Our study is experimental, only one DV and one IV. The study has two groups (control and experimental group) What statistical analysis should we use?

Based on the description of your study, where you have one dependent variable (DV) and one independent variable (IV) with two groups (control and experimental), a suitable statistical analysis would be an independent samples t-test.

The independent samples t-test is used to compare the means of two groups on a continuous outcome variable. In your case, you would compare the mean scores on the DV between the control group and the experimental group.

Here's how the analysis would typically be conducted:

Formulate hypotheses: Start by formulating your research hypotheses. For example, you might hypothesize that the experimental group will have significantly different mean scores on the DV compared to the control group.

Data preparation: Collect and organize your data, ensuring that you have the DV scores for each participant grouped by their respective experimental condition (control vs. experimental).

Assumptions checking: Before proceeding with the t-test, it is important to check the assumptions. These include checking for normality of the DV scores within each group and equality of variances between the two groups. Normality can be assessed through visual inspection or using statistical tests such as the Shapiro-Wilk test. Equality of variances can be evaluated using tests like Levene's test. If assumptions are violated, you may need to consider alternative non-parametric tests such as the Mann-Whitney U test.

Perform the t-test: If the assumptions are met, you can conduct the independent samples t-test using your statistical software. The t-test will compare the means of the DV between the control and experimental groups and determine if the observed difference is statistically significant.

Interpret the results: Analyze the output from the t-test, paying attention to the t-value, degrees of freedom, and p-value. The t-value indicates the magnitude and direction of the difference between the groups. The p-value indicates the statistical significance of the difference. If the p-value is below the chosen significance level (e.g., p < 0.05), it suggests that the mean scores on the DV significantly differ between the control and experimental groups.

Report the findings: Summarize the results of your study, including the means and standard deviations for each group, the t-value, degrees of freedom, and the p-value. Interpret the findings in the context of your research hypotheses.

Remember to consider effect sizes, confidence intervals, and other relevant statistical measures to provide a comprehensive interpretation of the results.

Note that the independent samples t-test assumes independence between the participants in each group. If there are dependencies or repeated measures within the groups, alternative analyses like paired t-tests or repeated measures ANOVA may be more appropriate.

It is always recommended to consult with a statistician or data analysis expert to ensure the appropriate analysis is selected and conducted correctly for your specific research design and data.

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