Quantitative Research Design PPT

Chapter 9 Quantitative Research Design Key Features of Quantitative Research Design: Interventions Key questions – Will there be an intervention? – What specific design will be used? Broad design options – Experimental (randomized control trial) – Quasi-experimental (...

Chapter 9 Quantitative Research Design Key Features of Quantitative Research Design: Interventions Key questions – Will there be an intervention? – What specific design will be used? Broad design options – Experimental (randomized control trial) – Quasi-experimental (controlled trial without randomization) – Nonexperimental (observational study) Copyright © 2019 Wolters Kluwer · All Rights Reserved Key Features of Quantitative Research Design: Comparisons Key question – What type of comparisons will be made to illuminate relationships? Some design options – Within-subjects design: Same people are compared at different times or under different conditions. – Between-subjects design: Different people are compared (e.g., men and women). Copyright © 2019 Wolters Kluwer · All Rights Reserved Other Key Features of Quantitative Research Design Control over confounding variables – How will confounding variables be controlled? – Which specific confounding variables will be controlled? – Randomization, crossover, homogeneity, matching, statistical control Copyright © 2019 Wolters Kluwer · All Rights Reserved Other Key Features of Quantitative Research Design—(cont.) Masking/blinding – From whom will critical information be withheld to avert bias? Time frames – How often will data be collected? – When, relative to other events, will data be collected? – Cross-sectional, longitudinal design Copyright © 2019 Wolters Kluwer · All Rights Reserved Other Key Features of Quantitative Research Design—(cont.) Relative timing – When will information on independent and dependent variables be collected—looking forward or backward in time? – Retrospective (case-control), prospective (cohort) Location – Where will the study take place? – Setting choice; single site versus multisite Copyright © 2019 Wolters Kluwer · All Rights Reserved Question Tell whether the following statement is True or False. An experimental research design involves a nonrandomized controlled trial. a.True b.False Copyright © 2019 Wolters Kluwer · All Rights Reserved Answer b. False Rationale: A quasi-experimental research design involves a controlled trial without randomization. Copyright © 2019 Wolters Kluwer · All Rights Reserved Causality Many (if not most) quantitative research questions are about causes and effects. Research questions that seek to illuminate causal relationships need to be addressed with appropriate designs. Copyright © 2019 Wolters Kluwer · All Rights Reserved The Counterfactual Model of Causality A counterfactual is what would have happened to the same people exposed to a “cause” if they simultaneously were not exposed to the cause. An effect represents the difference between what actually did happen when exposed to the cause and what would happen with the counterfactual condition. Copyright © 2019 Wolters Kluwer · All Rights Reserved Criteria for Causality Three key criteria for making causal inferences – Temporal: The cause must precede the effect in time. – Relationship: There must be a demonstrated association between the cause and the effect. – Confounder: The relationship between the presumed cause and effect cannot be explained by a third variable or confounder; another factor related to both the presumed cause and effect cannot be the “real” cause. Copyright © 2019 Wolters Kluwer · All Rights Reserved Additional Criteria for Causality Additional criterion in health research – Biologic plausibility: The causal relationship should be consistent with evidence from basic physiologic studies. Copyright © 2019 Wolters Kluwer · All Rights Reserved Research Questions and Research Design Different designs are appropriate for different questions. – Therapy, prognosis, etiology/harm, and description Experimental designs (RCTs) offer the strongest evidence of whether a cause (an intervention) results in an effect (a desired outcome). – That’s why they are high on evidence hierarchies for questions about causes and effects. Copyright © 2019 Wolters Kluwer · All Rights Reserved Experimental Design Intervention: The researcher does something to some subjects—introduces an intervention (or treatment). Control: The researcher introduces controls, including the use of a control group counterfactual. Randomization: The experimenter assigns participants to a control or experimental condition on a random basis. – The purpose is to make the groups equal with regard to all other factors except receipt of the intervention. Copyright © 2019 Wolters Kluwer · All