixed Data Collection: Incorporates both qualitative and quantitative data, allowing for a broad range of information.

Case Study Explained

Definition:

• An empirical inquiry that investigates a phenomenon in its real-life context.

• It aims to describe and explain the phenomenon through a detailed investigation.

KeyFeatures:

• Multiple Variables: Deals with complex situations with many variables.

• Multiple Sources of Evidence: Uses data from various sources, converging in a triangulating manner.

• Theoretical Propositions: Benefits from prior theoretical propositions to guide data collection and analysis.

WhentoUseCaseStudyMethod:

• To answer "why" and "how" questions.

• When you can't manipulate the behavior of people in the study.

• To cover contextual information important for understanding the phenomenon.

• When boundaries between context and phenomenon are not clear.

• Interested in examining interrelationships within a real-life context.

LogicofCaseStudy:

• Unit of Study: Can be a person, family, community, program/policy, or any phenomenon.

• Examples:

  • Health interventions (e.g., polio immunization, dengue prevention).

  • Health policies/programs (e.g., devolution, HIV/AIDS program).

  • Project evaluation (e.g., effects of IEC program on health-seeking behavior).

TypesofCaseStudies:

• Single vs. Multiple Cases: Can involve one or several cases.

• Comparative Case Method: Distinctive form of multiple case study.

Goals:

• Exploratory: To explore new areas.

• Descriptive: To provide a detailed description.

• Explanatory: To explain phenomena.

ResearchDesign:

• Components:

  • Research objectives, questions, propositions.

  • Unit(s) of analysis.

  • Logic linking data to propositions.

  • Criteria for interpreting findings.

RoleofTheory:

• Theoretical Framework: Guides the study (e.g., organizational theories).

ParadigmaticUnderpinnings:

• Constructivism (Psychology): Social phenomena interpreted by individuals.

• Constructionism (Sociology): The world is socially constructed through interaction and language.

MethodologicalApproaches:

• Can involve phenomenology, ethnography, or grounded theory.

Critique:

• Limitations:

  • Little basis for scientific generalization.

  • Findings not generalizable.

  • Often lengthy and complex.

  • Cannot directly address causal relationships.

  • Not easily replicable.

  • Potential reliability issues.

Strengths:

• Multiple Techniques: Uses various data collection methods.

• Holistic: Captures multiple dimensions and interconnections.

• Depth: Provides detailed insights.

SampleResearchQuestions:

• "Why do women not use the prenatal services in local health service centers?"

• "How effective was the malaria prevention program?"

• "How significant was the participation of the community in health planning in the devolved setting?"

Steps in Formulating Case Study Design

1. Determine Your Research Questions:

• Clearly define your focus.

• Conduct a literature review.

2. Select the Case:

• Choose between single or multiple cases as your unit of analysis.

• Tips for Delineating Your Case:

  • Do you want to analyze the individual, program, process, or differences between programs?

Example:

• Question: How do women in their 30s who have had breast cancer decide whether or not to have a radical mastectomy?

  • Case: Decision-making process or experiences of breast cancer.

3. Developing the Case Study Research Questions:

• Case Example 1:

  • Research Question: How do women aged 30-40 decide whether or not to have a radical mastectomy? What factors influence their decision?

• Case Example 2:

  • Research Question: How do women experience the need to decide on whether or not to have a radical mastectomy?

4. Determining What Your Case Will Not Be:

• Boundaries:

  • Time & place.

  • Time & activity.

  • Definition & context (e.g., defining breast cancer and radical mastectomy).

  • Time period they are making the decision.

  • Health centers they are attending.

• Considerations: Your theoretical framework/propositions, research objectives, questions, time, and budget.

5. Type of Cases:

• Single vs. Multiple:

  • Describe a single case or compare multiple cases.

• Goals:

  • Intrinsic: Deep understanding of a specific case.

  • Instrumental: Understanding an issue or problem through multiple cases.

6. Determine Data Collection & Analysis Techniques:

• Develop a detailed plan including social preparation and ethics.

• Techniques must be appropriate to your case.

7. Components of Case Study Design:

• Research Questions: Clearly defined.

• Propositions: Theoretical framework or orientation.

• Unit of Analysis: What/who you are studying.

• Data Collection: How data will be gathered.

• Data Analysis: How data will be linked to propositions and interpreted.

• Criteria for Findings: Standards for evaluating the results.

Example:

• Research Question: How significant was the participation of the community in health planning in the devolved setting?

  • Case: The community's involvement in health planning.

Summary:

Case study design involves clear research questions, selecting and delineating your case, developing research questions, setting boundaries, choosing the type of case, planning data collection and analysis, and ensuring ethical considerations.

