# How Many Factors to Retain in Factor Analysis

**Dominique Makowski**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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# The method agreement procedure

When running a factor analysis, one often needs to know how many components / latent variables to retain. Fortunately, many methods exist to statistically answer this question. Unfortunately, there is no consensus on which method to use. Therefore, the `n_factors()`

function, available in the psycho package, performs the **method agreement procedure**: it runs all the routines and returns the number of factors with the highest consensus.

# devtools::install_github("neuropsychology/psycho.R") # Install the last psycho version if needed library(tidyverse) library(psycho) results <- attitude %>% psycho::n_factors() print(results) ## The choice of 1 factor is supported by 5 (out of 9; 55.56%) methods (Optimal Coordinates, Acceleration Factor, Parallel Analysis, Velicer MAP, VSS Complexity 1).

We can have an overview of all values by using the `summary`

method.

n.Factors | n.Methods | Eigenvalues | Cum.Variance |
---|---|---|---|

1 | 5 | 3.72 | 0.53 |

2 | 3 | 1.14 | 0.69 |

3 | 1 | 0.85 | 0.81 |

4 | 0 | 0.61 | 0.90 |

5 | 0 | 0.32 | 0.95 |

6 | 0 | 0.22 | 0.98 |

7 | 0 | 0.14 | 1.00 |

And, of course, plot it π

plot(results)

The plot shows the **number of methods** (in yellow), the **Eigenvalues** (red line) and the cumulative proportion of **explained variance** (blue line).

For more details, we can also extract the final result (the optimal number of factors) for each method:

Method | n_optimal |
---|---|

Optimal Coordinates | 1 |

Acceleration Factor | 1 |

Parallel Analysis | 1 |

Eigenvalues (Kaiser Criterion) | 2 |

Velicer MAP | 1 |

BIC | 2 |

Sample Size Adjusted BIC | 3 |

VSS Complexity 1 | 1 |

VSS Complexity 2 | 2 |

# Tweaking

We can also provide a correlation matrix, as well as changing the rotation and the factoring method.

df <- psycho::affective cor_mat <- psycho::correlation(df) cor_mat <- cor_mat$values$r results <- cor_mat %>% psycho::n_factors(rotate = "oblimin", fm = "mle", n=nrow(df)) print(results) ## The choice of 2 factors is supported by 5 (out of 9; 55.56%) methods (Parallel Analysis, Eigenvalues (Kaiser Criterion), BIC, Sample Size Adjusted BIC, VSS Complexity 2). plot(results)

# Credits

This package helped you? Donβt forget to cite the various packages you used π

You can cite `psycho`

as follows:

- Makowski, (2018).
*The psycho Package: an Efficient and Publishing-Oriented Workflow for Psychological Science*. Journal of Open Source Software, 3(22), 470. https://doi.org/10.21105/joss.00470

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**Dominique Makowski**.

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