Rights Reserved Question Which characteristic is a key criterion for causality? a.Cause occurring before the effect b.Third variable involved with the cause and effect c.No empirical relationship between the cause and effect d.Single-source evidence about the relationship Copyright © 2019 Wolters Kluwer · All Rights Reserved Answer a. Cause occurring before the effect Rationale: Three key criteria for causality include the following: – The cause must precede the effect in time. – There must be a demonstrated empirical relationship between the cause and effect. – The relationship cannot be explained by a third variable. An additional criterion is that evidence of the relationship should come from multiple sources. Copyright © 2019 Wolters Kluwer · All Rights Reserved Experimental Designs Posttest-only (or after-only) design – Outcome data collected only after the intervention – Symbolic representation: R X O R O – R = Randomization; X = Receipt of intervention; O = Observation/measurement of dependent variable Copyright © 2019 Wolters Kluwer · All Rights Reserved Experimental Designs—(cont.) Pretest–posttest (before–after) design – Outcome data collected both at baseline and after the intervention – Symbolic representation: R O X O R O O Copyright © 2019 Wolters Kluwer · All Rights Reserved Experimental Designs—(cont.) Crossover design – Subjects are exposed to 2+ conditions in random order. – Subjects serve as their own control. – Symbolic representation: R O XA O XB O R O XB O XA O Copyright © 2019 Wolters Kluwer · All Rights Reserved Experimental Condition Must be designed with sufficient intensity and duration that effects might reasonably be expected Researchers describe the intervention in formal protocols that stipulate exactly what the treatment is. Attention must be paid to intervention fidelity (or treatment fidelity), that is, whether the treatment as planned was actually delivered and received. Copyright © 2019 Wolters Kluwer · All Rights Reserved Control Group Conditions (Counterfactuals) No intervention is used; control group gets no treatment at all. “Usual care” or standard or normal procedures is used to treat patients. An alternative intervention is used (e.g., auditory vs. visual stimulation). A placebo or pseudointervention, presumed to have no therapeutic value, is used. Attention control condition and delayed treatment (wait- listed) Copyright © 2019 Wolters Kluwer · All Rights Reserved Control Group Conditions—(cont.) Attention control—extra attention but not the active ingredient of the intervention Delayed treatment (“wait-listed controls”)—the intervention is given at a later date. – Symbolic representation: R O X O O R O O X O Copyright © 2019 Wolters Kluwer · All Rights Reserved Advantages and Disadvantages of Experiments Advantages—most powerful for detecting cause and effect relationships Disadvantages—often not feasible or ethical, Hawthorne effect (knowledge of being in a study may cause people to change their behavior), often expensive Copyright © 2019 Wolters Kluwer · All Rights Reserved Question Tell whether the following statement is True or False. A true experiment requires that the researcher manipulate the independent variable by administering an experimental treatment (or intervention) to some subjects while withholding it from others. a.True b.False Copyright © 2019 Wolters Kluwer · All Rights Reserved Answer a. True Rationale: In a true experiment, the researcher manipulates or does something, usually an intervention or treatment, to some subjects and not to others. Copyright © 2019 Wolters Kluwer · All Rights Reserved Quasi-Experiments Involve an intervention but lack either randomization or control group Two main categories of quasi-experimental designs – Nonequivalent control group designs Those getting the intervention are compared with a nonrandomized comparison group. – Within-subjects designs One group is studied before and after the intervention. Copyright © 2019 Wolters Kluwer · All Rights Reserved Nonequivalent Control Group Designs If preintervention data are gathered, then the comparability of the experimental and comparison groups at the start of the study can be examined. – Nonequivalent control group pretest–posttest design – Symbolic representation: O1 X O2 O1 O2 Copyright © 2019 Wolters Kluwer · All Rights Reserved Nonequivalent Control Group Designs— (cont.) Without preintervention data, it is risky to assume the groups were similar at the outset. – Nonequivalent control group posttest only is much weaker. – Symbolic representation: X O1 O1 Copyright © 2019 Wolters Kluwer · All Rights Reserved Within-Subjects Quasi-Experiments One-group pretest–posttest designs typically yield extremely weak evidence of causal relationships. – Symbolic representation: O1 X O2 