research final review.docx

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health sciences
Hall- Fanshawe College

Research final review WEEK 10 Quantitative research:  Formal, objective, systematic process where numerical data is obtained  Describe, compare, predict a phenomenon  Descriptive- structured observations/survey, for phenomenon, situation or group  Exploratory- gain new insights, discover and increase knowledge about phenomenon (with little information on the topic)  Casual- experimenting to assess cause and effect Casual statements:  X precedes Y  X and Y are correlated  Everything else is controlled or eliminated. No extraneous variables impacting outcome  We never prove something. Gather evidence to support the claim Qualitative Quantitative Understanding Prediction Interview/observations Survey/questionnaires Discovering frameworks Existing frameworks (theoretical/conceptual) Theory generating Theory testing Textual Numerical Quality of informant>sample size Sampling/sample core issues for reliability Rigor: trustworthiness and authenticity Rigor: reliability and validity Subjective Objective Inductive Deductive May be included in the research Excluded from the research Research designs intro:  Directs research process (who, what, when, where and how)  Located in method section  Purpose: answer questions, understand biases, direct analysis, direct interpretation  Develop study to bridge gap for what is unknown  Literature review is crucial-ethical approval and funding Good designs-elements:  Objectively conceptualized- literature review reflect who what when where why  Accurate- answers research question accurately  Feasible- large samples to answer question within time and funding  Control- control potential bias to give valid results  Internally valid- results are believable  Externally valid- results are useful for other people/situations Quantitative research designs:  Experimental: o Randomized control trials o Quasi-experimental o Pre-experimental  Non-experimental o Descriptive (correlational, univariate) o Correlational (retrospective/prospective, natural, path analytical)  Models of analysis: parametric vs. non-parametric  Randomly assigned participants: experimental  No..  Control group/more than 1 measurement: quasi/pre-experimental  No.. non-experimental Experimental design:  Intervention that is controlled or delivered  There is an experimental and control group  There is random assignment to groups  * 1-3 true experimental  *1-2 quasi-experimental  *just 1 pre-experimental Randomization:  Random assignment to groups (internal validity issue)  Equal extraneous variables in both groups  Random selection from population to sample (external validity issue)  Equal extraneous variables in sample that are true for population Concept of variable:  Measurable characteristic that varies among subjects  Independent variable- interventions/presumed cause (salt intake)  Dependent variable- outcome/presumed effect (blood pressure)  Extraneous variables/covariate- alternative causes (exercise) Concept of control:  Decrease error and the influence of unwanted extraneous variables  Increase probability of accurate findings and reflect true relationship between IV and DV  Control group: not exposed to intervention, eventually all groups exposed to the same intervention Ways to control extraneous variables:  Use homogenous sample (same age)  Random assignment into groups (eliminate biases)  Minimize threats to internal validity  Use experimental design (manipulate independent variable)  Statistical manipulation Concept of setting:  Natural setting- uncontrolled, real life situation (not recommended) o Can influence results based on cause and effect  Partially controlled-manipulated/modified o Quiet, light room to influence learning partially  Highly controlled- artificial environment for the research (recommended) o Cause and effect strongly tested, results may vary in different setting Research validity:  Design o Internal o External  Instrument o Reliability o Validity  Statistical o Type 1 error (alpha)- false positive (mostly accurate, small amount by chance) o Type 2 error (beta)- false negative (reject relationship based on the stats) Internal validity:  Changes in the outcome (DV) due to changes in the exposure (IV)  Goal: rule out other expectation  Threats: o History- other concurrent events o Maturation- developmental change o Testing- pretest causes an effect o Instrumentation- reliability of measure o Mortality- subject drop out o Selection bias- poor selection of subjects, no randomization) External validity:  Are the findings generalizable to other populations/settings?  Goal: useful beyond the participants  Threats: o Selection effects- sample does not represent population of interest o Reactivity- natural reactions to being studied o Measurement effects- act of being tested affects the outcome o ** can exclude people based on the mean to not alter the results Study designs to control threats:  Experimental (RCT)- controls most, gold standard  Quasi-experimental- controls some  Non-experimental- may control some  Descriptive- may control some LEVEL SOURCE 1 Systematic review, meta-analysis, evidence based guideline from RCT 2 RCT 3 Controlled quasi-experimental 4 Non-experimental 5 Systematic review of descriptive 6 Descriptive 7 Expert opinion Design Threats t validity Strengths Limitations Experimental RCT Selection bias Establish Difficult to causality/casual implement History direction Generalizability Testing Control may be low Instrumentation Not always ethical Measurement effects Quasi- History Establish Unclear cause experimental causality/casual and effect Testing direction statement Instrumentation