Case Study Method

Prepare:

• Skills Needed: Ask good questions, be a good listener, be adaptive and flexible, have a firm grasp of the issues, and remain unbiased.

ProtocolofInvestigation:

• Overview of the Project

• Field Procedures

• Case Study Questions

• Guide for Case Study Report

Collect:

• Sources of Evidence:

  • Documentation

  • Archival Records

  • Interviews

  • Direct Observation

  • Participant Observation

  • Physical Artifacts

Principle 1: Use Multiple Sources of Evidence:

• Triangulation: Enhances accuracy and credibility by converging multiple sources of data.

Principle 2: Create a Case Study Database:

• Organization: Document all data to increase reliability.

• Includes: Notes, documents, tabular materials, narratives.

Principle 3: Maintain a Chain of Evidence:

• Transparency: Ensures that an external observer can follow the derivation of evidence from research questions to conclusions.

Analyze:

• General Strategies:

  • Rely on theoretical propositions.

  • Develop a case description.

  • Use both qualitative and quantitative data.

  • Examine rival explanations.

Pattern Matching:

• Compare empirical patterns with predicted ones to strengthen internal validity.

Explanation Building:

• Build an explanation about the case, often in a narrative form.

Time Series Analysis:

• Trace changes over time, using statistical tests and complex analyses.

Logic Models:

• Stipulate a complex chain of events with repeated cause-effect patterns.

Share:

• Reporting Issues:

  • Various structures for case study reports (linear-analytic, comparative, chronological, theory-building, suspense, unsequenced).

CaseStudy/CaseSeries:

• Definition: An in-depth account of interesting characteristics observed in a group of subjects.

• Subjects: Can be organizations, institutions, political units, etc.

• Purpose: Generate hypotheses for further study.

• Disciplinary Origin: Multidisciplinary, including business, law, social sciences, medicine, and education.

Summary:

Case studies investigate phenomena in real-life contexts, using multiple sources of evidence to enhance accuracy. They are guided by theoretical propositions and analyzed through various strategies to provide in-depth insights.

Participatory Action Research

Definition:

• Participatory Action Research is not just research hoped to be followed by action. It's action that is researched, changed, and re-researched within the process by participants.

• Goal: It aims to be active co-research by and for those to be helped, creating a genuinely democratic and non-coercive process.

KeyElements:

1. People:

   • Inclusive of all: female, male, adult, children, farmers, fisherfolk, urban poor.

   • Critical inquiry responds to the experiences and needs of oppressed people.

2. Power:

   • Power is central in constructing reality, language, meanings, and rituals of truth.

   • Promotes empowerment through developing common knowledge and crucial awareness suppressed by dominant systems.

3. Praxis:

   • Recognizes the inseparability of theory and practice.

   • Grounded in principles of social justice and challenges the separation between researchers and the "researched".

ResearchProcess:

1. Select Location: Gain approval from local officials and leaders.

2. Preliminary Visit: Initiate dialogue and share purpose/objectives.

3. Data Collection: Gather secondary and field data (spatial, time-related, social, environmental, economic, governance).

4. Synthesize & Analyze Data: Identify problems and opportunities.

5. Rank & Plan: Prepare action plans, reports, and costings.

6. Implement Plan: Adopt and implement actions.

7. Follow-Up: Evaluate, disseminate findings, and maintain momentum.

CycleofInquiry:

• Reflection on Action: Stops current action to raise questions.

• Planning: Identifying questioners, the questioned, and desired answers.

• Fieldwork: Engage in fieldwork to improve understanding.

• Generating Ideas: Develop ideas for new actions.

• Implementation: Implement new actions, followed by further reflection.

DifferencesfromConventionalResearch:

• Cyclical Process: Participatory action research proceeds through cycles, starting with reflection and moving to new actions.

• Conscious Problematization: More deliberate about naming problems and focusing inquiry efforts.

• Inclusive Inquiry: Involves others who should be involved.

• Documentation: More rigorous documentation and recording of actions.

Application:

• Community-Based: Works with the community to articulate and investigate problems collectively.

• Learning from Experience: Organizes conditions for groups to learn from their own experiences and make these accessible to others.

Participatory Action Research empowers communities to take charge of their inquiry process, ensuring that research is directly relevant and beneficial to those involved.

Mixed method research

Definition:

• Mixed-method research combines both qualitative and quantitative approaches in a single study, providing a comprehensive understanding of research problems.

KeyComponents:

• Core Component: The primary method used to address the research question.

• Supplementary Component: A secondary method used to enhance the core method.

