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Related Concept Videos

Quantitative Analysis01:12

Quantitative Analysis

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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Review and Preview01:10

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Review and Preview01:13

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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Updated: Jan 11, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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Six suggestions for improving quantitative evaluations.

David Hemenway1

  • 1Harvard TH Chan School of Public Health Harvard Injury Control Research Center, Boston, Massachusetts, USA hemenway@hsph.harvard.edu.

Injury Prevention : Journal of the International Society for Child and Adolescent Injury Prevention
|November 11, 2025
PubMed
Summary
This summary is machine-generated.

Researchers should improve quantitative analysis by developing causal theories, verifying data accuracy, exploring datasets, disaggregating findings, assessing plausibility, and ensuring methodological transparency for robust public policy evaluation.

Keywords:
FirearmOutcome EvaluationPolicy analysis

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Area of Science:

  • Quantitative analysis
  • Econometrics
  • Public policy evaluation

Background:

  • Regression analysis is commonly used to evaluate public policy effects.
  • Many researchers begin regressions before fully understanding their data or research questions.
  • The firearms literature provides examples of these analytical challenges.

Purpose of the Study:

  • To offer practical suggestions for enhancing quantitative analyses.
  • To guide researchers in improving the rigor of regression-based studies.
  • To highlight common pitfalls in data analysis and interpretation.

Main Methods:

  • The author provides six key recommendations for researchers.
  • Suggestions include developing causal theories and investigating data accuracy.
  • Emphasis is placed on data exploration, disaggregation, plausibility checks, and transparency.

Main Results:

  • Adopting these suggestions can lead to more reliable and interpretable research findings.
  • The effectiveness of Child Access Prevention Laws serves as a case study.
  • Improved analytical practices enhance the understanding of policy impacts.

Conclusions:

  • Researchers should prioritize a deep understanding of data and theory before analysis.
  • Transparency in methods and results is crucial for scientific credibility.
  • Implementing these suggestions can strengthen the validity of quantitative research in public policy.