Week 1, Chap 1 & 4: Role of Research to Support Nursing Practice PDF

**Week 1, chap 1 & 4: "Role of Research to Support Nursing Practice"** **Nursing research** - A systematic, rigorous, logical way to acquire knowledge to support the way nursing care is provided - Importance of research in nursing practice stems from Florance Nightingale who noted con...

**Week 1, chap 1 & 4: "Role of Research to Support Nursing Practice"** **Nursing research** - A systematic, rigorous, logical way to acquire knowledge to support the way nursing care is provided - Importance of research in nursing practice stems from Florance Nightingale who noted connections between sanitary conditions and death rates among wounded soldiers - In the early 20th century, the scientific method was developed to determine a systematic way to create knowledge and understanding about the world - Research is used to generate and test new theory related to nursing practice **Evidence informed nursing:** analysis of science information to make decisions about patients. **Evidence informed decision making:** is what I have available in order to inform care ej. hospital protocols, postpartum nursing research/review literature **Research process** - **Problem statement:** situation or problem that has emerged from practice. What are the best strategies to prevent youth smoking? - **Hypothesis:** your prediction - A **clinical question** is practical and specific, guiding patient care decisions (e.g., *\"What is the best antibiotic for pneumonia in elderly patients?\"*). - A **research question** is broader and investigative, aiming to generate new knowledge (e.g., *\"How does antibiotic resistance impact pneumonia treatment outcomes?\"*). Clinical questions help make **immediate** care decisions, while research questions explore **why or how** something happens over time. **Clinical question 3 elements:** 1. Situation 2. Intervention 3. Outcome   **Research Design:** is the how we did it, how does it look like. - Is it feasible? - Is it qualitative or quantitative? +-----------------------------------+-----------------------------------+ | Quantitative | Qualitative | +===================================+===================================+ | Objective | Subjective | | | | | Concrete | Interpretive | | | | | measured | Descriptive | +-----------------------------------+-----------------------------------+ +-----------------------------------+-----------------------------------+ | Quantitative | Qualitative | +===================================+===================================+ | Uses research questions and | Uses open ended questions. | | hypotheses to guide research | | | projects. | Requires the researcher to | | | interpret data to form thematic | | Includes variables: | ideas. | | | | | - Independent (X= the cause) | - You reach a saturation level | | | | | - Dependent (Y= the effect) | | | variables. | | | | | | X (amount of study time) → Y | | | (exam score)X (type of fertilizer | | | used) → Y (plant growth) | | +-----------------------------------+-----------------------------------+ **Research methods:** your recipe of how you are going to do your study - Selection and number of participants - Data Collection Methods - Surveys - Interviews - Document review - Data Analysis Strategies - Statistical Analysis - Thematic Analysis **Dissemination of research:** sharing results to inform and improve practice. **Qualitative research -- locations** Phenomenon Abstract, intro or both --------------------------- ----------------------------------------- Purpose Abstract, intro or others Literature review Intro, discussion or both Design Abstract, intro, methods, design Sample Methods, sample, subjects, participants Legal - ethical issues Procedures, description of sample Data collection procedure Data collection or procedures Data analysis Methods, data analysis Results Abstract, results, findings Discussion Discussion Recommendations At end of article  **Quantitative research -- locations** +-----------------------------------+-----------------------------------+ | Research problem | Abstract, intro (not labelled) or | | | in problem | +===================================+===================================+ | Purpose | Abstract, intro ot both, end of | | | literature review, discussion, | | | purpose | +-----------------------------------+-----------------------------------+ | Literature review | Intro (not labelled), literature | | | review, or related literature. | | | | | | Variables may appear as titles | +-----------------------------------+-----------------------------------+ | Theoretical framework, | Literature review, | | | theoretical/conceptual framework | | Conceptual framework, or both | | +-----------------------------------+-----------------------------------+ | Hypothesis/research questions | Intro, hypothesis, research | | | questions, results | +-----------------------------------+-----------------------------------+ | Research design | Intro (stated or implied), | | | methods | +-----------------------------------+-----------------------------------+ | Sample: type and size | Size: Methods, sample, subjects, | | | participants | | | | | | Type: stated or implied in any of | | | previous headings under size | +-----------------------------------+-----------------------------------+ | Legal -- ethical issues | Methods, sample, subjects, | | | participants | +-----------------------------------+-----------------------------------+ | Instruments (measurement tools) | Methods, instruments, measures | +-----------------------------------+-----------------------------------+ | Validity and reliability | Methods, instruments, measures, | | | procedures (stated or implied) | +-----------------------------------+-----------------------------------+ | Data -- collection procedure | Methods, procedure, data | | | collection | +-----------------------------------+-----------------------------------+ | Data analysis | Methods, procedure, data analysis | +-----------------------------------+-----------------------------------+ | Results | Results | +-----------------------------------+-----------------------------------+ | Discussion of findings and new | Discussion | | findings | | +-----------------------------------+-----------------------------------+ | Implication, | Combined with discussion or | | | separated | | Limitation, & | | | | | | Recommendation | | +-----------------------------------+-----------------------------------+ | References | At end of article | +-----------------------------------+-----------------------------------+ | Communicating research results | In research articles, poster and | | | paper presentations | +-----------------------------------+-----------------------------------+ A screenshot of a questionnaire Description automatically generated **Critical appraisal:** objectively determine the strength, quality, and consistency of evidence; these characteristics help you determine the applicability of the evidence to research, education, or practice.  **7 Levels of evidence** A diagram of a pyramid Description automatically generated May not say explicitly, might be in "methods" **Week 2, chapters 3 &5: "Searching, Reviewing and Critiquing the Literature**" A **literature review** is a summary and analysis of existing research on a specific topic. It identifies key findings, gaps, and trends, helping to understand the current state of knowledge and guide future research. What's its value? **Bias in literature review:** When you focus solely on studies that confirm your assumptions and beliefs about a topic. Done through: - **Published Journal articles** (main source): - Research studies - Systematic reviews - **Textbooks and books** - **Dissertations\*** - **Conference papers** - **Grey literature** A **dissertation** is a long, formal research paper or project required for a doctoral or master\'s degree. **Primary Sources (Original Research)** - Research studies - Dissertations - Conference papers **Secondary Sources (Summarize, Interpret, or Analyze Primary Sources)** - Systematic reviews - Textbooks and books - Published journal articles (if they are review articles, meta-analyses, or theoretical discussions) - Grey literature (depends on the type; reports and white papers often summarize research rather than conduct it) A **primary source** presents **original** research (they did the study), while a **secondary source** interprets or summarizes other research findings. (info from other than the original) - **Primary Source** = original, primary source; authors of the study - **Secondary Source** = getting the information from someone other than the original author. - May be necessary (ex. source is written in another language or not available to you) - Risks: may lead to misinterpretation and misrepresentation **Grey literature:** refers to **unpublished** research like reports or theses. It\'s harder to access and verify but can still be useful. You can't prove the sources. Probably not peer-reviewed, not widely disseminated/shared, sources are harder to verify, unjustified, even coming from a big-name company, they are not referencing anything. **(CINAHL) Cumulative Index to Nursing and Allied Health Literature** **Boolean Operators:** simple word used to complete or exclude keywords in a search (AND, OR, NOT). **Wild cards:** (pg. 96) **\*** = Nurs\*. Multiple characters **\#** = ped -\> pe\#a, color -\>colo\#r = x word that can be spelled different **?** = for "ne?t". A word that you don't know the middle/missing letter is  A paper with text on it Description automatically generated **Critical Appraisal of Research Articles** x7 Considerations: - Where is the article published? - Is the journal peer-reviewed? - Who are they and what are their credentials? - What knowledge and expertise do they have in the area of research? - What is the phenomenon of interest and is it clearly communicated? - Are the research questions clearly communicated? - Do they align with the methods used in the research study? - Will the findings from the study contribute to knowledge in the field of nursing? - Is there congruency between the methodology and methods used? - Is there a [theoretical framework] that guides the research process? - Is the method adequate for addressing the phenomenon of interest? - Were participants chosen appropriate for informing the research? - Was there anyone overlooked or excluded? - Are the data collection and analysis procedures explained? - Qualitative Studies: - Is **[saturation level]** of data described? - Are the participants voices adequately represented? - Does data analysis process offer an interpretation of the data, conceptual framework or theory? - Quantitative Studies: **[clear measuring tool is identified]** - Is the data collected using valid and reliable instruments? - Is the sample representative of the population being studied - Do the analysis techniques match the data collection strategies - Does the research discuss how they promoted rigour, validity or trustworthiness?  Nurses improve quality care and patient safety through accurate documentation, evidence-based practice, (CDSS) clinical decisions support systems use, and reduced complications. **Week 3: Exploring Prominent Paradigms and Theoretical Frameworks that Guide Nursing Research. Chap 2 & Journal** **Nursing Knowledge that Informs Practice** **Empirical and Theoretical Knowledge** - Serves as a guide for evidence-informed practice - Theoretical knowing is concerned with developing or testing theories or ideas that nurses and researchers have about how the world operates - Theoretical knowing is informed by empirical knowing which involves observations of reality - All research is based on philosophical beliefs about the world and they are based on a worldview or what we call Paradigm **Paradigm x3 (on pink below)** - [Ways of viewing the world ] - Form the foundation from which research is undertaken - Includes a set of assumption about reality, how knowledge is created and what is valuable to learn - Some nursing researchers gravitate towards one specific paradigm; however, remember that [our research question and process should drive the selection of a paradigm] - All aspects of a research study must flow from the chosen paradigm - Three prominent paradigms in nursing are [positivism/post-positivism, constructivism, and critical theory]. **Terminology to Understand Paradigms** A screenshot of a computer Description automatically generated 1. **Positivism/Post-Positivism Paradigm** **Ontology/Epistemology:** (*what's it is, why exist?)* - **Post-Positivism:** - Aims for objectivity and impartiality (**producing unbiased and generalizable research**) - Focuses on [cause and effect] (**causality**) - **emphasizes the use of empirical data to understand the world** - Rejects the idea that we can see the world perfectly as it is because we are human beings and are subject to error and bias - Full objectivity and impartiality cannot always be relied upon - **recognizes the limitations of empirical data and emphasizes the importance of subjectivity in research**. - Scientists are required to put aside their biases see the world as it "really" is - Triangulation of data and replicating of findings encouraged - Intense scrutiny of research and ejection of poorly conducted research **Positivism/Post-Positivism Paradigm** **Aim:** to explain, predict and [control] **Methods:** - Dominantly quantitative methods are used - Experimental and non-experimental approaches are used - Includes research questions and hypotheses as well as accounting and controlling factors that might influence the research process 2. **Constructivism** **Ontology/Epistemology:** - Objectivity is not possible nor desirable as [knowledge is co-created] - Focuses on understanding human experiences, social and cultural constructs, values, perspectives and language - The ways which we understand our world are largely dependent on our perceptions and context - Research emphasizes the [meaning ascribed/assigned to the human experience] - There are multiple truths and truth is relative to the context **Constructivism Paradigm** **Aim:** Understand social realities and interpretations of meaning **Methods:** - Predominantly Qualitative in nature requiring interpretation of interview data, document or artifact review (pictures, words, art, etc.) - Research is a process of interaction between the researcher and participant \*Also referred to as *[interpretivism or relativism]* 3. **Critical Theory** *(social imbalances -- truth comes by who has the power)* **Ontology and Epistemology:** - Reality is constructed by those in power at a particular time and place - Objectivity is not a desired outcome, perceptions influence knowledge generation and creation - Understanding is shaped by numerous social, political, economic and cultural factors - Gender, sexual orientation, class and economic status, race and ethnicity, ability, geographical location, etc. - [Requires the researcher to understand how power imbalance associated with these factors influence health and well-being outcomes] - Specific critical theories include: *[feminist theory, critical race theory, intersectionality, etc.]