FINAL 2 Flashcards
A critical component of the research process that provides an in-depth analysis of recently published research findings in specifically identified areas of interest. The review informs the research question and guides development of the research plan.
Literature review
A traditional approach to research in which variables are identified and measured in a reliable and valid way.
Quantitative research
A naturalistic approach to research in which the focus is on understanding the meaning of an experience from the individual’s perspective.
Qualitative research
Study conducted by examining a single phenomenon across multiple populations at a single point in time w/ no intent for follow-up in the design.
Cross-sectional design
Strengths of cross-sectional designs
- practical and economical
- no waiting for the outcome of interest to occur
- enable the exploration of health conditions that are affected by human development
- procedures are reasonably simple to design and carry out
- data are collected at one point in time so results can be timely and relevant
- large samples are relatively inexpensive to obtain
- there is not loss of subjects due to attrition
Limitations of cross-sectional designs
- transitory nature of data collection makes causal association difficult
- don’t capture changes that occur as a result of environmental factors or other events that occur over time
- may be difficult to locate individuals at varying stages of a disease or condition
- impractical for the study of rare diseases or uncommon conditions
Study conducted by following subject’s over a period of time, with data collection occurring at prescribed intervals.
Longitudinal designs
Strengths of longitudinal designs
- can capture historical trends and explore causal associations
- cost-effective and cost-efficient
- can document that a causal factor precedes an outcome, strengthening hypotheses about causality
- provide the opportunity to measure characteristics and events accurately and do not rely on recall
Limitations of longitudinal designs
- attrition rates and potential loss of subjects over time are common
- dependent on accurate, complete secondary data or the subject’s ability to recall past events
- once begun, it cannot be changed w/out affecting the overall validity of the conclusions
- expensive to conduct and require time and commitment from both parties
- conclusions may be based on a limited number of observations
- large sample sizes are expensive to access
- systematic attrition of subjects is possible due to long-term commitment requirements
A design that involves the analysis of two variables to describe the strength and direction of the relationship between them.
Correlation study
Strengths of a correlation study
- relatively uncomplicated to plan and implement
- researcher flexibility in exploring relationships among 2 or more variables
- outcomes of correlation studies often have practical application in nursing practice
- provide a framework for examining relationships between variables that cannot be manipulated for practical/ethical reasons
Limitations of a correlation study
- researcher cannot manipulate variables of interest, so causality cannot be established
- correlation designs lack control and randomization between variables
- correlation measured may be the result of a suppressor value
- demonstration of a correlation is not evidence of anything other than a linear association btw 2 variables
A bell-shaped distribution in which the mean is set at 0 and a standard deviation of 1.
Standard normal distribution
The average; a measure of central tendency.
Mean
A measure of central tendency that is the exact midpoint of the numbers of a data set.
Median
A measure of central tendency that is the most frequently occurring value in the data set.
Mode
A measure of variability that is the distance between the two most extreme values in the data set.
Range
An outcome of interest that occurs after the introduction of an independent variable; the “effect” of cause and effect.
Dependent variable
A factor that is artificially introduced into a study explicitly to measure an expected effect; the “cause” of cause and effect.
Independent variable
Factors that exert an effect on the outcome but that are not part of the planned experiment and may confuse the interpretation of the results.
Extraneous variables
Quantitative analyses
- select tests a priori
- run all tests identified
- report all tests that were run
The error that arises from the sampling procedure; it is directly affected by variability and indirectly affected by sample size.
Standard error
Tells us the findings are real.
*When the p-value is very small, indicating that the probability the results were due to chance is also very small, then the test is said to have…
Statistical significance
Tells us if the results are important for practice.
*The extent to which an intervention can make a real difference in patient’ lives.
Clinical significance
The magnitude of the impact that the intervention or variable is expected to have on the outcome.
Effect size
A way that you can analyze more than 2 groups; helps reduce the risk of error.
Analysis of variance (ANOVA)