Time-series designs gather preintervention and postintervention data over a longer period. – Symbolic representation: O1 O2 O3 O4 X O5 O6 O7 O8 Copyright © 2019 Wolters Kluwer · All Rights Reserved Advantages and Disadvantages of Quasi- Experiments May be easier and more practical than true experiments but – They make it more difficult to infer causality. – Usually there are several alternative rival hypotheses for results. Copyright © 2019 Wolters Kluwer · All Rights Reserved Question Which design is considered a quasi-experimental research design? a.Pretest–posttest design b.Posttest-only design c.Crossover design d.Within-subjects design Copyright © 2019 Wolters Kluwer · All Rights Reserved Answer d. Within-subjects design Rationale: Quasi-experimental research designs include nonequivalent control group and within-subjects designs. The other research designs are used for experimental research. Copyright © 2019 Wolters Kluwer · All Rights Reserved Nonexperimental Studies If researchers do not intervene by controlling independent variable, the study is nonexperimental (observational). Not all independent variables (“causes”) of interest to nurse researchers can be experimentally manipulated. – For example, gender cannot ever be manipulated. – Smoking cannot ethically be manipulated. Copyright © 2019 Wolters Kluwer · All Rights Reserved Types of Nonexperimental Studies Correlational designs Cause-probing questions (e.g., prognosis or harm/etiology questions) for which manipulation is not possible are typically addressed with a correlational design. A correlation is an association between variables and can be detected through statistical analysis. Correlational studies are weaker than RCTs for cause- probing questions, but different designs offer varying degrees of supportive evidence. Copyright © 2019 Wolters Kluwer · All Rights Reserved Types of Nonexperimental Studies— (cont.) In a prospective correlational design, a potential cause in the present (e.g., experiencing vs. not experiencing a miscarriage) is linked to a hypothesized later outcome (e.g., depression 6 months later). This is called a cohort study by medical researchers. Prospective designs are stronger than retrospective designs in supporting causal inferences—but neither is as strong as experimental designs. Copyright © 2019 Wolters Kluwer · All Rights Reserved Retrospective Designs In a retrospective correlational design, an outcome in the present (e.g., depression) is linked to a hypothesized cause occurring in the past (e.g., having had a miscarriage). One retrospective design is a case–control design in which “cases” (e.g., those with lung cancer) are compared to “controls” (e.g., those without lung cancer) on prior potential causes (e.g., smoking habits). Copyright © 2019 Wolters Kluwer · All Rights Reserved Descriptive Research Not all research is cause probing. The purpose of descriptive studies is to observe, describe, and document aspects of a situation. Some research is descriptive (e.g., ascertaining the prevalence of a health problem). Other research is descriptive correlational—the purpose is to describe whether variables are related, without ascribing a cause-and-effect connection. Copyright © 2019 Wolters Kluwer · All Rights Reserved Advantages and Disadvantages of Nonexperimental Research Disadvantage: does not yield persuasive evidence for causal inferences – This is not a problem when the aim is description, but correlational studies are often undertaken to discover causes. Advantage: efficient way to collect large amounts of data when intervention and/or randomization is not possible Copyright © 2019 Wolters Kluwer · All Rights Reserved Time Dimension in Research Design Cross-sectional design—Data are collected at a single point in time. Longitudinal design—Data are collected two or more times over an extended period. – Follow-up studies Longitudinal designs are better at showing patterns of change and at clarifying whether a cause occurred before an effect (outcome). A challenge in longitudinal studies is attrition or the loss of participants over time. Copyright © 2019 Wolters Kluwer · All Rights Reserved Controlling the Study Context Controlling external factors (such as research context) – Achieving constancy of conditions – Control over environment, setting, time – Control over intervention via a formal protocol: intervention fidelity Copyright © 2019 Wolters Kluwer · All Rights Reserved Controlling Participant Factors Randomization – Subjects as own controls (crossover design) Homogeneity (restricting sample) Matching Statistical control (e.g., analysis of covariance) Copyright © 2019 Wolters Kluwer · All Rights Reserved Characteristics of Good Quantitative Research Design Statistical conclusion validity—the ability to detect true