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Statistics Guide for Research Grant Applicants

Series Springerbriefs in statistics Subject category Mathematical Physics and Mathematics Abstract This book serves as a practical guide to methods and statistics in medical research. It includes step-by-step instructions on using SPSS software for statistical analysis, as well as relevant examples to help those readers who are new to research in health and medical fields.

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Simple texts and diagrams are provided to help explain the concepts covered, and print screens for the statistical steps and the SPSS outputs are provided, together with interpretations and examples of how to report on findings. Brief Guidelines for Methods and Statistics in Medical Research offers a valuable quick reference guide for healthcare students and practitioners conducting research in health related fields, written in an accessible style.

Hereafter you can find the brief excerpt of main rules for reporting statistical data There is an excellent statement by the International Committee of Medical Journal Editors for reporting statistical analyses:.

Statistical Guidelines

When possible, quantify findings and present them with appropriate indicators of measurement error or uncertainty such as confidence intervals. Avoid relying solely on statistical hypothesis testing, such as P values, which fail to convey important information about effect size.

References for the design of the study and statistical methods should be to standard works when possible with pages stated. Define statistical terms, abbreviations, and most symbols. In general, this principle requires reporting the descriptive statistics from which other statistics are derived, such as the numerators and denominators of percentages, especially in risk, odds, and hazards ratios. Likewise, P values are not sufficient for re-analysis. Preliminary analyses: Check for normality of distribution of your data and identify any statistical procedures used to modify raw data before analysis.

Examples include mathematically transforming continuous measurements to make distributions closer to the normal distribution, creating ratios or other derived variables, and collapsing continuous data into categorical data or combining categories. For example: Unpaired or t-test for independent samples was used to compare continuous variable between groups with and without coronary heart disease. For simplicity, round as much as is reasonable. For example, mean age can often be rounded to the nearest year without compromising either the clinical or the statistical analysis. Check your school's graduation requirements as well as your psychology program's core requirements for more information.

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Sign up to get these answers, and more, delivered straight to your inbox. More in Student Resources. So why are statistics important in psychology? Statistics allow psychologists to:.

Organize Data: When dealing with an enormous amount of information, it is all too easy to become overwhelmed. Statistics allow psychologists to present data in ways that are easier to comprehend. Visual displays such as graphs, pie charts, frequency distributions , and scatterplots make it possible for researchers to get a better overview of the data and to look for patterns that they might otherwise miss.

Describe Data : Think about what happens when researchers collect a great deal of information about a group of people. The U. Census is a great example. Using statistics, we can accurately describe the information that has been gathered in a way that is easy to understand.

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Descriptive statistics provide a way to summarize what already exists in a given population, such as how many men and women there are, how many children there are, or how many people are currently employed. Make Inferences Based Upon Data: By using what's known as inferential statistics, researchers can infer things about a given sample or population. Psychologists use the data they have collected to test a hypothesis or a guess about what they predict will happen.

Using this type of statistical analysis, researchers can determine the likelihood that a hypothesis should be either accepted or rejected. Quite a bit actually. Was this page helpful? Thanks for your feedback! Sign Up.

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