Presentation:

• Uppercase Letters: Core component (e.g., QUAL for qualitative core).

• Lowercase Letters: Supplementary component (e.g., quan for quantitative supplementary).

• Plus Sign (+): Simultaneous execution of core and supplementary components.

• Arrow (→): Sequential execution of core and supplementary components.

TheoreticalDrive:

• Direction: Guides the use of the appropriate methodological core (inductive or deductive).

Examples:

1. QUAL + quan: Qualitative core with quantitative supplementary.

2. QUAN → qual: Quantitative core with qualitative supplementary.

Purposes:

1. Illustrate different aspects of a phenomenon.

2. Illuminate a problem from different angles.

3. Enrich the knowledge base with input from both perspectives.

4. Translate research findings into practical settings.

ReasonsforUsingMixed-MethodResearch:

• Insufficient data from one resource.

• Need to explain initial results further.

• Enhance a primary method with a second method.

• Projects with multiple phases.

ChoosinganAppropriateMixedMethodsDesign:

• Level of Interaction: Independent or interactive strands.

• Relative Priority: Equal or unequal priority.

• Timing: Concurrent, sequential, or a combination.

• Point of Interface: Where the strands are mixed.

MajorDesigns:

1. Convergent Parallel Design:

   • Data Collection: Survey and focus groups or one-on-one interviews simultaneously.

   • Data Analysis:

     • Side-by-side comparison.

     • Data transformation (qual to quan or vice versa).

   • Interpreting Results:

     • Look for similarities and convergence.

     • Handle discrepancies by stating limitations, revisiting data, or collecting additional data.

   • Challenges:

     • Requires expertise in both methods.

     • Different samples and sample sizes complicate merging data.

     • Merging data types and resolving contradictory results.

   Explanatory Sequential Design:

   • Data Collection: Quantitative data first, followed by qualitative data.

   • Sample Sizes: Qualitative study typically has a smaller sample.

   • Selection: Based on quantitative results; participants should be informed about the follow-up.

   • Interpreting Results: Determine if qualitative data provides a better understanding.

   • Challenges:

     • Time-consuming.

     • IRB issues.

     • Deciding which quantitative results to follow up.

     • Selecting participants for qualitative study.

   Exploratory Sequential Design:

   • Also Known As: Instrument development design.

   • Purpose: Generalize qualitative findings to a large sample.

   • Reasons:

     • Instruments not available.

     • Variables unknown.

     • No theory or model as a guide.

   • Design Samples: Different participants in quantitative and qualitative phases.

   • Phases:

     1. Participatory qualitative research in multiple sites.

     2. Participatory ranking of dimensions of deprivation.

     3. Nationally representative trial of new measures.

   Embedded Design:

   • A combination of qualitative and quantitative methods within the main design.

   Transformative Design:

   • Guided by a theoretical framework advocating for social justice.

   Multiphase Design:

   • Combines multiple methods over several phases.

   SummaryofMixed-MethodResearch

   Mixed-Method Research uses both qualitative and quantitative approaches within a single project. It often bases itself in pragmatism, requiring expert researchers and considerable resources to handle its complexity.

The complexity of this research needs expert researchers and a considerable time span and resources

Essentials of the Research Problem Statement

• Summary: Summarize existing knowledge about the phenomenon of interest and identify the research gap.

• Justification: Explain the importance of addressing this knowledge gap (significance statement).

• Population of Interest: Identify the population of interest, sometimes including the setting.

QuantitativeStudiesFramework

• Population: The entire group of interest.

  • Target Population: The entire set of individuals meeting the sampling criteria.

  • Accessible Population: The portion of the target population accessible to the researcher.

• Elements: Individual units of the population/sample.

• Sampling: Selecting a portion of the population to represent the whole.

  • Sampling Method: The process of selecting elements.

  • Sampling Unit: Non-overlapping collection of elements.

  • Sample: A subset of population elements.

  • Representative Sample: Reflects the characteristics of the population.

  • Sampling Bias: Systematic over/underrepresentation of a segment.

  • Sampling Criteria: Characteristics for membership/eligibility in the target population.

    • Inclusion Criteria: Characteristics required for the target population.

    • Exclusion Criteria: Characteristics for elimination from the target population.

Elements

• Can be a person, event, behavior, or any single unit of study.

  • Quantitative Research: Subjects or research participants.

  • Qualitative/Mixed Methods Research: Study participants or informants.

GeneralizingFindings

• In quantitative research, findings are first generalized to the accessible population, then potentially to the target population.

• Example: Reviewing records to determine low birth weight prevalence among babies born in Province A.

