Quantitative Design
When you are planning on a study, you always want to ensure that the evidence obtained enables you to address the research problem effectively, logically, and as unambiguously as possible. If you begin the investigation too early, without thinking critically about what information is required, the overall problem may not be adequately addressed and the validity of the study will be undermined. Research design integrates all elements needed for the study to maximize validity and balance feasibility prior to beginning the data collection. Our quantitative design planning includes descriptive and experimental designs to provide you a blueprint for the collection, measurement, and analysis of data.
Quantitative Sampling
What is it? A representative sample, or sampling, is a subset of the individuals that accurately reflects the larger population. Too large of a sample could be waste of time and costly, whereas too small of a sample may not capture the true effect or relationship among the variables of interests in the population. Therefore, within the process of sampling, it is crucial to determine a minimal sample size, one that represents a target population as well as perform sufficient power.
Power analysis is commonly conducted to determine the sample size prior to collecting the data. The minimum sample size calculation is related to power level, effect size, statistical analysis method, number of predictors or groups, and alpha level. The method used for sampling power calculations depends on the types of statistical analysis being performed and how you will collect the data (such as random, cluster, and stratified, etc.). To calculate the sample size, our consultants use tools such as G*Power, MedCalc, Stata, R, SAS, PASS, and SPSS (SamplePower).
Our consultants can help you with your sampling plan – deciding the most efficient sample size that meets sufficient statistical power, within a shorter time and at less cost.
How can we help you?
- Determine minimum sample size beforehand by running priori power analysis
- Calculate power and effect size after analyzing data using post hoc power analysis
- Design different types of sampling, including probability sampling and non-probability sampling
- Suggest and review inclusion and exclusion criteria for sampling
- Write sample size determination paragraph for proposal development
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Read MoreFAQ About Quantitative Sampling
The survey needs to be implemented with a group representative of the problem and purpose being pursued. Depending on the research design, you will likely want to survey people with no relation to the problem/purpose, some relation to the problem/purpose, and complete relation to the problem/purpose. Sample sizes can be determined by the represented sample population studied and the type of analysis pursued.
Determining the minimum sample is essential in order to apply findings to a particular target group. Additional sampling methods can help ensure the best chance of finding significant findings if they exist. You want to be sure you can defend your results being representative of your organization, school, state, program, etc.
Obtaining a representative sample depends on your awareness of your target audience. It is important to do some background research in order to accurate reflect the makeup of your group according to the appropriate demographic profiles of gender, age, experience, marital status, income, education level, race, religious affiliation, etc.
Conducting a power analysis based on desired analysis and expected effect size gives a more in depth estimate for the sample size needed for a project. An inadequately powered sample will not reveal significant differences for smaller effect sizes.
A representative sample that is sufficiently powered will provide the best opportunity to identify significant or meaningful findings if they do exist. Conversely, if proper measures are taken and there are no significant findings, that is also very meaningful.
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Peggy Ostrander, DNPc, APRN, FNP-C Plano, Texas