* **Critical Theory Paradigm** **Aim:** Emancipation (*collective and individual liberation, freeing someone from oppression/limitations*) and social change, reconstruction of what we know **Methods:** - Inquiry requires a dialogue between the researcher and participant - Dialogue is transformative, and brings forwards the historical context behind experiences of suffering, conflict and collective struggle - Predominantly Qualitative in nature and may include interviews and review of documents and artifacts **Research Methods Qualitative (review)** - Qualitative Research:  - Systemic, interactive research method used to describe and interpret life experiences - Emphasis is on capturing personal perception of the study participants (studying the human experience) - What is the experience of managing chronic conditions in the community from the perspectives of older adults, family caregivers, and health care providers working in a variety of settings? - Quantitative Research - Explore, describe and explain a phenomenon or generate theory - Testing for the presence of specific relationships, assessing for group differences, clarifying cause and effect interactions or explaining how effective a nursing intervention was - Involve precise and controlled measurement techniques to gather data and they are analyzed statistically - Is there a difference in prevention of osteoporosis in at-risk survivors of breast cancer who receive a combination of long-term progressive strength training exercises and vit D in comparison with those who do not receive this treatment?  **Types of Research Methods** - Mixed Methods Research - Design of the study includes [both quantitative and qualitative methods] - Common in program evaluation, organizational studies and policy development - Supports triangulation (multiple sources) of data **Ladder of Abstraction** **Frameworks to Guide Research** - Research and practice are linked with theory - **Conceptual framework "Road map of our study":** - **Helps you to visualize your project and put it into action (define variables etc)** - A structure or assembly of concepts that is used as a map or scaffolding (guidance) of ideas for a study. - Provides an understanding of a phenomenon of interest and identifies what factors are most significant as we examine various aspects of health. - **Theoretical framework:** - **Describes theories underpinning the research problem** - Unified set of interrelated concepts that explains or predict phenomena - Theory guides practice and research - Research can be used to test a theory and theory building Kolb's **Functions of a Framework** - **Inductive reasoning:** Start with details of experience and move to a general picture. - Ie. More qualitative research uses literature and theory to guide research process development but will usually not use the theory for data analysis. - **Deductive reasoning:** Start with a general picture and move to a specific direction; uses two or more concepts. - Ie. More quantitative research staring with general picture and then testing the theory to confirm cause/effect relationships. **Inductive:** - Specific to general. - Observations Generalization. - Develops theories from data - *I see three cats with whiskers → All cats must have whiskers.* **Deductive:** - General to specific. - Theory Conclusion. - Tests theories with data - *All birds have wings → A robin is a bird → A robin has wings*. **Theories to Guide Research** Prominent theoretical frameworks used to guide nursing theory include: - Systems theory - Self-efficacy theory - Social determinants of health framework - Pain theory - Nursing Theory - Watson's Philosophy of Transpersonal Caring - Paplau's Theory of Interpersonal Relations **Concepts and Variables** - Concepts: - Image or symbolic representation of an abstract idea (ex. Pain, Environment, Socioeconomic status, Learning) - They are a major component of theory and convey the abstract ideas within a theory - Variable: - Property that is being studied and something that changes or varies over time or with an intervention - Studies often are conducted to understand how changes in one variable impact another (independent and dependent variables) **Evaluating and Critiquing the Use of Theory in Research** - Is the framework identified clearly? - Where would it be discussed in a research article? - Is the framework consistent with a nursing perspective? - Does the framework align with the phenomena being studied? - Are the concepts and variables clearly defined? - Is there a logical, consistent link between the framework and concepts being studied? - Are the study findings examined in relation to the framework? **Practice Questions:** - Where is the literature review typically found in a research report: a. In the abstract b. In the discussion section c. Following the methods section d. After the introduction at the beginning of the report - Which of the following statements identifies how the literature review supports and links to the research process e. Literature review guides all steps of the research process. *Is a summary of what others have already discovered about a topic. It organizes the main ideas and studies so you can understand what's been done before and how your work fits in or adds something new.* f. Literature review is necessary only in defining the problem statement. g. Literature review provides a vehicle to disseminate the findings of the study. h. The value of the literature review is limited to finding gaps or inconsistencies in the knowledge base - Which of the following is an appropriate method of ***"weeding out"*** irrelevant sources when conducting an electronic search for articles for the literature review on a selected topic? i. Avoiding articles from clinical journals j. Avoiding articles that do not include the term "study" in the title k. Selecting on those articles to read whose abstracts indicate to align with your clinical question. *Reading abstracts is a quick and effective way to assess relevance before delving into full articles.* l. Colleting and critically readings all articles published in the last 5 years that related to the topic area - What is commonly referred to as scientific knowledge? m. Ethical n. Personal o. Empirical: *Scientific knowledge is based on empirical evidence, derived from observation, experimentation, and systematic data collection.