Introducing
Your new presentation assistant.
Refine, enhance, and tailor your content, source relevant images, and edit visuals quicker than ever before.
Trending searches
Quantitative Research
24
Quantitative Research
Presented by
MEREDITH CROWDER MSN, RN
Quantitative
Research
Quantitative
- Concerned with the measurement and the relationship of variables
Types of Variables
Variables
Independent:
- Those that are constant and do not vary within the study.
- Controlled by the Researcher
Dependent:
- Varies
- Measured by the researcher
Research Design
- Plan or strategy for the researcher to answer the question and test hypothesis
- See pg. 138 Table 9.1
Research Designs
Types of Designs
- Experimental Design: Randomized Controlled Trial (RCT)
- Quasi Experimental Design
- Non-experimental Studies (correlation research)
- Cross-Sectional Designs
- Longitudinal Designs
Designs
Characteristics of Good Quantitative Research Design
#4
#3
#1
#2
What makes a good design?
Internal Validity
External Validity
Construct Validity
Statistical Validity
Data Collection
& Sampling
Sampling
- Defined population (from PICO/PIO)
- Target vs. Accessible
- Sample - subset of population elements
- Ensure sample is representative of the population
Non-probability vs. Probability
Sampling Designs
Probability Sampling
- Random selection, all elements have equal chance of selection
- Examples include:
- Simple Random Sampling
- Stratified Random Sampling
- Systemic Sampling
Non-probability Sampling
- Less likely to produce representative sample
- Examples include:
- Convenience Sampling
- Quota Sampling
- Consecutive Sampling
- Puposive Sampling
Evaluation of Sampling
Evaluation
*Probability Sampling is the only viable method of obtaining representative samples*
Non-probability sampling can lead to sample bias and under or over representation of the population
Data Collection
Data
Collection
- Data collection methods vary upon needs of the researcher
- Existing Data vs. New Data
- Types of Data Collection:
- Self Reports/Patient-Reported Outcomes
- Observational Methods
- Biophysiologic Measures
Data Quality
Reliability - the extent to which scores are free from measurement error
Reliability = Consistency
Validity - the degree to which an instrument is measuring the construct it purports to measure
Interpretation
Interpretation
When reading quantitative articles:
- Statistical Data is summarized in Results section
- The researchers Interpretation of the results is summarized in the Discussion section
Aspects of Interpretation
Appraising Evidence
Box 2.1 (pg. 32) includes 6 questions to consider when appraising the evidence
1. Credibility and accuracy
2. Precision of the estimate of effects
3. Magnitude of effects
4. Meaning of the results
5. Generalizability of the results
6. Implications of the results for nursing practice, theory, and further research
Pg. 32 Box 2.1
Critiquing
Critiquing
- Use table 4.1 on pg. 66 as your guide for critiquing Quantitative Research
- Inference - means drawing conclusions with limited data
- Interpretation requires multiple inferences
- Assess whether the evidence is right, and be on the lookout for potential biases
- Assess for Clinical Significance
Pg. 66 Table 4.1
The Great Donut Debate
Lets Practice!
https://www.surveymonkey.com/r/WNBTZB7