How to Use Statistical Analysis Techniques in Health science Articles
You can use statistical analysis techniques in health science research to examine the relationship between variables, identify trends, or provide a summary of data characteristics. Over four (4) kinds of statistical techniques are useful in health science research. However, many researchers may not know appropriate statistical analysis techniques or how to use them in health science research.
In this article, we will see the top statistical analysis techniques to use in health science research. Vital information about the uses of these techniques are also in this guide to help you understand their relevance to health science studies. Take advantage of all the information provided to maximize the significance and impact of your article(s) to scientific knowledge.
How to Use Statistical Analysis Techniques in Health Science Articles
You must understand the research question(s) and available data to use statistical analysis techniques in health science articles. The research question(s) will determine to a great degree an appropriate statistical analysis technique for your health science article. You also need to:
- Define the study objectives,
- Identify all relevant variables,
- Screen dataset to check for missing variables, and
- Scan available data for accuracy and likely outliers (outcomes that deviate from others)
Understanding the research questions and data helps you choose an appropriate statistical analysis technique for your health science research.
1. Descriptive statistics
Many health science articles that involve analysis apply descriptive statistics to define the major characteristics of collected data.
Most measures used in descriptive statistics helps define essential statistics like the mean, median, and mode (measures of central tendency) of a dataset. It also helps define measures of dispersion like the range, standard deviation and variance of collected data.
2. Hypothesis testing
Hypothesis testing is a statistical analysis technique that helps assess the validity of a tentative statement. Most hypothesis tests can help researchers examine the differences or relationships between and among variable in health science studies.
Hypothesis tests start with the formulation of a null hypothesis (H0) that suggests no relationship or difference between variables. Next, a researcher will have to form an alternative hypothesis (H1) that suggests the presence of a relationship or difference between variables.
Researchers need to test a hypothesis with an appropriate statistic. Most popular statistic used for hypothesis testing include chi-square (c2) tests, analysis of variance, (ANOVA), t-tests, and correlation analysis.
The test statistic used helps researchers determine whether to accept the null or alternative hypothesis. Decision rules differ among test statistic used to assess the validity of a null or alternative hypothesis statement.
3. Regression analysis
Regression analysis examines the presence of a relationship between a dependent variable and at least one independent variable. This technique in health science research helps researchers quantify the association variables share. Two common forms of regression – linear and logistic – are applicable in health research.
Linear regression analysis is useful when potential relationships between variables are linear (not more than one possible outcome per variable). Logistic regression analysis is useful in health science research where outcomes per variable may be more than one.
4. Survival analysis
Survival analysis is useful when health science researchers need to examine data with a time-and-event template. Many survival analysis techniques are application to health science research that deals with mortality, disease progress per time leading to a specific event. Some of the most popular survival analysis methods include Cox proportional regressions and Kaplan-Meier curves.
Survival analysis helps researchers with valuable information about the likelihood of an event happening over time. It also assess the factors that could influence such outcomes as time progresses.
5. Meta-analysis
Health science researchers can conduct meta-analyses to combine information from different studies to provide unitary evidence based on multiple findings. Most meta-analyses are part of systematic reviews and require vast experience on a particular subject to conduct valid research.
Top 10 Software for Statistical Analysis in Health Science Articles
- R
- SAS
- SPSS
- Stata
- MATLAB
- Python
- JMP
- Excel
- Minitab
- GraphPad Prism
FAQs
Why is statistical analysis important in health science articles?
Statistical analysis is important in health science articles to allow researchers break down complex information and draw conclusions from findings they make. It also helps researchers highlight trends and relationships to make guided decisions to improve knowledge and boost advancements in the health sciences.
How do I choose the appropriate statistical analysis technique for my health science research?
You can choose an appropriate statistical technique by considering the research question, study design, and data characteristics.
Researchers should have a thorough understanding of all parameters and variables, data type, and unique statistical assumptions before choosing a statistical technique for health science research. You can consult a qualified statistician or refer to relevant text to choose a suitable technique for analysis.
What are some common statistical tests used in health science research?
Regression analysis, correlation analysis, analysis of variance (ANOVA), chi-square (c2) tests, meta-analysis, and survival analysis are common tests used in health science research. Authors can choose among these statistical tests based on the research question and data type.
How can I ensure the validity and reliability of my statistical analysis in health science articles?
You can ensure validity of statistical analysis by defining the research question(s) and objectives while ensuring data completeness. Also, you need to select appropriate statistical techniques for your analysis and ensure best practices.
What are some data visualization techniques that can be used in health science articles?
Box plots, heat maps, line/bar graphs, scatter plots, and histograms are popular visual aids used in health science articles. These visual aids can help authors explore and display trends, patterns, and relationships for easier communication and understanding of findings made.
Final Word
Using statistical techniques in health science research helps you explain complex concepts in clear detail. Several statistical techniques exist that can help you analyze data, get results, discuss your findings, and arrive at a valid conclusion.
Take advantage of all the information in this guide to choose an appropriate statistical technique for your health science articles. You can also consider recommended tools highlighted above to help you conduct statistical analysis for your research with ease.