Control Difficult to implement Measurement effects Generalizability may be low Not always ethical Classic No randomization step= quasi-experimental design (non-equivalent control) Non control group= quasi-experimental design (one group, pre-test and post-test) Non- Cross-sectional Selection bias Fast No causal experimental association Recall others Less expensive Large # of participants Large # of variables Prospective Selection bias Time line VERY expensive History established Long term follow up needed Instrumentation Large # of participants Large loss to Testing follow up Large # of possible variables Retrospective Testing bias Fewer Difficult to find participants adequate control Selection bias group Large # of Selection effects variables Causation Correlation Time/temporality- cause must precede effect Statistical Makes sense biologically/plausibility Needs corroborating research Dose-response Association: General relationship between 2 random variables Consistent association Correlation: linear relationship between the random variables Strength of association Other quantitative studies:  Methodological- develop new tools/test existing ones in new populations  Meta-analysis- statistical: pool data from studies of same design  Secondary data analysis- descriptive: analysis or re-analysis of data for reason other than why it was collected Critiquing quantitative research:  Is the study design appropriate  Are the control issues discussed  Does the design reflect feasibility concerns  Does the design flow logically  What are the threats to internal validity/how were they controlled  What are the threats to external validity/how were they controlled WEEK 11 Terms:  Population- all possible subjects  Target population- all possible subjects who meet eligibility criteria  Sampling- sample selection  Sample- a subset of subjects  Element- one subject Sampling:  Process of selecting study participants  Quantitative: to produce a representative group of participants so results from the sample can be generalized to the population of interest Sample:  people (children, seniors, patients), places (hospitals, units, cities), time (season, am/pm shift)  heterogeneity- participants are diverse: wide age range, all types of cancer patients, dissimilarities of the sample group inhibits the researcher’s ability to interpret findings to make generalizations  homogeneous- participants are similar, all females, all same age, a group with limited variation in attributes or characteristics Threats to validity related to sampling:  selection bias- possible alternate explanation (internal)  attrition (mortality)- loss from groups (internal)  selection effects- sample does not represent population of interest (external) Types of sampling strategies:  probability (random) o equal and independent probability of selection o need complete frame o not very common  strategies: simple random, systematic, stratified random, cluster  non-probability o elements chosen non-randomly o common  strategies: convenience (accidental), quota, purposive, network/snowball, theoretical Simple random sampling (SRS) -need a sampling frame (often incomplete or out of date) -each element has an equal & independent probability of selection -uses a random number generator -is the bias for statistical inference Systematic sampling -need sampling frame -uses frame’s order to locate element (100 elements from frame 10, pick every 5 one) -faster than SRS -bias if frame arranged that coincides with sampling Stratified random sampling -need sampling frame -used if SRS would provide too few elements -divide sampling frame into strata than chose random sample from each -complicated and expensive but representative -proportional stratified: number chosen from each strata proportional to sampling frame Cluster sampling (multistage) -if sampling frame or each element is not available -uses stages of random sampling (select X cities, select X hospitals in city, select X RNs in hospital) -convenient, efficient but higher sampling error/bias possible -more complicated analysis Convenience sampling -use elements available at time and place of study -does not require sampling frame -can be fast and efficient but may not provide representative sample Quota sample -researcher sets quota for number of elements from specific strata -ensures adequate elements from strata but cannot ensure unbiased sample Controlling Sampling error:  random selection  random assignment to groups or matching  estimate sample size using power analysis  overestimate sample size to account for drop out Matching:  to reach pre-set sample size for groups  to ensure that important variables comparable in sample  often necessary for cluster sampling  harder to find elements/participants if matching-limits statistical analysis Sample size:  estimated a priori to determine number of elements required to demonstrate effect  too small=type 2 error or low power, non-significant result due to few observations  too large=unnecessary cost without added benefit of study results  critical piece of the protocol development  large sample size does not make up for non-representative sample Sample size calculations:  not required for pilot studies  enlist the help of an epidemiologist or statistician  formula is based on: o study design o number of study variables o sampling strategy o heterogeneity of measures (literature review) o precision required (treatment effect size) o data analysis techniques (statistical tests planned) o cost (time and money) Power:  ability to detect a difference in effect between study groups or subjects (probability of rejecting the null hypothesis)  not enough po
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