relationships statistically Internal validity—the extent to which it can be inferred that the independent variable caused or influenced the dependent variable External validity—the generalizability of the observed relationships across samples, settings, or time Construct validity—the degree to which key constructs are adequately captured in the study Copyright © 2019 Wolters Kluwer · All Rights Reserved Question Tell whether the following statement is True or False. Cross-sectional research designs are helpful in showing patterns of change. a.True b.False Copyright © 2019 Wolters Kluwer · All Rights Reserved Answer b. False Rationale: Longitudinal studies, in which data are collected two or more times over an extended period, are better at showing patterns of change than cross-sectional studies, which collect data at a single point in time. Copyright © 2019 Wolters Kluwer · All Rights Reserved Threats to Statistical Conclusion Validity Low statistical power (e.g., sample too small) Weakly defined “cause”—independent variable not powerful Unreliable implementation of a treatment—low intervention fidelity Copyright © 2019 Wolters Kluwer · All Rights Reserved Threats to Internal Validity Temporal ambiguity Selection threat—biases arising from preexisting differences between groups being compared – This is the single biggest threat to studies that do not use an experimental design. History threat—other events co-occurring with causal factor that could also affect outcomes Maturation threat—processes that result simply from the passage of time Mortality/attrition threat—differential loss of participants from different groups – Typically a threat in experimental studies Copyright © 2019 Wolters Kluwer · All Rights Reserved Threats to External Validity Inadequate sampling of study participants Unfortunately, enhancing internal validity can sometimes have adverse effects on external validity. Copyright © 2019 Wolters Kluwer · All Rights Reserved Threats to Construct Validity Is the intervention a good representation of the underlying construct? Is it the intervention or awareness of the intervention that resulted in benefits? Does the dependent variable really measure the intended constructs? Copyright © 2019 Wolters Kluwer · All Rights Reserved Time for a Break Copyright © 2019 Wolters Kluwer · All Rights Reserved  Chapter 10 Sampling and Data Collection in Quantitative Studies & Ethics Basic Sampling Concepts Population (“P” in PICO questions) – The entire group of interest based on eligibility criteria Sampling – Selection of a portion of the population (a sample) to represent the entire population Eligibility criteria – The characteristics that define the population Inclusion criteria Exclusion criteria Copyright © 2019 Wolters Kluwer · All Rights Reserved Basic Sampling Concepts—(cont.) Sampling bias: overrepresenting or underrepresenting population segment in terms of key characteristics Strata: subpopulations of a population (e.g., male/female) Target population: the entire population of interest Accessible population – The portion of the target population that is accessible to the researcher, from which a sample is drawn Copyright © 2019 Wolters Kluwer · All Rights Reserved Sampling Goal in Quantitative Research Representative sample – A sample whose key characteristics closely approximate those of the population—a sampling goal in quantitative research More easily achieved with: – Probability sampling – Homogeneous populations – Larger samples achieved through power analysis Copyright © 2019 Wolters Kluwer · All Rights Reserved Sampling Designs in Quantitative Studies Nonprobability sampling – Does not involve selection of elements at random; is rarely representative of the population Probability sampling – Involves random selection of elements: each element has an equal, independent chance of being selected. – Allows researchers to estimate the magnitude of sampling error (difference between population values and sample values) Copyright © 2019 Wolters Kluwer · All Rights Reserved Types of Nonprobability Sampling— Quantitative Research Convenience sampling: selecting the most conveniently available people as participants Quota sampling: identifying population strata and figuring out how many people are needed from each stratum Consecutive sampling: recruiting all people from an accessible population over a specific time interval Purposive sampling: handpicking sample members Copyright © 2019 Wolters Kluwer · All Rights Reserved Numbers and Percentages of Students in Strata of a Population, Convenience Sample, and Quota Sample Strata Population Convenience Quota Sample Sample Male 100 (20%) 5 (5%) 20 (20%) Female 400 (80%) 95 (95%) 80 (80%) Total 500 (100%) 100 (100%) 100 (100%) Copyright © 2019 Wolters Kluwer · All Rights