* p. Aesthetic **Week 4: Quantitative Research Design and Methodology. Chap: 10,11,12**  **1. How are conceptual and theoretical frameworks used differently in quantitative vs qualitative research?** - **Quantitative Research:** - **Theoretical Frameworks:** Researchers use a **pre-existing, unified theory** (e.g., systems theory or self-efficacy) to guide their study, explaining and predicting phenomena. It structures research and guides hypothesis testing. - **Conceptual Frameworks:** Less common but sometimes used to organize variables and concepts. - **Qualitative Research:** - **Conceptual Frameworks [(road map):]** Often **emerge during the study**, guiding exploration of ideas. These frameworks are flexible and grounded in literature but allow for new insights to develop. - **Theoretical Frameworks [(blueprint/instructivo):]** Rarely used at the start; if used, they are more to **sensitize** the researcher to existing theories rather than structure the study. **Key Difference:** Quantitative research uses pre-defined frameworks to test theories, while qualitative research uses frameworks more flexibly to explore and understand phenomena. **2. Which research method (qualitative or quantitative) does inductive vs deductive reasoning align with?** - **Deductive Reasoning:** - **Definition:** Starts with a theory and tests it through specific observations. - **Alignment:** Aligns with **quantitative research** because it focuses on hypothesis testing based on established theories. - **Inductive Reasoning:** - **Definition:** Begins with specific observations and builds toward a general theory or pattern. - **Alignment:** Aligns with **qualitative research** because it explores meanings and experiences, often with open-ended data collection. **Key Difference:** Deductive reasoning starts with theory (quantitative), while inductive reasoning builds theory from data (qualitative). **Theoretical** vs **Conceptual Framework**: **Aspect** **Theoretical Framework** **Conceptual Framework** --------------------- ------------------------------------------------------------------------- ------------------------------------------------------------------------------------- **Definition** A unified set of interrelated concepts to explain or predict phenomena. A structure of concepts that guides the study, often built from existing knowledge. **Purpose** Explains and predicts relationships between variables. Organizes concepts and ideas to explore a phenomenon. **Development** Developed based on established theory. Developed from literature, often flexible and emergent. **Use in Research** Guides hypothesis testing and measurement of variables. Guides exploration and understanding of the study topic. **Example** *Self-efficacy theory in health behavior studies.* *Exploring how social support impacts mental health outcomes.* **Timing** Precedes the research; established before data collection. Can evolve during the research process. - **Theoretical Framework** = **Objective, hypothesis testing** (often quantitative: surveys, experiments). - **Conceptual Framework** = **Subjective, exploration** (often qualitative: interviews, focus groups). **QUANTITATIVE PROCESS** **Elements of research design:** 1. Participants (who) 2. Observations (what) 3. Measurement of time (when) 4. Selection of participants (where) 5. Role of investigator  **Variables:** 1. **Independent Variable (X):** The cause. - **Experimental research:** It's manipulated (e.g., testing IM vs. oral medication). - **Non-experimental research:** Naturally occurring - not manipulated (e.g., smokers vs. non-smokers). 2. **Dependent Variable:** The effect - Observed outcome that changes based on the independent variable (e.g., lower pain ratings after oral medication). 3. **Control Variable:** Variables kept constant to ensure results are accurate (e.g., using patients with the same diagnosis). **Internal validity** *[(**cause-effect test**):]* Checks whether the independent variable (cause) truly caused the change in the dependent variable (effect), without interference from other factors. **Threads to internal validity:** *(we are trying to reduce bias)* 1. History - Other events going on at the same time - Ex. Traumatic event occurs while collecting data from a group of participants,COVID 2. Maturation (getting older or growth, development) - Biological or psychological process that occur within the individual - Ex. Age change, significant life event that occur during data collection process (ex. Pregnancy, loss of parent, etc.) 3. Testing - Taking a test multiple times 4. Instrumentation *(tool has to be the same x everyone, height measure with different things - wrong)* - Changes in way observations or instruments impact obtained measurement - Ex. Several researchers collecting data or observing for an event 5. Mortality/Attrition - Loss of participants - Selection bias **External Validity** refers to how well study findings [can be generalized] to other settings, populations, or times. Determines how generalizable and applicable study findings are to different settings, populations, and situations, and identifies any limitations in their broader use. Threads: [selection effects, reactive effects, measurement effects. ] - **Without internal validity**, there's no basis for generalizing (external validity). no internal = no external. **Quantitative design types x3: LEVEL II** 1. **Experimental/ "randomized control trial" best kind** - **Types of designs:** 1.True experimental 2.Solomon four-group 3.After-only - **Characteristics:** - Randomization - Control - Manipulation - **Rules out threads** - **Identifies cause and effect relationships between variables** 2. **Quasiexperimental (mix) -- LEVEL III** - **Types of Quasi-experimental designs:** 1. Nonequivalent control group design 2. After-only nonequivalent control group design 3. One-group pretest-posttest design 4. Time series design - Tests "cause and effect" when full control isn't possible - **Control** may be limited due to the nature of the study or participant availability. - [It **may not have randomization or a control group**.] - However, it still **includes an experimental treatment**. - This design is used when true experimental methods can't be applied. **Aspect** **Evaluation Research** **Example** ------------------------- ------------------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------- **Purpose** Evaluate outcomes of programs/interventions **Designs Used** \- **Experimental**: Full control possible **L II** Testing new med on pts \- **Quasi-Experimental**: Limited control **LIII** Real -- world **protocols, strategies** (education) or **planning** (pain mngt, d/c) \- **Non-Experimental**: Observational -- events as naturally occur. Have the least amount of control Observing pt recovery after surgery in a single unit overtime **Common Applications** Testing new programs, improving care **Goal** Measure effectiveness, support quality improvement 3. **Non-experimental** **Types:** **1.Survey Studies** *(perception)* - Descriptive - Exploratory - Comparative **2.Correlational** *(relationship)* **3.Developmental Studies** *(time-based)* - Cross-Sectional  - Longitudinal  - Retrospective **4.Methodological** *(measurement