Research: Module 1

Evidence Informed Practice (EIP)

• EIP is a triad of best available research evidence, professional/clinical expertise, and client values/individualized needs.
• The better the research evidence, the more confident clinical decisions are.
• Evidence alone is insufficient to make clinical decisions.
• Following evidence-informed/evidence-based standards improves RMT effectiveness, insurance benefits, standardization, and reputation in the health care community.

Quantitative vs Qualitative Methods

• Quantitative methods assume a uniform reality and measurable data, expressing it in numbers.

• It assumes linear cause and effect.

• Quantitative methods test hypotheses and use numbers to summarize information.

• Researchers control the treatment setting and participants.

• The focus is on ruling out rival explanations and using statistics to show the probability of chance outcomes.

• Qualitative methods assume observations affect what's being observed; description varies; multiple factors affect outcomes.

• Qualitative research doesn't assume one single reality and uses multiple factors or causes for outcomes.

• Qualitative observations are done in natural settings, unlike controlled settings seen in quantitative approaches.

• Data collection includes interviews, observations, documents, questionnaires, and surveys

Quantitative vs Qualitative Methods (Continued)

• Both quantitative and qualitative methods have limitations, but are crucial for scientific research.
• Both methods assess the credibility of a study, its reliability, and validity.
• Depending on the research question, some studies use both approaches.
• Qualitative analysis aids in developing specific and quantifiable hypotheses.
• Quantitative statistics help to illuminate patterns in qualitative studies.
• Most health care research utilizes quantitative methods.

Descriptive vs Explanatory Studies

• Descriptive studies describe a group/sample/population without intentions of going beyond that specific group or segment.

• These studies don't test theories or learn more about the specific group.

• They set the stage for eventually testing a theory by first forming a hypothesis.

• Studies seeking generalized statements about a population based on a sample are explanatory/inferential studies.

• These look to see if the population benefits from something.

• Descriptive studies form hypotheses but include weaker evidence due to a lack of control or comparisons.

• However, these studies form a foundation for hypotheses based on observation.

• Explanatory studies test hypotheses and provide evidence about the relationships between variables or events.

• These provide a stronger evidence base by clarifying cause and effect relationships.

• Explanatory studies are divided into observational and experimental studies.

• Examples include case studies, case series, correlation studies, and qualitative studies.

Study Designs

• Study designs range from observational to experimental, progressing toward higher quality evidence.
• The pyramid illustrates study design quality with Meta-Analyses at the top, followed by systematic reviews, critically appraised literature, randomized controlled trials, and non-randomized controlled trials, cohort studies, case series, etc.

Types of Studies (continued)

• Meta-analysis: studies focused on a particular question grouped by criteria from multiple databases; identifies published articles; reduces selection bias, used to estimate the size of a treatment effect, and resolve contradictory/inconclusive data.

• Systematic review: a similar approach to meta-analysis but incorporates previously unpublished studies and aims to eliminate publication bias by including a wide range of evidence from diverse sources to determine inclusion and assess the validity.

• RCT (Randomized Controlled Trial): also known as randomized trials, clinical trials, or intervention studies. These studies provide direct evidence of cause-and-effect following treatment by randomly assigning participants to treatment or control groups.

• Cohort study: prospective, longitudinal, observational studies that attempt to explain the relationship between treatment and outcome. Cohort studies observe a group of individuals over time to explore a relationship.

• Before/After without Control: a type of case series where a practitioner measures an outcome; sets eligibility standards; gathers baseline data; intervenes; and measures the outcome to determine experimental case series. Weaknesses include lack of control groups and potential overestimation of treatment impact.

• Before/After with Control: similar to before/after without control but includes a control group making it more reliable.

• Case Report: describes events related to a single patient's experience to provide insight into events; helps propose hypotheses related to patient care and can be used to report adverse responses to treatment.

• Case Series: combines individual case studies of similar patients to further investigate patterns or phenomena.

• Anecdote: a brief description of a person or incident and can be useful as an initial observation or a trigger for creating a case report.

• Correlation Study: identifies existing data to study a population and determine if there's an association or correlation between exposure and outcome, yet it does not prove causality.

Components of a Research Article

• Abstract: a summary of the background, purpose, design, methods, results, conclusion, and discussion of a study.

• Introduction: details study purpose, importance, research questions, and literature review.

• Methods: describes the detailed study conduct, to allow replications. It's important to explain how the study was done, including all the processes, settings, and tools.

• Results/Findings: describes the analysis of study data (qualitative or quantitative), and neither supports nor dismisses the hypothesis or a research question.

• Conclusion/Discussion: answers the study question, explains the results, cites other research to help validate the results, and indicates the study's significance.

Hypothesis

• Hypothesis: a testable statement that can be demonstrated as true or false.
• Hypothesis is a suggestion on how things work, which is testable and measurable.
• Hypothesis will have a dependent variable, the one that is changed; independent variable, the thing being measured.
• The null hypothesis is the accepted assumption that there is no relationship between variables.
• Researchers try to disprove the null hypothesis.

Variables

• Independent Variable: the variable manipulated or changed in an experiment; it is the cause.
• Dependent Variable: the measured variable affected by the independent variable; it is the effect.
• Controlled Variable: variables kept constant and unchanged throughout the study.

Types of Experiment Designs

• By number of Independent variables: simple designs have one; complex designs have multiple
• By subject assignment: between-subjects (different subjects in each group); within-subjects (same subjects in all groups); mixed designs (a blend of both).

Class #2

• Statistics: essential for interpreting data
• Internal vs. External validity: addresses the study's methodology and generalizability to real-world settings
• Ethics and Peer review: crucial for study quality control
• Critiquing an article: evaluating research designs, methods, and interpretation of findings.