# Comparing the means of two or more groups

These methods will be the subject of the next review, along with a discussion of the relative merits of parametric and nonpara-metric approaches. Though the formulas are not strenuous - if you think about it the expressions are more like "puzzles" where you need to input numbers into their specific location - doing them by hand can be cumbersome.

In this case, the test statistic is defined by the two-sample t statistic. Another option is to estimate the degrees of freedom via a calculation from the data, which is the general method used by statistical software such as MINITAB.

The pooled SE for the Comparing the means of two or more groups in means is then as follows. For example, imagine you are conducting a weight loss study where you compare starting weight of your subjects to the end weight.

The hypothesized value is the null hypothesis that the difference between population means is 0.

If a subject provides two scores, then the scores are not independent. Female, Male the analysis will involve comparing two independent proportions. Because the sample sizes in the unpaired case may be and indeed usually are different, the combined SE takes this into account and gives more weight to the larger sample size because this is likely to be more reliable.

As for the previous two cases, a t statistic is calculated. The analysis of data with two scores per subject is shown in the section on the correlated t test later in this chapter.

Each value is sampled independently from each other value. The next step is to compute the estimate of the standard error of the statistic. Again, the larger the t statistic, the smaller the P value will be.

This requires an alternative approach that is known as analysis of variance ANOVAand will be the subject of a future review. The value 0 is not included in the interval, again indicating a significant difference at the 0.

In this case, our statistic is the difference between sample means and our hypothesized value is 0. That is, one could fail to reject a null hypothesis concluding that the diet did not result in a significant weight loss, where instead, if the proper alternative would have been selected a rejection would have taken place and the diet would have shown a significant weight loss.

The two populations have the same variance. We will start with comparing two independent population proportions, move to comparing two independent population means, from there to paired population means, and ending with the comparison of two independent population variances.

Graduate, Undergraduate the analysis will involve comparing two independent means. The Software is Only as Good as the User! For this calculation, we will make the three assumptions specified above. GPA and taken from two distinct groups e.

However, if you were to select the options of "Difference less than hypothesize difference" you would get a decision that conflicts with the the prior option.

This is referred to as univariate data.

The consequences of violating the first two assumptions are investigated in the simulation in the next section. What is important is whether there is a difference in the population means.

If conditions are satisfied, we calculate the specific test statistic and again compare this to a critical value rejection region approach or find the probability of observing this test statistic or one more extreme p-value approach.

Since we are assuming the two population variances are the same, we estimate this variance by averaging our two sample variances. However, the gender difference in this particular sample is not very important. Example The dataset "Normal Body Temperature, Gender, and Heart Rate" contains observations of body temperature, along with the gender of each individual and his or her heart rate.

In this lesson when comparing two proportions or two means, we will use a null value of 0 i. In the previous lesson the null value could vary. The one sample t-test requires that the data have an approximately Normal distribution, whereas the paired t-test requires that the distribution of the differences are approximately Normal.

A seemingly small mistake that has big consequences!! To explore the likely role of chance in explaining this difference, an unpaired t-test can be performed.

In the dataset, the first column gives body temperature and the second column gives the value "1" male or "2" female to describe the gender of each subject.In this lesson when comparing two proportions or two means, we will use a null value of 0 (i.e.

"no difference"). Although we can test for a specific difference, for example does the diet result in an average weight loss of more than 10 pounds. CRJ Chapter 9 – Comparing Groups The Existence, Strength, and Direction of an Association Chapter 9: Comparing Means Prof.

Kaci Page 2 of 9 Chapter 9/1: Comparing Two or more than Two Groups Cross tabulation is a useful way of exploring the relationship between variables that contain only a few.

Jul 12,  · Statistics review 5: Comparison of means. The present review covers the specific case of comparing means in rather more detail.

Comparison of means arises in many different formats, and there are various methods available for dealing with each of these. where SD 1 and SD 2 are the SDs in the two groups and n 1 and n 2 are the.

I Comparing means between groups is an important method for identifying discrimination and other social problems. Hypothesis Test for the Di erence Between Two Means The null hypothesis is that the di erence is some amount d0 Comparing means with Stata In fact, we can now test a null of equality.

where x̅ i and x̅ j are the two sample means, n i and n j are the two sample sizes, MS W is the within-groups mean square from the ANOVA table, and q is the critical value of the studentized range for α, the number of treatments or samples r, and the within-groups degrees of freedom df W.

Choose and run SPSS analysis procedures for comparing means of interval data. Created for students taking ALALALAL at GSU and other SPSS beginners Choose The Right Procedure Based On The Design Of Your Study One factor two levels: One factor 2/more levels: 2/more factors: Between-subject design.

Comparing the means of two or more groups
Rated 4/5 based on 94 review