CriteriaofaGoodSamplingDesign

• Representativeness: Reflect both characteristics and variability of the population.

• Adequate Sample Size: Large enough for reliable generalizations.

• Practicality and Feasibility: Simple and straightforward to implement.

• Economy and Efficiency: Maximize information at the smallest cost.

SamplingPlan

• Describes strategies to obtain a sample for a study.

• Aims to enhance representativeness, reduce systematic bias, and decrease sampling error.

• Must be detailed for critical appraisal, replication, and future meta-analysis.

SAMPLING IN QUANTITATIVE RESEARCH

Probability (Random) Sampling Methods:

1. Simple Random Sampling (SRS):

   • Process:

     1. Establish a sampling frame.

     2. Number the elements.

     3. Draw a random sample using a random number table or online randomizer.

   • Advantages: Simple design and analysis.

   • Disadvantages: Costly if spread out, requires sampling frame, may not be representative if population is heterogeneous.

2. Systematic Sampling:

   • Process:

     1. Compute the sampling interval (k=N/n).

     2. Select every k-th individual starting from a randomly chosen point.

   • Advantages: Less time-consuming, easier to perform.

   • Disadvantages: Risk of systematic bias, widely spread units.

3. Stratified Random Sampling:

   • Process:

     1. Divide the population into strata.

     2. Select samples from each stratum through SRS or systematic sampling.

   • Advantages: Ensures subgroup representativeness, increases precision.

   • Disadvantages: Requires large sample size, detailed population information needed.

4. Cluster Sampling:

   • Process:

     1. Develop a sampling frame of clusters.

     2. Randomly select clusters.

   • Advantages: Useful when population frame is unfeasible, cost-efficient.

   • Disadvantages: Less precision, homogeneity within clusters.

5. Multi-Stage Sampling Design:

   • Process:

     1. Divide the population into primary sampling units.

     2. Subdivide each primary unit into secondary units and sample.

     3. Continue until the desired stage is reached.

   • Advantages: Sampling frame needed only for primary units, cost-efficient.

   • Disadvantages: Increased variance, complex data analysis.

CombinationofDesigns

• Often, sampling designs are not used in their pure form but are combined to suit the study's requirements.

Summary

Probability sampling ensures every member of the population has a chance to be selected, enhancing representativeness and generalizability. Various methods like SRS, systematic, stratified, cluster, and multi-stage sampling cater to different research needs, balancing precision, cost, and practicality.

NON-PROBABILITY SAMPLING IN QUANTITATIVE RESEARCH

Definition:

• Probability of each member being selected cannot be determined, so standard errors and statistical inference methods cannot be applied.

Advantages:

• Easier to execute.

• Less costly.

• Sometimes the only feasible method.

Disadvantages:

• Higher likelihood of bias.

• Cannot assess the reliability of sample results.

• Results are best for descriptive purposes, not for making generalizations.

TypesofNon-ProbabilitySampling:

1. Convenience Sampling (Accidental Sampling):

• Definition: Selecting participants based on ease of access.

• Advantages: Easy to obtain samples, low cost.

• Disadvantages: Results may not be generalizable due to narrow focus.

2. Quota Sampling:

• Definition: Divides population into strata, ensures representation by convenience sampling within each stratum.

• Advantages: Ensures subgroup representation.

• Disadvantages: Potential for bias due to non-random sampling within strata.

Non-ProbabilitySamplingMethodsinMixedMethodResearch:

1. Purposive Sampling:

• Also known as purposeful, judgmental, or selective sampling.

• Selects information-rich cases that can provide deep insights.

• Focuses on critical cases for understanding the study's purpose.

2. Network (Snowball) Sampling:

• Identifies participants through referrals from initial participants.

• Effective for finding participants who provide essential insights.

• Expands sample size from initial convenience or purposive samples.

3. Theoretical Sampling:

• Typically used in grounded theory research.

• Gathers data from diverse groups to develop theory.

• Encourages heterogeneity to represent a wide range of behaviors and situations.

GuidelinesforCritiquingQuantitativeSamplingPlans:

1. Was the population identified, and were eligibility criteria specified?

2. What type of sampling design was used, and does it yield a representative sample?

3. How many participants were in the sample? Was the sample size affected by refusals or attrition?

4. Were key characteristics of the sample described (e.g., mean age, percentage of female)?

5. To whom can the study results reasonably be generalized?

Summary:

Non-probability sampling methods are practical and cost-effective but come with limitations like potential bias and lack of generalizability. These methods include convenience sampling, quota sampling, purposive sampling, network (snowball) sampling, and theoretical sampling, each serving specific research needs.