On Wednesday we talked about why sample size matters. Today I want to focus on how the sampling method affects the data. Basically, there are two ways to obtain a sample. One way is to randomly select members of the population, and the other is to use whomever is available.
To understand the difference, I want you to imagine you are a member of a midwifery organization. That midwifery organization wants to learn something about its members — maybe they want to know how many families the average midwife works with each month. How is sampling method likely to affect the data the organization collects?
On Monday we talked about regression techniques and I explained that finding a statistically significant result would be difficult because of our sample size. Today we’ll explore how sample size affects the results you will get.
Like the other statistical techniques we’ve talked about, regression techniques allow us to examine the relationship between two variables. But the regression techniques go a step beyond the Chi-Square and T-Test because they allow us to examine the relationship of multiple variables. Unlike the previous tests, regression allows you to find the correlation of multiple variables at one time, and then see what portion of the variation is due to each individual variable. This allows for control of potentially confounding variables, and is helpful for supporting the presence or absence of causation.
In studies that use regression techniques you may read the terms dependent and independent variables. These terms describe the relationship of the variables in the regression equation. The independent variable stands on its own, and is considered to change on its own. The dependent variable is “dependent” because regression is analyzing the portion of change in the variable that responds to changes in the independent variables.
On Wednesday we used a T-Test to see if there was a difference in mean labor time between women who worked as a doula for income and those that worked as a doula for hobby. The next obvious question is, what is a T-Test and why did we choose to use it?
Remember back to last week when we talked about a chi-square? With a chi-square we were able to see if two groups differed on a characteristic that was a categorical variable. This means we could look for differences that are basically yes or no categories. For example, those who had gestational diabetes and those who did not or those with an intact perinium and those without. This works well for some things, but what if you wanted to know how big the difference was?
On Monday we talked about how p-values tell us the probability of obtaining the result if the null hypothesis (the status quo) is true. Today we turn our attention to the confidence interval and the additional information using a confidence interval provides.
There are two things you need to remember to make sense of a confidence interval. First, your sample provides an estimate of the true value. Second, if you took a different sample, you would get a different estimate. So what does this mean? Continue reading
We’ve talked about data and the descriptive statistics. We’ve talked about the measures we use and stratifying by populations and sub populations to assess disparities. We talked about the types of studies and last week we moved into our first statistical test of a hypothesis, the chi-square. Today we will talk about how we know the results of our statistical test are significant by using a p-value.
The p-value is the probability of obtaining the result if the null hypothesis is true. What level of a p-value is significant is actually arbitrary – it simply needs to be selected before the experiment begins. Most commonly researchers will use a p-value of 0.05 or less as significant. But what does this really mean? Continue reading
In this study, Pregnancy outcome in women with previous one cesarean section, the authors did many statistical comparisons of the data. One comparison is easily understood with a chi-square – does having a previous vaginal delivery increase the success rate of VBAC? Continue reading
When research presents risk, what is being presented is the number of people with the selected characteristic divided by the total number of people.
When research presents odds, what is being presented is the number of people with the selected characteristic divided by the number of people without the characteristic.
Visualize this difference by thinking of a set of dice. With six sides, each having a different number between 1-6, your risk of rolling a 1 is 1 divided by six which is 0.167. But your odds of rolling a 1 is 1 divided by 5 (the total number of non-one options) which is 0.20. Continue reading
I’ve seen bits and pieces of the Full 40 Campaign, but took the time to dig out all the materials after attending the AWHONN Conference last week. As usual, there are some things I like, and some I wonder about.
What do I like? AWHONN has produced some easy to share materials. You can find them at the Healthy Mom & Baby website. There is also a collection of social media images you can use. These were harder to find – I eventually had to copy them from the Facebook album to get them into Pinterest to share. Continue reading
We have been talking about statistics, and how to understand the statistics piece of a research article. Today we will start looking at the most common statistical tests used in health-care research. We will not talk about how to do these tests. Instead we will focus on how and why these tests are used. We will begin with the Chi-square.
Chi-square is used to analyze the difference in proportions for categorical data. If you remember, categorical data is information with discrete groups like race, sex, or the rating out of ten given to pain during pushing stage. A proportion is simply the number in the group who experienced something.