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What are some common categorical data analysis techniques used in healthcare research?

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Categorical data analysis is a branch of statistics that deals with data that can be grouped into discrete categories, such as gender, race, diagnosis, or treatment. Categorical data analysis is widely used in healthcare research to explore the associations, differences, and effects of various factors on health outcomes and behaviors. In this article, you will learn about some common categorical data analysis techniques used in healthcare research and how they can help you answer relevant research questions.

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1 Chi-square test

A chi-square test is a basic technique that allows you to compare the observed frequencies of categorical data with the expected frequencies under a null hypothesis. For example, you can use a chi-square test to check if there is a significant difference in the distribution of blood types among patients with and without a certain disease. A chi-square test can also be used to test the independence of two categorical variables, such as whether smoking status and lung cancer are related. A chi-square test is easy to perform and interpret, but it has some limitations, such as requiring a large sample size and not providing information about the direction and strength of the association.

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Eren Unlu

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Try to understand computation of chi-square test rather than applying it blindly. It is really intuitive and simple. Follow a tutorial and example, and keep learning about statistical hypothesis testing.

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2 Logistic regression

Logistic regression is a technique that allows you to model the relationship between one or more categorical predictor variables and a binary outcome variable, such as survival or death, response or non-response, or yes or no. For example, you can use logistic regression to estimate the odds of developing diabetes based on age, weight, and family history. Logistic regression can also be used to test the significance and effect size of each predictor variable on the outcome variable, as well as to adjust for confounding factors and interactions. Logistic regression is a flexible and powerful technique, but it requires some assumptions, such as linearity and independence of the predictor variables.

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3 ANOVA and ANCOVA

ANOVA and ANCOVA are techniques that allow you to compare the means of a continuous outcome variable across different levels of one or more categorical predictor variables, while controlling for other variables. For example, you can use ANOVA to compare the mean blood pressure among patients who received different types of medication, or ANCOVA to compare the mean blood pressure while adjusting for age and gender. ANOVA and ANCOVA can also be used to test the significance and effect size of each predictor variable on the outcome variable, as well as to detect interactions and contrasts. ANOVA and ANCOVA are widely used and easy to interpret, but they also require some assumptions, such as normality and homogeneity of variance.

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4 Cluster analysis

Cluster analysis is a technique that allows you to group categorical data into meaningful and homogeneous clusters based on their similarity or dissimilarity. For example, you can use cluster analysis to identify different subgroups of patients based on their symptoms, preferences, or outcomes. Cluster analysis can also be used to explore the patterns and characteristics of the clusters, as well as to validate or generate hypotheses. Cluster analysis is a useful and exploratory technique, but it also has some challenges, such as choosing the appropriate clustering method, determining the optimal number of clusters, and interpreting the results.

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5 Factor analysis

Factor analysis is a technique that allows you to reduce the dimensionality of categorical data by extracting the underlying factors or latent variables that explain the variability and correlation among the observed variables. For example, you can use factor analysis to identify the main dimensions of patient satisfaction based on a set of survey questions, or to measure the construct validity of a psychological test based on a set of items. Factor analysis can also be used to simplify and summarize the data, as well as to test hypotheses and theories. Factor analysis is a complex and sophisticated technique, but it also requires some assumptions, such as linearity and normality of the variables.

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Eren Unlu

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Another usage of factor analysis is to use factor scores directly as machine learning input features, as along with other features or solely. Of course, if you plan to mix them with the raw features, as it is a linear decomposition, you'll be introducing pure multicolinearity.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Eren Unlu

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One particular method of categorical analysis in healthcare when data is vastly multidimensional with complex interactions is the usage of machine learning algorithms (but ones that can handle categorical features inherently like catboost or neural nets with embeddings), even in a self-supervised/unsupervised setting. Then, using feature interpretence methods like shap values you can analyze the data more properly.

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What are some common categorical data analysis techniques used in healthcare research?

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2

3

4

5

6

1 Chi-square test

2 Logistic regression

3 ANOVA and ANCOVA

4 Cluster analysis

5 Factor analysis

6 Here’s what else to consider

Statistics

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What are some common categorical data analysis techniques used in healthcare research?

1

2

3

4

5

6

1 Chi-square test

2 Logistic regression

3 ANOVA and ANCOVA

4 Cluster analysis

5 Factor analysis

6 Here’s what else to consider

Statistics

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