Inhoudstafel eng - Nursing Research, International Edition PDF

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C4: Research problems, questions and hypotheses Overview of research problems Basic terminology 1. Research problem 2. Problem statement 3. Research question 4. Hypotheses 5. Statement of purpose 6. Research aims/objectives Research problems and paradigms Sources of research problems 1. Clinical experience 2. Quality improvement efforts 3. Nursing literature 4. Social issues 5. Theories 6. Ideas from external sources Developing and refining research problems Selecting a topic Narrowing the topic Evaluating research problems 1. Significance 2. Researchability 3. Feasibility 4. Researcher interest Communicating research problems and questions Statement of purpose 1. Population/Problem/Patient 2. Intervention 3. Control 4. Outcome Research questions Variables 1) Independent variable (IV) 2) Dependent variable (DV) 3) Moderator variable 4) Mediating variable Problem statements 1. Problem identification 2. Background 3. Scope of the problem 4. Consequences 5. Knowledge gaps 6. Proposed solution

Research hypotheses Function in quantitative research Characteristics of testable hypotheses Derivation of hypotheses 1) Inductive hypotheses 2) Deductive hypotheses Wording of hypotheses 1. Population 2. Clearly 3. Concisely 4. Present time Types of hypotheses 1) Directional hypotheses 2) Nondirectional hypotheses 3) Research hypotheses 4) Null hypotheses Hypotheses testing and proof 1. Accepted/supported (never proven) 2. Rejected

C7: Ethics Ethics and research Codes of ethics Government regulations for protecting study participants 1. Beneficence 2. Respect for human dignity 3. Justice Ethical dilemmas in conducting research Ethical principles for protecting study participants Beneficence 1. Right to freedom from harm and discomfort 2. Right to protection from exploitation Respect for human dignity 1. Right to self-determination 2. Right to full disclosure Justice 1. Distributive justice 2. Right to fair treatment 3. Right to privacy Procedures for protecting study participants Risk/benefit assessments Informed consent and participant authorization Content of informed consent 1. Participant status 2. Study goals 3. Type of data 4. Procedures 5. Nature of commitment 6. Sponsorship 7. Participant selection 8. Potential risks 9. Potential benefits 10. Alternatives 11. Compensation 12. Confidentiality pledge 13. Voluntary consent 14. Right to withdraw and withhold information 15. Contact information Comprehension of informed consent Documentation of informed consent 1) Consent form 2) Short form 3) Implied consent Authorization to access private health information Confidentiality procedures Anonymity Confidentiality in the absence of anonymity Certificates of confidentiality (US) Sensitive information Debriefing, communications and referrals

Treatment of vulnerable groups External reviews and the protection of human rights 1) Institutional review boards (IRB) (US) 2) Data and safety monitoring boards Building ethics into the design of the study Other ethical issues Ethical issues in using animals in research Research misconduct

C9: Quantitative research design General design issues Causality Research question categories 1. Therapy/treatment 2. Prognosis 3. Causation/etiology/harm Criteria for causality 1. Temporal 2. Relationship 3. No confounding 4. Coherence 5. Consistency 6. Biological plausibility Effect: counterfactual model Experimental design Randomized controlled trial 1. Manipulation: experimental intervention/treatment 1) Identical intervention 2) tailored/patient-centered intervention 2. Control condition/group/arm 3. Randomization Randomization variants 1) Stratified randomization 2) Permuted block randomization 3) Urn randomization 4) Randomized consent 5) Partial randomization/partially randomized patient preference (PRPP) 6) Cluster randomization Blinding/masking 1) Open 2) Closed a. Single-blind b. Double-blind Specific experimental designs Basic experimental designs 1) Pretest-posttest design/before-after design 2) Posttest-only design/after-only design 3) Multiple intervention design 4) Wait-list design Specific experimental designs 1) Factorial design 2) Crossover design Strengths and limitations

Quasi-experiments Quasi-experimental designs 1) Nonequivalent control group designs (without randomization) a. Pretest-posttest design b. Posttest-only design Matching 1. Conventional matching 2. Propensity matching 3. Historical comparison group 2) Time series designs 1. One-group pretest-posttest design 2. Time series design Variations 1. Time series with withdrawing and reinstitution of treatment 2. Single-subject experiments 3. Time series nonequivalent control group design 3) Other quasi-experimental designs 1. Partially randomized patient preference 2. Quasi-experimental dose-response analysis Quasi-experimental and comparison conditions Strengths and limitations Nonexperimental/observational research Nonexperimental designs 1) Correlation designs 2) Descriptive designs Correlational cause-probing research 1) Retrospective designs (present  past) 1. Case-control design 2) Prospective non-experimental design/prospective cohort design (present  future) 3) Natural experiments 4) Path analytic studies Descriptive research 1) Descriptive correlational studies 2) Univariate descriptive studies Strengths and limitations of correlational research Designs and research evidence Evidence for nursing practice 1. Descriptive research 2. Correlational research 3. Experimental research Hierarchy of designs for different cause-probing research questions Examples

