An initial path towards statistical analysis

This interactive page allows you to find the best statistical method to analyze your given dataset.
Go through the images step by step, click on the answers that apply to your data, and let the page guide you.

Start here with your data! This is your first question.

How do I know?

• Inspect your data with str or summary. Are there several variables? An example for this can be found here: Inspecting the swiss dataset
• What does the data show? Does the underlying logic of the data suggest dependencies between the variables?

Univariate statistics

You are dealing with Univariate Statistics. This means that... You either have only one variable, or the variables are dependent on each other. But what kind of variables do you have?

How do I know?

• Check the entry on Data formats to understand the difference between categorical and numeric variables.
• Investigate your data using str or summary. integer and numeric data is not categorical, while factorial and character data is.

At least one categorical variable

Your dataset does not only contain continuous data. Is it only categorical, though?

How do I know?

• Investigate your data using str or summary. integer and numeric data is not categorical, while factorial and character data is.

Only categorical data: Chi Square Test

If you have only categorical variables, you should do a Chi Square Test. A Chi Square test... LINK TO CHI SQUARE TEST R EXAMPLE

Categorical and continuous data

Your dataset is a mix of categorical and continuous variables. How many factor levels ADD BETTER EXPLANATION

How do I know?

• R commands: quantile(), str, summary
• Investigate your categorical dependent variables using...
• ADD MORE

One or two factor levels: t-test

With one or two factor levels, you should do a t-test. A t-test ... ADD. Check the entry on the T-Test to learn more.

Depending on the variances in your data, the type of t-test differs.

ADAPT THE T-TEST ENTRY SO THAT DIFFERENCE BETWEEN STUDENT AND WELCH IS CLEAR

How do I know?

• Use an F-Test to check whether the variances of the datasets are equal. LINK LEFT BOX TO F-TEST

More than two factor levels: ANOVA

Your categorical variable has more than two factor levels: you should do an ANOVA. An ANOVA is... ADD Check the entry on the ANOVA to learn more.

However, the kind of ANOVA depends on the distribution of your dependent variable.

How do I know?

• R commands: ks.test, shapiro.test, hist
  
• HOW DO I TEST A NORMAL DISTRIBUTION HERE?
• Check the entry on Normal distributions to learn more.

WHERE TO WE LINK?? OR DO WE EXPLAIN ANOVA HERE? R commands: aov, Anova, ezAnova, var.test(), lm
Relevant figures: boxplot()

You might end here, but you might also want to do a Multiple ANOVA with model reduction. WHEN and WHY?? HOW DO WE LINK TO this decision.???

Only continuous variables

Your data is only continuous. Are there dependencies between the variables?

How do I know?

• ADD INFO - HOW DO I KNOW IF THEY ARE DEPENDENT?

No dependencies: Correlations

If there are no dependencies between your variables, you should do a Correlation. A correlation ... ADD. Check the entry on Correlations to learn more. The type of correlation depends on your data distribution.

• ADD INFO ON PEARSON AND SPEARMAN CORRELATIONS; WITH R CODE

LINK TO CORRELATION R EXAMPLES (pearson, spearman)? How do I know?

• ADD INFO - HOW DO I KNOW IF THE DATA IS NORMALLY DISTRIBUTED?
• Check the entry on Normal distributions to learn more.

Clear dependencies

You have one (or more) dependent variable(s) in your dataset EXPLAIN BETTER. Is the dependent variable normally distributed?

How do I know?

• ADD INFO - HOW DO I KNOW IF THE DATA IS NORMALLY DISTRIBUTED?
• Check the entry on Normal distributions to learn more.

Normally distributed dependent variable: Linear Regression

If your dependent variable(s) is/are normally distributed, you should do a Linear Regression. A linear regression .. ADD Check the entry ADD ??

There may be one exception to a plain linear regression: if you have several predictors, there is one more decision to make:

Is there a categorical predictor?

You have several predictors in your dataset. But is (at least) one of them categorical?

How do I know?

