Quantitative Insights
Quantitative research produces large sets of numeric data for accurate and repeatable analysis. It is crucial to convert data into great insights, to interpret data in a more meaningful matter, and to apply the analysis results into practice. Our quantitative insights services support you to take what the data shows and determine how to use the results for effective change.
Determining Meaningfulness
What is it? Meaningfulness refers to whether the difference or relationships shown in the results are of sufficient magnitude to influence daily life and practice. Typically, researchers report significant results statistically. However, statistical significance is not sufficient in many situations and it is not automatically equivalent to a meaningful or practical impact.
When you find out a statistical significance, is it large enough to care about? This is called effect size. Effect size could characterize the magnitude of an effect or a strength of a relationship regardless of sample size, therefore it is needed to explicitly interpret the practical meaningfulness of a result finding. You could also apply your expertise and knowledge to determine if the effect is big enough to be meaningful in the real world.
Our consultants can help you consider two key issues when delivering results: statistical significance and practical/clinical meaningfulness.
How can we help you?
- Write and summarize significant findings statistically
- Assess effect sizes to determine practical significance
- Evaluate instrument treatment sensitivity
- Relate statistical results to theory
- Integrate data results
- Develop conclusions
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Read MoreFAQ About Determining Meaningfulness
Certain patterns in responses warrant cases to be removed to protect the integrity of data. Cases should be flagged where respondents answer too quickly or slowly. Additionally, responses may need to be removed from participants who drop off after certain points, answer with limited to no variance or answer antithetical questions inconsistently.
Particular assumptions about the shape of the distribution, parameters, variance and other data set factors need to be met for specific types of analyses. Failing to meet assumption requirements will severely undermine analysis potential and relevance.
A systematic review begins with a research question or focus and uses previously conducted research to answer. A meta-analysis may be used as part of a systematic review as it is the process of combining results from numerous previous studies into a single estimate or effect size. Both approaches use a multitude of existing research to address new or particular research concerns.
Secondary data can suplement or replace certain parts of research that may be s
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, exp
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Peggy Ostrander, DNPc, APRN, FNP-C Plano, Texas