tools)* - Methodological research - Systematic Reviews - Integrative Reviews - Secondary Analysis - [Examine events, people or situations as they naturally occur], or test relationships and differences among variables - Independent variable is not manipulated - Control within study still considered *SURVEY STUDIES (perception)* Classification: - Descriptive - Exploratory - Comparative - Collect information like **opinions, attitudes, or facts**. - These studies use **descriptive** and **inferential (prediction) statistics** to find **relationships** between variables - Goal is to identify **differences** between them, not prove cause and effect. - Can be qualitative or quantitative [Not testing cause -- effect, but 1 variable change or relation to another] *CORRELATIONAL STUDIES -- (relationship)* - Examines the relationship between two or more variables. - Does **not** test for causation, only how one variable changes in relation to another. - Used to test hypotheses about the relationship between variables. - Example: Investigating if more study hours lead to better test scores. *DEVELOPMENT STUDIES (time-based)* - Types: 1. **Cross-sectional**: One slide of something. Data collected [at one point in time], not over time (e.g., one-time survey on health). 2. **Longitudinal**: Data collected over several time points from the same group (e.g., monitoring diabetic children over years). 3. **Retrospective**: Looks at past events or exposures (independent variables- cause) to understand their impact on current outcomes or conditions (dependent variables - effect), e.g., studying long-term effects of [smoking] (independent) on the development of [cancer] (dependent). \- Examine changes overtime, looking at relationships between variables at different points *METHODOLOGICAL RESEARCH (measurement tools)* - About making and testing tools like [surveys] to collect data - **Psychometrics** = theory and development of measurement instruments (surveys or questionnaires) and measurement techniques  - Helps create better ways to measure things, like a [scale] to check nurses' confidence in their work **Additional types of quantitative studies** 1. **Secondary analysis:** - Researcher reanalyzes the data from a (experimental or nonexperimental) study for a completely different purpose. - Would still have a research question and hypothesis - Ex. Take an existing data set from a previous study and ask a new question 2. **Epidemiological studies:** - Examine factors affecting the health and [illness of populations in relation to their environment.] - Investigate the distribution, determinants, and dynamics of health and disease. - Are often [prevalence or incidence focused] 3. **Review of literature:** - Systematic Reviews: [Summarize research] on a topic using strict methods. - Meta-Analysis: [Combines] study [results to find] overall [trends.] - Integrative Review: Combines [both qualitative and quantitative] studies. - Why are they done? - To gather and analyze all available evidence to make informed decisions. - **[Cochrane Collaboration]** is an international organization that ensures systematic reviews are high-quality and trustworthy. **Evaluating Quantitative Research** **Questions for Clinical appraisal (critique) of a research study** - General Critique Criteria: - How does the literature review process and theoretical frameworks inform the design of a quantitative research study? - Additional Critiquing Criteria: - What design is used? - Is the design experimental, quasiexperimental or non-experimental? - Is the problem one of a cause-and-effect relationship? - Is the method used appropriate for the problem? - Is the design suited to the study setting? - Study design appropriate? - Control measures match design? - Design reflects feasibility? - Design flows from research question, framework, literature review, hypothesis? - Control of threats to internal validity? - Control of threats to external validity? - Can you link the design to the appropriate level of the evidence hierarchy? - Experimental Critiquing Criteria - What experimental design is used? Is it appropriate? - How are randomization, control, and manipulation applied? - Are there reasons to believe that alternative explanations exist for the findings? - Are all threats to validity, including mortality, addressed in the report? - Quasiexperimental Critiquing Criteria - What quasiexperimental design is used? Is it appropriate? - What are the most common threats to the validity of the findings? - What are the plausible alternative explanations for the findings? Are they addressed? - Does the author address threats to validity acceptably? - Are limitations addressed? **Week 5: Quantitative Research Methods: Sampling, Data collection & Analysis Chap: 13, 14, 17** **Sampling:** Selection of a representative population for study. Concepts involved in sampling: 1. Population: - Well-defined set that has certain specified properties or characteristics from which data is gathered - The population will have unique descriptors that qualify them as meeting the inclusion and exclusion criteria for the study 2. Inclusion Criteria (Eligibility criteria): Characteristics that meet the requirements for inclusion in a study 3. Exclusion Criteria: Characteristics that restrict the population from being included in a study **Sampling and Representativeness:** - A sample is selecting a [portion] or subset [of a designated population] - The sample chosen needs to [represent the entire population] under study [Representative sample] - Examining every individual in a population would not be feasible so a sample of that population is obtained - Ensuring a sample is representative of a population ensures that a sample [is generalizable] Two types of sampling strategies: 1. Nonprobability sampling (non-random) CHOICE 2. Probability sampling (randomization of sample) CHANCE 1. **Nonprobability sampling (nonrandom):** - Elements or characteristics are chosen through nonrandom methods - Most [common form] of sampling in [nursing research] - Most [common] used sampling [strategies] are [*convenience* and *quota*] 2. **Probability sampling (randomization of sample):** - Form of random selection of characteristics or participants - Most [common] sampling [strategies] are [*simple random sampling, stratified random sampling, cluster sampling* and *systematic sampling*]  **Sample size** depends on the following: - Type of design used - Type of sampling procedure used - Type of formula used for estimating optimum sample size - Degree of precision required - Heterogeneity of the attributes under investigation - Relative frequency of occurrence of the phenomenon of interest in the population (i.e., common versus rare health problem) - Projected cost of using a particular sampling strategy - Sample size is [estimated using] a statistical procedure called a [power analysis] - **Power Analysis** (*Size*) -- A math method to **decide how many people** need to be in the study. - **Effect Size** (*Difference*) -- Measures **how big or small** the difference is between two groups. - **Small Effect = Big Sample** (*More*) -- If the difference is **tiny**, we need **more people** to see it clearly in the results. **Components of a power analysis: ** 1. **Significance Criterion** (*Threshold*) -- The cutoff for deciding if results are real (usually 0.05 or 0.01). 