README.md
In FertNet: Process Data from the Social Networks and Fertility Survey

FertNet

The goal of FertNet is very specific, namely to process and correct the Social Networks and Fertility Data, with a special focus on the network data. The data was collected through the LISS (Longitudinal Internet studies for the Social Sciences) panel, and can be downloaded from https://www.dataarchive.lissdata.nl/study_units/view/1377. The data is free, but requires registration.

The aim of the Social Networks and Fertility Data was to collect personal network data from a representative sample of Dutch women. 758 women each named 25 individuals from their network (so-called alters), reported on several characteristics about these individuals (alter attributes), and also listed the relationships between those 25 individuals (alter-alter-ties). This package helps to deal with this network data by producing dataframes with alter attributes and dataframes with edgelists (alter-alter-ties) and storing them in list-columns. This facilitates later analyses and visualisation of these networks, particularly when these datasets are transformed into tidygraph-objects. Additionally, this package corrects some of the reporting errors of the respondents, and provides sensible variables names and English labels.

For this package it is important that you download the SPSS-version of the data, named wj18a_EN_1.0p.sav from HERE.

You can install FertNet like so:

install.packages("FertNet")

You can install the development version of FertNet like so:

if (!require("remotes")) install.packages("remotes")
devtools::install_github("gertstulp/FertNet")

The main function of the FertNet package is produce_data. [be sure to have wj18a_EN_1.0p.sav downloaded and in your working directory]

library(FertNet)
data <- produce_data()

produce_data() is a wrapper around several functions, each of which gives some insights into what happens. The below code results in the exact same as produce_data():

data <- read_data() |> 
  translate() |>
  change_column_types() |>
  fix_errors() |>
  create_relation_labels() |>
  create_nw()

The package also allows you to:

1) Create a new variable to the dataset that is a list-column with tidygraph-objects. For this, the tidygraph package needs to be installed.

2) Add background variables of the respondents. For this to work, you need to download the SPSS-version of the background data from February 2018, named avars_201802_EN_1.0p.sav from HERE. The data is free, but requires registration.

The below code is therefore probably what most researchers are after.

data <- produce_data(tidygraph_col = TRUE,
                     background_vars = TRUE)

Let’s produce a visualisation of a network for one of the respondents. This requires the package ggraph.

if (!require("ggraph")) install.packages("ggraph")
library(ggraph)
ggraph(data$tidygraph[[13]], layout = "kk") +
  geom_edge_link(colour = "grey") +
  geom_node_point(aes(colour = closeness_a), size = 7) +
  geom_node_text(aes(label = names_a), colour = "white") +
  labs(colour = NULL) +
  theme_graph()

The produce_data function comes with one additional argument that allows you to keep the variables on the time it took respondents to answer eachcquestion. This information is probably less relevant to most researchers, which is why it defaults to being excluded.

data <- produce_data(remove_timing_vars = TRUE)

Stulp, G. (2020), “Methods and Materials of the Social networks and fertility survey (Sociale relaties en kinderkeuzes)”, https://doi.org/10.34894/EZCDOA, DataverseNL, V3
Stulp, G. (2021). Collecting large personal networks in a representative sample of Dutch women. Social Networks ,64, 63–71. https://doi.org/10.1016/j.socnet.2020.07.012.
Buijs, VL, & Stulp, G. (2022). Friends, family, and family friends: Predicting friendships of Dutch women. Social Networks, 70, 25–35. https://doi.org/10.1016/j.socnet.2021.10.008.
Stadel, M & Stulp, G. (2022). Balancing bias and burden in personal network studies. Social Networks, 70, 16–24. https://doi.org/10.1016/j.socnet.2021.10.007.
Stulp, G & Barrett, L. (2021). Do data from large personal networks support cultural evolutionary ideas about kin and fertility? Social Sciences 10, 177. https://doi.org/10.3390/socsci10050177.