![]() Predictors include water temperature in degrees Celsius, altitude, and whether the wetland is a fen or a marsh. Odds ratios measure how many times bigger the odds of one outcome is for one value of an IV, compared to another value.įor example, let’s say you’re doing a logistic regression for a ecology study on whether or not a wetland in a certain area has been infected with a specific invasive plant. If you’re at all familiar with logistic regression, you’re also familiar with odds ratios. Most people mean standardized when they say “effect size statistic.” But both describe the magnitude and direction of the research findings. So a difference in two means and a regression coefficient are both effect size statistics and both are useful to report. Unstandardized statistics are still measured in the original units of the variables. People like correlation because the strength and direction of any two correlations can be compared, regardless of the units of the variables on which the correlation was measured. Standardized statistics have been stripped of all units of measurement.Ĭorrelation is a nice example. There are two types of effect size statistics– standardized and unstandardized. “…provide information about the magnitude and direction of the difference between two groups or the relationship between two variables.” This quotation by Joseph Durlak explains it nicely: ![]() So now what do you use? Types of Effect Size Statisticsįirst, it’s important to understand what effect size statistics are for and why they’re worth reporting. Many of the common effect size statistics, like eta-squared and Cohen’s d, can’t be calculated in a logistic regression model. Things get trickier, though, once you venture into other types of models. If you’re running an ANOVA, t-test, or linear regression model, it’s pretty straightforward which ones to report. Effect size statistics are expected by many journal editors these days. ![]()
0 Comments
Leave a Reply. |