2. **Effect Size** (*Impact*) -- How strong the relationship is between variables (estimated from past studies). 3. **Power (1-β)** (*Confidence*) -- The chance of correctly finding a real effect (usually 0.8 or 80%). 4. **Sample Size** (*How Many?*) -- The number of people needed (this is what we solve for!). - Sampling Procedure - **Critique for the following criteria: ** - The sample characteristic are clearly described, inclusion and exclusion criteria specified - To what extent the sample is representative of the target population? - Would it be possible to replicate the study population? - Method of sampling described is appropriate to the chosen population? - Has bias been introduced by the sampling method? - How have the rights of participants been preserved? - Have limitations to generalizability of the study findings been identified and are they appropriate? Activity: - Your research team is trying to understand how effective the supports and resources provided within your nursing program support student success.  You have developed and tested an instrument to evaluate various resources and want to survey students in the HBSN program. - In groups create a sampling strategy to collect data. 1. Who is your population 2. What is your inclusion and exclusion criteria 3. What type of sampling strategy will you use (probability or non-probability sampling) 4. How might you recruit students to participate in your study 5. Consider what your research question might be and ensure that there is alignment with your search strategy 1. **Population:** students in the HBSN program 2. **Inclusion criteria:** to be a the HBSN program with access to college resources and that have 1 year in the program. **Exclusion criteria:** (the opposite) Students not in the HBSN program without access to college resources or with less than one year in the program. 3. **Probability sampling:** (randomization of sample) CHANCE (Everyone in the population has an equal chance of being selected, through [randomization.] Example: Picking names out of a hat.) 4. **Recruitment:** (Thru incentives) \$5 Tim Hortons gift card (if they qualify and participate), email invitations or in-class announcements to boost participation 5. **Research question:** In/among [students in the HBSN program] (P) how does [the use of college supports and resources] (I) compared [to not using them] (C) affect [student success] (O) within [a year] of the program (T)? **Data Collection** - ***Objective:** Data is **unbiased** and based on facts, not opinions or personal feelings.* - ***Systematic:** Data is collected in a **structured and consistent** way to ensure reliability and accuracy.* - The process of translating a concept of interest into observable and measurable phenomena - The success or reliability of a study depends on the data collection methods used - Methods are the ways in which researchers collect and analyze data from participants **Methods** for collecting information about a phenomenon: 1. **Biological and physical indicators of health**: Blood pressure, heart rate, blood tests, other physical tests 2. **Psychosocial Variables** (anxiety, hope, quality of life): Observation of behaviour, self-reports about attitudes, questionnaires, interviews. 3. **Other** sources of data: Records, diaries, media, data from another study **Data Collection Strategies** 1. **Physiological or biological measurements** 2. **Observational methods:** With or without concealment 3. **Questionnaires** 4. **Records or available data**: - Hospital records - Historical documents - Audio or videotapes 5. **Interviews:** - Open-ended - Closed-ended **Measurement Tools (**Questionnaires and Surveys) - Gather data from participants about knowledge, attitude, beliefs and feelings - Survey -- can include a questionnaire and/or an interview - Questionnaire -- written set of questions within the survey aim to gather information on a particular concept/topic. - Many use a Likert-type scale as well as open-ended questions *("how satisfied are you? Very moderate, not satified")* - Open Ended Questions - Constructing a New Data Collection Instrument: - Requires the research to define the construct/concept being measured - Formulate items or questions that represent the concept - [Assess the items for content validity] (do they measure what they are intended to measure) - [Pretest and pilot test the items] - [Estimate reliability and validity] of the tool [using statistical tests] - Example of a Measurement Tool **Data Consistency:** - - - - - **Response Rate:** the percentage of people who complete a survey or participate in a study out of the total number invited. **Factors that impact the response rate** in the collection of **quantitative data**: - **Survey Design:** Clear, concise, and easy-to-understand questions can increase participation. - **Incentives:** Offering rewards (like gift cards) can encourage more people to respond. - **Timing:** Sending surveys at convenient times or multiple reminders can boost responses. - **Length of the Survey:** Shorter surveys tend to get higher response rates. - **Privacy and Anonymity:** Ensuring confidentiality makes participants more likely to respond. - **Sampling Method:** If your sample is hard to reach or not motivated, response rates might be lower. **Critiquing Criteria** - Are the data collection methods clear? - Are all instruments identified? - Is the method used appropriate? - Were the collection procedures similar for all participants? - Could you replicate the data collection? - Were precautions taken to prevent bias? (randomization, blinding -- certain info is hidden) **Data Analysis** - A process of analyzing the data so that meaningful results are obtained - Data analysis [techniques] are selected based on: 1. the design, 2. type of data collected, 3. the research questions that were tested - In **quantitative data analysis**, **[statistical procedures]** are used to organize and give meaning to the data (descriptive and inferential statistics) 1. Descriptive statistics: Summarize and organize data about the sample characteristics variables in a research study (Think of them as [describing the population]) 2. Inferential statistics: test for relationships and help the research make predictions and generalize findings based on the data ([make inferences or tests hypotheses about a sample]) 1. **Descriptive Statistics (is visual** 👁️**)** - Description and/or summarization of sample data - Allow researchers to arrange [data visually to display meaning] and to help in understanding the sample characteristics and variables under study. - In some studies, descriptive statistics may be the only results sought from statistical analysis. - Supports review of data in manageable proportions by summarizing - Measure of central tendency and measures of variability (spread) - Scatter plot, histograms, table, bar graph - **Levels of Measurement / Data measurement Scales:** helps to determine the type of "statistical analysis" (descriptive/inferential statistics) that can be done: +-----------------------+-----------------------+-----------------------+ | Qualitative scales | 4. **Nominal** | - Classify | | | | objective (facts, | | | *(no specific order)* | things you can | | | | see) or events | | | | into categories | | | | | | | | 1. Dichotomous varia | | | | ble | | | | (2 categories | | | | --"yes/no", | | | | gender) | | | | | | | | 2. Categorical | | | | | | | | - Ex Gender, | | | | martial | | | | status, true vs | | | | false | | | | | | | | **Label variables | | | | without any | | | | quantitative value, | | | | data in categories | | | | without specific | | | | order (eye color, | | | | taste...)