• Check the entry on Data formats to understand the difference between categorical and numeric variables.
• Investigate your data using str or summary. integer and numeric data is not categorical, while factorial and character data is.

ADD???

ANCOVA

If you have at least one categorical predictor, you should do an ANCOVA. An ANCOVA is.... (ADD) Check the entry on ANCOVA to learn more.

Not normally distributed dependent variable

The dependent variable(s) is/are not normally distributed. Which kind of distribution does it show, then? For both Binomial and Poisson distributions, your next step is the Generalised Linear Model. However, it is important that you select the proper distribution type in the GLM ADD MORE INFO

How do I know?

• ADD INFO - HOW DO I KNOW THE DISTRIBUTION TYPE?
• Check the entry on Non-normal distributions to learn more.

Generalised Linear Models

You have arrived at a Generalised Linear Model (GLM). GLMs are... ADD

Depending on the existence of random variables, there is a distinction between Mixed Effect Models and Generalised Linear Models, which are based on regressions.

How do I know?

• HOW DO I KNOW IF I HAVE RANDOM VARIABLES???
• R commands: glmer, glmmPQL
  

Relevant figures:

WHAT IS THIS ABOUT? Poisson GLM|Dependent variable is count data R commands: glm
Relevant figures: plot

Binomial GLM|Dependent variable is 0/1 or proportions] R commands: glm
Relevant figures:

Multivariate statistics

You are dealing with Multivariate Statistics. This means that ... ADD You have multiple variables, and maybe they also have internal dependencies. Which kind of analysis do you want to conduct?

How do I know?

• In an Ordination, you arrange your data alongside underlying gradients in the variables to see which variables most strongly define the data points.
• In a Cluster Analysis, you group your data points according to how similar they are, resulting in a tree structure.
• In a Network Analysis, you arrange your data in a network structure to understand their connections and the distance between individual data points.

Ordinations

You are doing an ordination. An ordination ... ADD Check the entry on Ordinations (to be added) to learn more.

There is a difference between ordinations for different data types - for abundance data, you use Euclidean distances, and for continuous data, you use Jaccard distances.

How do I know?

• Check the entry on Data formats to learn more about the different data formats.
• Investigate your data using str or summary. Abundance data is marked as FORMATNAME, and continuous data is marked as FORMATNAME.

MAKE THE STUFF BELOW CLEARER - OR LINK SOMEWHERE ELSE? Linear-based ordinations Linear-based ordinations are... It uses Euclidean distances, which is... R commands:
Relevant figures:

Correspondance analysis A correspondence analysis is... It uses Jaccard distances, which is... R commands:
Relevant figures:

Cluster Analysis

So you decided for a Cluster Analysis. A Cluster Analysis .. ADD Check the entry on Clustering Methods to learn more.

There is a difference to be made here, dependent on whether you want to classify the data based on prior knowledge (supervised) or not (unsupervised). DIFFERENCE BETWEEN SUPERVISED AND UNSUPERVISED?? DISTINCTION NOT MADE IN THE CLUSTERING ENTRY

WHERE DO WE LINK?

How do I know?

• HOW DO I KNOW IF ITS SUPERVISED OR NOT?

Network Analysis

You have decided to do a Network Analysis. In a Network Analysis... ADD Check the entry on Social Network Analysis to learn more.

There is a distinction here between bipartite and tripartite networks, with two or three kinds of nodes, respectively.

How do I know?

• Check your data using the R code ADD CODE

ADD MORE BELOW - OR DO WE LINK SOMEWHERE ELSE? Bipartite If your data has two different kinds of nodes, your network is called a "bipartite" network.

R commands:

• is_bipartite(graph)
• make_bipartite_graph(types, edges, directed = FALSE)

Tripartite R commands:
Relevant figures:

Resterampe

Analysis of Variance
INSERT TYPE II
INSERT RANDOM FACTOR
INSERT LMM

Type III Anova

Dependent variable is count data

Dependent variable is 0/1 or proportions

Random factors

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