Can I still continue my research with pseudo rsquare 0.07?

A pseudo R-squared value of 0.07 suggests that your logistic regression model explains about 7% of the variance in the dependent variable. While this value might seem relatively low, whether you can continue your research depends on several factors:

Context and Purpose of the Study: Consider the context of your research and the goals of your study. In some fields or research questions, a pseudo R-squared of 0.07 might be acceptable, while in others, a higher explanatory power might be necessary.

Comparison to Previous Research: Compare your results to existing literature in your field. If your pseudo R-squared is in line with similar studies, it might be acceptable. However, if it is significantly lower, you may need to investigate why your model is explaining less variance.

Significance of Predictor Variables: Assess the significance of the predictor variables in your model. If some variables are not significant, it may be worth reconsidering your model and possibly exploring additional variables that could improve the explanatory power.

Model Assumptions and Diagnostics: Ensure that your logistic regression model meets the necessary assumptions. Check for issues such as multicollinearity, outliers, and adherence to the assumptions of logistic regression. Also, consider diagnostic tests to assess the goodness-of-fit of your model.

Sample Size: A larger sample size can improve the precision of your estimates and potentially increase the explanatory power of your model. If your sample size is small, increasing it might be beneficial.

Consider Alternative Models: Depending on the nature of your data and research question, you might explore alternative modeling techniques, such as different machine learning algorithms or non-linear models.

Consult with a Statistician or Mentor: If you have access to a statistician or a mentor with expertise in statistical modeling, it can be valuable to seek their advice. They may provide insights into potential improvements or alternative approaches.

Remember that statistical significance does not necessarily equate to practical significance. It's crucial to interpret your results in the context of your research question and the field you are studying. If your model is providing meaningful insights or contributes to the existing body of knowledge, it may still be worthwhile to continue your research, even with a modest pseudo R-squared.

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