Reserved Types of Probability Sampling Simple random sampling – Researchers establish a sampling frame—a list of population elements. Stratified random sampling – The population is first divided into two or more strata, from which elements are randomly selected. Systematic sampling – Involves the selection of every kth case from a list, such as every 10th person on a patient list Copyright © 2019 Wolters Kluwer · All Rights Reserved Sample Size The number of study participants in the final sample – Sample size adequacy is a key determinant of sample quality in quantitative research. – Sample size needs can and should be estimated through power analysis. – The risk of “getting it wrong” (statistical conclusion validity) increases when samples are too small. Copyright © 2019 Wolters Kluwer · All Rights Reserved Critiquing Sampling Plans: Considerations The type of sampling approach used (e.g., convenience, consecutive, random) The population and eligibility criteria for sample selection The sample size, with a rationale A description of the sample’s main characteristics (e.g., age, gender, clinical status, and so on) Copyright © 2019 Wolters Kluwer · All Rights Reserved Data Collection in Quantitative Research Basic decision is the use of: – New data, collected specifically for research purposes, or – Existing data Records (e.g., patient charts) Historical data Existing data set (secondary analysis) Copyright © 2019 Wolters Kluwer · All Rights Reserved Examples of Records, Documents, and Available Data Hospital records (e.g., nurses’ shift reports) School records (e.g., student absenteeism) Corporate records (e.g., health insurance choices) Letters, diaries, minutes of meetings, etc. Photographs Copyright © 2019 Wolters Kluwer · All Rights Reserved Major Types of Data Collection Methods Self-reports Patient-reported outcome Observation Biophysiologic measures Copyright © 2019 Wolters Kluwer · All Rights Reserved Overview of Data Collection and Sources Structure Quantifiability Objectivity Copyright © 2019 Wolters Kluwer · All Rights Reserved Structured Self-Reports Data are collected with a formal instrument. – Interview schedule Questions are prespecified but asked orally. Either face-to-face or by telephone – Questionnaire Questions prespecified in written form, to be self- administered by respondents Copyright © 2019 Wolters Kluwer · All Rights Reserved Types of Questions in a Structured Instrument Closed-ended (fixed alternative) questions – For example, “Within the past 6 months, were you ever a member of a fitness center or gym?” (yes/no) Open-ended questions – For example, “Why did you decide to join a fitness center or gym?” Copyright © 2019 Wolters Kluwer · All Rights Reserved Advantages of Questionnaires (Compared With Interviews) Questionnaires are less costly and are advantageous for geographically dispersed samples. Questionnaires offer the possibility of anonymity, which may be crucial in obtaining information about certain opinions or traits. Copyright © 2019 Wolters Kluwer · All Rights Reserved Advantages of Interviews (Compared With Questionnaires) Higher response rates Appropriate for more diverse audiences – Some people cannot fill out a questionnaire. Opportunities to clarify questions or to determine comprehension Opportunity to collect supplementary data through observation Copyright © 2019 Wolters Kluwer · All Rights Reserved Composite Psychosocial Scales Scale—a device that assigns a numeric score to people along a continuum – Used to make fine quantitative discriminations among people with different attitudes, perceptions, traits Likert scales—summated rating scales Summated rating scales (composite scales) Copyright © 2019 Wolters Kluwer · All Rights Reserved Likert Scales Consist of several declarative statements (items) expressing viewpoints Responses are on an agree/disagree continuum (usually five or seven response options). Responses to items are summed to compute a total scale score. Copyright © 2019 Wolters Kluwer · All Rights Reserved Visual Analog Scale (VAS) Used to measure subjective experiences (e.g., pain, nausea) Measurements are on a straight line measuring 100 mm. End points labeled as extreme limits of sensation Copyright © 2019 Wolters Kluwer · All Rights Reserved Example of a Visual Analog Scale Copyright © 2019 Wolters Kluwer · All Rights Reserved Response Set Biases Biases reflecting the tendency of some people to respond to items in characteristic ways, independently of item content Examples: – Social desirability response set bias – Extreme response set – Acquiescence response set (yea-sayers) Copyright © 2019 Wolters Kluwer · All Rights Reserved Evaluation of Self-Reports Strong on directness Allows access to information otherwise not available to researchers But can we be