C10: Rigor and validit y Validity and inference Validity and inference 1) Statistical conclusion validity 2) Internal validity 3) Construct validity 4) External validity Controlling confounders 1. Randomization 2. Crossover 3. Homogeneity 4. Stratification/blocking 5. Matching 6. Statistical control Statistical conclusion validity Threats 1. Low statistical power 2. Restriction of range of values on the outcome 3. Unreliable implementation of treatment Internal validity Threats 1. Temporal ambiguity 2. Selection (bias) 3. History (bias) 4. Maturation/(maturity bias) 5. Mortality/attrition (bias) 6. Testing and instrumentation Internal validity and data-analysis 1. Selection bias 2. Attrition bias 3. Ordering bias Construct validity Enhancing 1. 2. 3. Threats 1. 2. 3. 4. 5.

Theoretical conceptualization and explanation of construct of interest Operationalization Assess match Reactivity: Hawthorne effect Researcher expectancies Novelty effects Compensatory effects Treatment diffusion of contamination

External validity Enhancing 1. Representativeness 2. Replication Multisite studies Diverse sample containing subgroups Systematic reviews 3. Real-world circumstances Threats 1. People 2. Treatment variation Trade-offs and priorities in study validity Approaches to conflict between internal and external validity 1. Emphasize one 2. Phased series of studies a. Efficacy studies b. Effectiveness studies 3. Compromise Research example

C26: Basics of mixed methods research Short history Mixed methods research (MMR) Mixed methods research Multi methods research Goals mixed methods research 1. Triangulation 2. Complementarity 3. Development 4. Initiation 5. Expansion Typology Classification systems 1. Weighting (W) 2. Mixing (M) 3. Timing (T) Designs 1) Triangulation design 2) Embedded design 3) Explanatory design 4) Exploratory design Research 1) Partially mixed methods research 2) Fully mixed methods research Convergent design/triangulation design Characteristics 1. Quan + Qual (M) 2. Same time (T) 3. Same weighting (W) Explanatory sequential design Characteristics 1. Quan  Qual (T) 2. Focus on Quan (W) 3. Connection between two phases (M) Exploratory sequential design Characteristics 1. Qual  Quan (T) 2. Focus on Qual (W) 3. Connection between two phases Sampling in mixed methods research Dependent on relationship 1) Identical relationship 2) Parallel relationship 3) Nested relationship 4) Multilevel relationship

Strengths and limitations C27: developing complex nursing interventions using mixed methods research Role in development and evaluation of complex interventions Intervention design model

C11: Specific types of quantitative research Clinical trials Phases of a clinical trial 1. Developing the best possible treatment 2. Polit test 3. RCTC 4. Effectiveness study Noninferiority and equivalence trials Types of trials 1) Superiority trial 2) Noninferiority trial 3) Equivalence trial Practical clinical trials Evaluation research Types 1) Process/implementation analysis 2) Outcome analysis 3) Impact analysis 4) Cost analysis a. Cost-benefit analysis b. Cost-effectiveness analysis c. Cost-utility analysis Health services and outcomes research Health services and outcomes research 1. Health services research 2. Outcomes research a. Evaluation research b. Outcomes research Model of health care quality 1. Structures 2. Processes 3. Outcomes Survey research Survey research 1. Personal/face-to-face interviews 2. Telephone interviews 3. Questionnaires 4. New technologies a. Computer-assisted personal interviewing (CAPI) b. Computer-assisted telephone interviewing (CATI) c. Computer-assisted self-interview (audio-CASI) Other types of research 1) Secondary analysis 2) Needs assessments 3) Delphi surveys 4) Replication studies 5) Methodologic studies 6) Quality improvement (QI) projects

C12: Sampling Introduction Sampling plan 1. Target population 2. Accessible population 3. Sampling frame 4. Sample Basic sampling concepts Population 1) Target population 2) Accessible population Eligibility criteria 1) Inclusion criteria 2) Exclusion criteria Samples and sampling 1) Probability sampling 2) Non-probability sampling Samples and sampling Terms 1. Strata 2. Staged sampling 3. Sampling bias Nonprobability sampling 1) Convenience sampling 2) Quota sampling 3) Consecutive sampling 4) Purposive sampling/ judgemental sampling Probability sampling 1) Simple random sampling 2) Stratified random sampling 3) Multistage cluster sampling 4) Systematic sampling Sampling size in quantitative studies Sample size basics Factors affecting sample size requirements in quantitative research 1. Effect size 2. Homogeneity 3. Cooperation and attrition 4. Subgroup analysis Statistical decision making 1) Type I error (false positive) 2) Type II error (false negative) Power analysis Components 1. Significance criterion 2. Sample size 3. Effect size 4. Power