** | +=======================+=======================+=======================+ | | 1. **Ordinal** | - Shows relative | | | | ranking of | | | *(in natural order -- | objectives | | | ranking- subjective) | | | | SATISFACTION* | - Numbers are | | | | assigned to each | | | | category can be | | | | compared | | | | | | | | - Higher ranking | | | | category can be | | | | said to have more | | | | of an attribute | | | | than the lower | | | | ranked categories | | | | | | | | - Ex. Ranking by | | | | level of | | | | education, Likert | | | | scale responses | | | | | | | | **Categorize data in | | | | natural order, ex in | | | | physio pt says pain | | | | in "much better, a | | | | bit better...", size | | | | of steps between | | | | items is unknown. (or | | | | "how satisfied you | | | | are..")** | +-----------------------+-----------------------+-----------------------+ | Numeric/ Continuous | 2. **Interval** | - A scale with | | Scales | | **equal** | | | *("space in between"- | intervals between | | | exact values between | the numbers | | | units - | | | | **comparison**) | - **[Zero point is | | | **SCALES** -- HUMAN | arbitrary (you | | | MADE - SUBJECTIVE* | choose | | | | it)]* | | | | * | | | | | | | | - Ex. Temperature, | | | | Beck Depression | | | | Inventory, Test | | | | scores | | | | | | | | **Items have an order | | | | + difference between | | | | categories is | | | | identical.** | | | | | | | | **No zero-point or | | | | interval scales but | | | | chosen zero point** | | | | | | | | **Ex. Temp measured | | | | in C / F. The | | | | difference between | | | | 20C and 30C & 10C and | | | | 20C is both 10 -- so | | | | the intervals are | | | | identical. 0C doesn't | | | | mean the absence of a | | | | value, is just | | | | another number used | | | | in the scale** | | | | | | | | **We cannot say that | | | | 20C is twice as warm | | | | as 10C** | | | | | | | | **Negative numbers | | | | also have a meaning** | | | | | | | | **"premium" vs | | | | "basic" streaming | | | | plan -- you don't | | | | have twice as much as | | | | channels, you just | | | | know that premium is | | | | higher.** | +-----------------------+-----------------------+-----------------------+ | | 3. **Ratio** | - Variables are | | | | ranked on scales | | | *(all the above + a | **with equal | | | true zero point- | interval and | | | **comparison**) | absolute zeros** | | | **FACTUAL** -- IT IS | | | | WHAT IT IS* | - Generally, only | | | | achieved in the | | | | physical sciences | | | | | | | | - Ex. Weight, blood | | | | pressure, height | | | | | | | | **Have all the above, | | | | so they tell us about | | | | the order, about the | | | | exact values between | | | | units, plus they do | | | | have an absolute zero | | | | point** *(cero vale | | | | cero)* | | | | | | | | **Ex. age, wt, which | | | | both have a true zero | | | | point *(cero vale | | | | cero).* "2 m twice as | | | | long as 1 m",** | | | | | | | | **"a 40 yo is twice | | | | as old as a 20 yo"** | +-----------------------+-----------------------+-----------------------+  **Levels of Measurement:** Name the Level of Measurement! 1. Gender Nominal 2. Comparison of GPA scores Interval 3. Temperature Interval 4. Marital status Nominal 5. Blood pressure Ratio 6. Rank level of satisfaction of care received Ordinal 7. Weight Ratio **Data analysis Using Statistics (x2):** **Descriptive Statistics** - Frequency Distribution - Organizes data by the number of times each event occurs - For example, test grades are grouped in a range and the number of student receiving each group of grades is reported - Note symmetry (distribution is shaped a bell) and kurtosis - Central Tendency: Summarizes the middle of the group - Mode: Most frequent score - Median: Middle score - Mean: Average score - Normal Distribution: A theoretical concept that observes that [**interval** or **ratio** data group themselves about a midpoint in a distribution closely approximating the normal curve]. - Skewness - - - - Variability or Dispersion (r/t kurtosis) - Relates to the [spread of data] - [Answers] questions such as "[Is the sample homogeneous or heterogeneous" and "is the sample similar or different"] - Used to describe the des?? in the dispersion of data - Range: - Difference between the highest and lowest scores - Always reported with other measures of variability - Standard Deviation (SD) - Average deviation from the mean scores - Relates to a normal distribution curve - Always reported with the mean and sometimes the range - A small SD means the [scores are very close to the mean] - A larger SD means data [points are spread out over a large range] of values **Inferential Statistics** - Combines mathematical processes with logic; allow researchers to test hypotheses about a population using data obtained. - Two main objectives of using this analysis technique: 1.[Estimate the probability] that statistics found in the sample accurately reflect the population 2.[Test hypotheses] about a population - Inferential statistics [allow] researchers to [make a statement or draw a conclusion about the larger population] from studying a sample. - [Uses probability to determine how confident we can be about the conclusions we have drawn] - Require: 1. Participant to be selected randomly -- using probability methods 2. The sample must also be representative. - This [allows you to make generalizations about a population] from a sample - [If] these are [not met the conclusions] from these tests [can be invalid] - Sample size calculations [help to estimate the number of participants required] for statistical decision making **Hypothesis Testing** - Allows researchers to answer questions such as: - "How strongly are these two variables associated with each other" - "How much of this effect is a result of chance" - "What is the effect of this intervention" - There is a scientific hypothesis and the null hypothesis: 1. **Scientific hypothesis (H₁)** -- is what the researcher believes the outcome to be (ex. Taking this drug therapy will slow the progression of cancer) 2. **Null hypothesis (H₀)** -- is that no difference exists between the groups or as a result of the intervention. - In **rejecting the null hypothesis,** we can infer that the treatment/intervention has a statistically significant effect - Identifying that the changes in effect are unlikely to happen by chance -- "*tx is working"* **Probability** - The chances of something happening frequently (having the same repeated outcome) in repeated trials under similar conditions. - Provide support