sure participants actually feel or act the way they say they do? Copyright © 2019 Wolters Kluwer · All Rights Reserved Observation Structured observation of prespecified behaviors – Involves the use of formal instruments and protocols that dictate what to observe, how long to observe it, and how to record the data Focus of observation Concealment Method of recording observations Copyright © 2019 Wolters Kluwer · All Rights Reserved Structured Observations Category systems  checklists – Formal systems for systematically recording the incidence or frequency of prespecified behaviors or events – Systems vary in their exhaustiveness. Exhaustive system: All behaviors of a specific type recorded, and each behavior are assigned to one mutually exclusive category. Nonexhaustive system: Specific behaviors, but not all behaviors, are recorded. Copyright © 2019 Wolters Kluwer · All Rights Reserved Rating Scales Ratings are on a descriptive continuum, typically bipolar. Ratings can occur: – At specific intervals – Upon the occurrence of certain events – After an observational session (global ratings) Copyright © 2019 Wolters Kluwer · All Rights Reserved Observational Sampling Time sampling—sampling of time intervals for observation – Examples: Random sampling of intervals of a given length Systematic sampling of intervals of a given length Event sampling—observation of integral events; requires researchers to either know when events will occur or wait for their occurrence. Copyright © 2019 Wolters Kluwer · All Rights Reserved Evaluation of Observational Methods Excellent method for capturing many clinical phenomena and behaviors Potential problem of reactivity when people are aware that they are being observed Risk of observational biases—factors that can interfere with objective observation – Observational biases probably cannot be eliminated, but they can be minimized through careful observer training and assessment. Copyright © 2019 Wolters Kluwer · All Rights Reserved Biophysiologic Measures In vivo measurements – Performed directly within or on living organisms (e.g., blood pressure measures) In vitro measurements – Performed outside the organism’s body (e.g., urinalysis) Copyright © 2019 Wolters Kluwer · All Rights Reserved Evaluation of Biophysiologic Measures Strong on accuracy, objectivity, validity, and precision May be cost-effective for nurse researchers But caution may be required for their use, and advanced skills may be needed for interpretation Copyright © 2019 Wolters Kluwer · All Rights Reserved Factors Affecting Data Quality in Quantitative Research Procedures used to collect the data Circumstances under which data were gathered Adequacy of instruments or scales used to measure constructs – Psychometric assessment evaluates the measure’s measurement properties. – Reliability: extent to which scores are free from measurement error Copyright © 2019 Wolters Kluwer · All Rights Reserved Data Quality in Quantitative Research: Validity Face validity: whether the instrument looks like it is measuring the target construct Content validity: the extent to which the instrument’s content adequately captures the construct Criterion validity: the extent to which the scores on a measure are a good reflection of a “gold standard” Construct validity: the degree to which evidence about a measure’s scores in relation to other variables supports the inference that the construct has been well represented Copyright © 2019 Wolters Kluwer · All Rights Reserved  Psychometric Tool Description Seattle Angina Questionnaire (SAQ) The SAQ is a well-established disease-specific measure of HRQL for patients with CAD. The SAQ consists of 19 items that quantify five clinically relevant domains: physical limitation (PL), chest pain or anginal stability (AS) and frequency (AF), treatment satisfaction (TS) and disease perception (DP); the AS and AF subscales in particular are most appropriate for this study as they are designed to capture classic ischemic- related angina, which we need to monitor during follow-up. Copyright © 2019 Wolters Kluwer · All Rights Reserved  SAQ The SAQ is scored by assigning each response an ordinal value and summing across items within each of the five subscales. Sub-scale scores are transformed (0-100) by subtracting the lowest score, dividing by the range of the scale, and multiplying by 100. Higher scores for each subscale indicate better functioning; no summary score is computed. Internal consistency reliability estimates for the five sub- scales were found to range from.58 to.80 on a sample of patients with stable angina (N=107) and alpha coefficients overall have ranged from 0.66 to 0.89. Construct validity of the SAQ has been demonstrated in a number of recent studies.139,142,143 Copyright © 2019 Wolters Kluwer · All Rights Reserved WHY ETHICAL CONSIDERATION IS IMPORTANT IN CONDUCTING RESEARCH? Copyright © 2019 Wolters Kluwer · All Rights Reserved Ethics in Research Respect for Persons – Treat individuals as autonomous agents. – Allow people to choose for themselves. – Give extra protection to those with limited autonomy. Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 86 Copyright © 2019 Wolters Kluwer · All Rights Reserved Principles Applied Respect: Informed consent, respect for privacy Beneficence: Sound research design, competent investigators, favorable risk– benefit ratio Justice: Equitable selection of participants Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 87 Copyright © 2019 Wolters Kluwer · All Rights Reserved Definition of Human participant A living individual about whom an investigator conducting research obtains – data through intervention or interaction with the individual, or – identifiable private information. Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 88 Copyright © 2019 Wolters Kluwer · All Rights Reserved Informed Consent What it is – Ongoing process of communications and mutual understanding – Shared responsibility for protection Elements of Informed Consent Form – Purpose of research – Expected duration for participant – Description of procedures, risk & benefit – Identification of experimental procedures Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 89 Copyright © 2019 Wolters Kluwer · All Rights Reserved Elements of Informed Consent Form Voluntary participation, withdrawal from study without penalty Confidentiality & Privacy Compensation for research-related injury Who can answer questions Who has access to records? Revelation of new findings Investigators’ name and their qualifications Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 90 Copyright © 2019 Wolters Kluwer · All Rights Reserved Research Ethics Boards (REBs) Review research projects and assess that ethical standards are met in relation to the protection of the rights of human participants 1. At least five members of various backgrounds to promote complete and adequate project review 2. Members qualified by virtue of expertise, experience, and reflect professional, gender, racial, and cultural diversity 3. Membership must include one member whose concerns are primarily nonscientific (lawyer, member of clergy, ethicist). Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 91 Copyright © 2019 Wolters Kluwer · All Rights Reserved  Research Ethics Boards (Cont.) 4. At least one member from outside the institution (community member) 5. REB members have mandatory training in scientific integrity and prevention of scientific misconduct, as do principal investigators of a research study and research team members. 6. REB is responsible for protecting participants from undue risk and loss of personal rights and dignity. Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 92 Copyright © 2019 Wolters Kluwer · All Rights Reserved REB Role Assessing recruitment: Is it fair? Evaluating inclusion and exclusion criteria Investigator–participant relationship Role of REB in study? Consent: Maximize autonomy Additional protections Assessing risk and benefit Assessing consent forms and process Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 93 Copyright © 2019 Wolters Kluwer · All Rights Reserved Recruitment Plans and materials must be reviewed by REB. Public service announcement or ads also reviewed. Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 94 Copyright © 2019 Wolters Kluwer · All Rights Reserved Full Board Review All members participate and review. All members participate in discussion and make comments. Decision is rendered by a majority of assembled quorum. No member has a conflict of interest. Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 95 Copyright © 2019 Wolters Kluwer · All Rights Reserved Special Considerations Vulnerable participants – Children – Prisoners – Mentally disabled persons – Economically disadvantaged – Educationally disadvantaged – Subtle vulnerability: language, culture, pregnancy, students, employees, substance abuse, health status Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 96 Copyright © 2019 Wolters Kluwer · All Rights Reserved CNA’s Code of Ethics, 2008 Promoting safe, compassionate, competent, and ethical care Promoting health and well-being Promoting and respecting informed decision making Preserving dignity Maintaining privacy and confidentiality Promoting justice Being accountable Copyright © 2018 Elsevier Canada, a division of Reed Elsevier Canada, Ltd. 97 Copyright © 2019 Wolters Kluwer · All Rights Reserved Critiquing Criteria Does the researcher indicate that the rights of participants have been ensured? 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