Dependent Samples

Definition

Dependent samples, also known as paired or matched samples, refer to a study design where the same individuals or subjects are measured or observed under two or more different conditions or at different time points. The key feature of dependent samples is that the observations within each pair or group are related or correlated, as they come from the same individuals.

5 Must Know Facts For Your Next Test

1. Dependent samples are often used in experimental designs where researchers want to control for individual differences and isolate the effect of the intervention or treatment.
2. The use of dependent samples can increase the statistical power of a study by reducing the error variance, as the paired observations are more closely related than independent samples.
3. Dependent samples are commonly used in pre-post test designs, where the same individuals are measured before and after an intervention or treatment.
4. Analyzing dependent samples requires specialized statistical tests, such as the paired t-test or repeated measures ANOVA, which account for the correlation between the paired observations.
5. The degree of correlation between the paired observations is an important factor in determining the appropriate statistical test and the interpretation of the results.

Review Questions

• Explain the key feature of dependent samples and how it differs from independent samples.
  • The key feature of dependent samples is that the observations within each pair or group are related or correlated, as they come from the same individuals. This is in contrast to independent samples, where the observations in each group are unrelated and come from different individuals. The use of dependent samples allows researchers to control for individual differences and isolate the effect of the intervention or treatment, which can increase the statistical power of the study.
• Describe the advantages of using dependent samples in experimental designs.
  • Using dependent samples in experimental designs offers several advantages: 1) It allows researchers to control for individual differences, as the same participants are measured under different conditions or at different time points. 2) It can increase the statistical power of the study by reducing the error variance, as the paired observations are more closely related than independent samples. 3) It is commonly used in pre-post test designs, where the same individuals are measured before and after an intervention or treatment, providing a more direct assessment of the intervention's effect.
• Analyze the statistical implications of using dependent samples and the appropriate tests for analyzing the data.
  • Analyzing data from dependent samples requires specialized statistical tests that account for the correlation between the paired observations. The most common tests used for dependent samples are the paired t-test, which compares the means of two related samples, and repeated measures ANOVA, which analyzes data from experiments where the same subjects are measured under different conditions or at multiple time points. The degree of correlation between the paired observations is an important factor in determining the appropriate statistical test and the interpretation of the results, as higher correlation can lead to increased statistical power and more precise estimates of the treatment effect.

Related terms

Paired t-test: A statistical test used to compare the means of two related or paired samples, where each observation in one sample is matched with a corresponding observation in the other sample.

Repeated Measures ANOVA: An analysis of variance (ANOVA) technique used to analyze data from experiments where the same subjects or participants are measured under different conditions or at multiple time points.

Correlation: A statistical measure that describes the strength and direction of the linear relationship between two variables, which is important in understanding the nature of dependent samples.