for the scientific hypothesis by rejecting the null hypothesis - **Type I and Type II Errors:** - Type I: Rejection of the null hypothesis when it is actually true --"no te creo, vieja puta" -- estoy segura de que es falso" - Type II: Accepting the null hypothesis when it is false --"si amiguis tienes razon, te apoyo (pense que si pero no era cierto)" - Increasing sample size will increase accuracy of the estimates about a population -- (sample size calculations using power of 0.8) **Level of Significance (Alpha level)** - Translates into the probability of making **Type I** error (ex. Probably of type one error = 0.05) - Level of significance is set at **0.05** or **0.01** at the beginning of a study (a priori) - Ex. The researcher is willing to accept the fact that if the study was done 100 times, the decision to reject the null hypothesis would be wrong 5 times out of those 100 trials. *(5%)* - Can set probability at 0.01 if one wants a smaller risk of reflecting a true null hypothesis - Ex. the decision to reject the null hypothesis would be wrong 1 time out of 100 trials) *\*FYI null hypothesis del que hablan aqui -\> se refieren a H0* - Selected alpha level depends on how important it is not to make an error. *(que tan exigente quieres ser -- 0.01)* - A statistically significant hypothesis = finding unlikely to have occurred by chance *(significa que lo que pense que era fue -- prove myself right)* - Magnitude of significance is important to the outcome of data analysis. *(0.05 or 0.01 significa algo)* - P (probability)-value is the exact level of significance -- it is calculated from the sample data - Therefore, if P\< alpha then the null hypothesis is rejected *= Type I error (tu nivel de significancia)* - We can then say the results are statistically significant **Statistical Analysis** - Tests used depend on the level of measurable of the variables and the type of hypothesis being studied - Tests may analyze for: - If there is a difference between groups (tests of difference) - Ex. Is there a difference between two groups when one has been provided an intervention, and one has not. - If a relationship exists between two or more variables (tests of relationships) **Tests of Difference** - The *t* statistic (t-test): [Tests whether the means of two groups are different]. - Requires **interval** or **ratio** level data - There is also a paired, correlated t test -- which is used [if the groups are related at all prior to the intervention] - Analysis of Variance (ANOVA): [Tests variations between and within multiple groups] - Tests whether group means are different and accounts for the variation between groups and within groups 1. One-way ANOVA, 2. Two-way ANOVA, 3. Repeated-Measures ANOVA - Analysis of Covariance (ANCOVA): [Measures differences among group means on an important variable, check whether groups are different at the beginning of a study or baseline] - Allows researchers to control for confounding (unmeasured) variables statistically - MANOVA: Measures differences in group means [when there is more than one dependent variable] **Tests of Relationship** - [Explore the relationship between two or more variables reflecting interval data] - Uses statistics that determine the correlation or degree of associations between two or more variables - Inferential statistics allow researchers to estimate how reliably they can make predictions and generalize findings (to draw conclusions that extend beyond the study data) - When we reject the null hypothesis, we are suggesting the variables tested are related and we are seeing a significant difference - **Correlation coefficients range from −1.0 to +1.0** - Determine the degree of association - 0 means no relationship, positive numbers indicate a positive relationship, negative numbers indicate a negative relationship - Ex. +1 may suggest as age increases so does length of hospital stay vs. -1 as age decreases the length of stay increase - Closer the number is to -/+1 the stronger the correlation is - [With all statistical analysis, the larger the sample size the greater chance of finding a significant correlation] - In considering the correlation coefficient the researcher explores whether the magnitude of the relationship is enough to have occurred by chance. - This is reported and explored through the p-value - Phi coefficient - express relationships between dichotomous variables - Point-biserial correlation -- expresses relationships **between** a nominal variable **and** an interval variable - Spearman's rho & Kendall's tau -- determine the degree of association between ranking variables **Statistical Analysis - Studying Complex Relationships [among More than Two Variables]** - **Multiple regression:** 1. One dependent variable 2. Multiple independent variables - Used to [determine what variables contribute to change in the dependent variable and to what degree] - **Types of multiple regression** a. Simple Regression b. Multiple Regression - Can be referred to as [forward], [backward] or [stepwise] solution **Statistical Analysis -- Confidence Intervals** - An estimated range of values that provides a measure of certainty about the sample findings - A confidence interval is [developed around a sample mean] and gives a range of values for the population mean as well as the probability of that value being right ([degree of confidence that the value is accurate and reflects the population)] - Most commonly [reported in research is a 95% degree of certainty], meaning 95% of the time, the findings will fall within the range of values given as the confidence interval. - [As sample size increases] the range of values will narrow around the mean and indicate that [the mean is more accurate as compared with a smaller sample] **Critiquing Data Analysis Approaches** - Critiquing Descriptive Statistics: - Are appropriate descriptive statistics used? - What level of measurement is used? - Is the sample size larger enough? - What descriptive statistics are reported? - Are these appropriate to the level of measurement used? - Are appropriate summary statistics provided for each major variable? - Critiquing Inferential Statistics: - Does the hypothesis reflect if differences or relationships are being tested? - Is the level of significance indicated? - Does the measurement level permit parametric testing? - Is the sample size large enough for parametric testing? - Is there enough information given to assess appropriateness of parametric use? - Do the statistics used match the problem, hypothesis, method, sample, and level of measurement? - Are hypothesis results clearly presented? - Do tables and graphs enhance text? - Are the results understandable? - Are practical and statistical significance distinguishable? **Important Consideration!** - Science and research prove nothing in isolation--- research evidence only provides support for a theory. - [One study's findings are rarely sufficient to support a major practice change.] - [Need to be critical of the results of a study] - Do they make sense? - Was the research conducted in a way that supports best practice? - If results seem too good to be true, then they usually are!  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