Epidemiology: basic concepts and definitions Introduction Introduction Definition Historical example Factors of interest 1) personal factors 2) environmental factors 3) time factors Illustration: mapping causal chain of IHD Illustration: mapping relative contributions Illustration: epidemiological transition Measures of disease frequency Prevalence of a disease 1. Prevalence 2. Prevalence rate Illustration: population at risk Illustration: childhood obesity Illustration: HIV and AIDS Prevalence of a disease Factors influencing prevalence: increased and decreased by Incidence of a disease 1. Incidence 2. Incidence rate 3. Cumulative incidence Person-time at risk Illustration: incidence rates Measures of frequency Differences between incidence and prevalence Measures of frequency: mortality 1. Mortality 2. Mortality rate 3. Total mortality – cause-specific mortality 4. Proportionate mortality 5. Case-fatality rate Measures of frequency: life expectancy 1. Life expectancy 2. Global life expectancy rate Measures of frequency: life expectancy Measures combining mortality and morbidity/disability 1. QALY 2. DALY 3. HALE 4. DFLE Measures of frequency: example Illustration: prevalence rates Illustration: Incidence rates Illustration: Cause-specific mortality rate Illustration: Mortality rates for subgroups Illustration: Infant mortality rate Illustration: Incidence and mortality

Illustration: Life expectancy Illustration: Non-communicable diseases Standardization Frequency measures: standardization Illustration: standardization Illustration: standardization Measures of association Types of epidemiological study 1) Cohort studies 1. relative risk (RR) 2. attributable risk in (exposed) individuals (Ar e) 3. population attributable risk/attributable risk for the population (PAP/ARp) Cohort studies Relation between RR-PE-ARP Illustration: relative risk Illustration: population attributable risk 2) Case-control studies 1. Odds ratio (OR) Case-control studies Study design and causation Considerations for causation Study designs: Strengths and limitations Causation: risk function 1. Individual level 2. Population level Risk function: interaction 1. Synergistic interaction 2. Multiplicative interaction

C13: Data collection Developing a data collection plan Steps 1. Identifying data needs 2. Selecting types of measure 3. Selecting and developing instruments 4. Pretesting the data collection package 5. Developing date collection forms and procedures Structured self-report instruments 1. Questionnaire (written) 2. Interview schedule (oral) Types of structured questions 1) open-ended questions 2) closed-ended questions Specific types of closed-ended questions 1) dichotomous question 2) multiple-choice question 3) rank-order question 4) forced-choice question 5) rating question 6) checklist 7) visual analog scale (VAS) 8) collecting event history date (calendar/diary) Composite scales and other structured self-reports 1) Likert-type summated rating scales 2) cognitive and neuropsychological tests 1. cognitive test a. intelligence tests b. aptitude test 2. neuropsychological tests 3) other types of structured self-reports 1. semantic differential (SD) scale 2. Q sorts 3. vignettes 4. ecologic momentary assessments (EMA) Questionnaires versus interviews Designing structured self-report instruments Tips for wording questions Considerations 1. Clarity 2. Ability of respondents to give information 3. Minimize risk of response bias 4. Sensitivity Tips for preparing response options Tips for formatting an instrument Administering structured self-report instruments Collecting interview data Collecting questionnaire data In person distribution Mail (or postal) Internet

Evaluation structured self-reports Response set bias Structured observation Structured observation Observational sampling Time sampling Event sampling Biophysiological measures 1) in vivo measurements 2) in vitro measurements

C14: Measurement and data quality Measurement Measurement Theories of measurement Errors of measurement Sources of error/bias 1. Transient personal factors 2. Situational contaminants 3. Response-set bias 4. Administration variations 5. Instrument clarity 6. Item sampling Major types of measure 1. Reflective scales 2. Formative indexes Measurement properties: an overview Measurement taxonomy Cosmin-polit-yang taxonomy 1) Scores (cross-sectional) 1. Reliability domain 2. Validity domain 2) Change scores (longitudinal) 1. Reliability of change domain 2. Responsiveness domain Correlation coefficient Reliability Reliability Types/approaches 1) Test-retest reliability 2) Interrater/interobserver reliability 3) Intrarater/interobserver reliability 4) Parallel test reliability Interpretation of reliability coefficients Factors affecting reliability 1. Observer training 2. Sample heterogeneity 3. Population-situation Internal consistency

Validity Validity 1) Content validity 2) Face validity 3) Criterion validity 1. Concurrent validity 2. Predictive validity Continuous measure + continuous criterion Dichotomous measure + dichotomous criterion Diagnostic accuracy 1. True positive 2. Tue negative Predictive values 1. Positive predictive value 2. Negative predictive value Likelihood ratio (LR) 1. LR+ 2. LRContinuous measure + dichotomous criterion 1. Receiver operating characteristic curve (ROC) 2. Area under curve (AUC) 4) Construct validity Components 1. Hypothesis testing validity a. convergent validity b. known groups (discriminative) validity c. divergent (discriminant) validity 2. Structural validity 3. Cross-cultural validity Reliability of change scores Measure change Responsiveness Responsiveness...