(Weighted) Spread of like-dislike scores

Description

The (weighted) spread of like-dislike scores is a measure originally proposed by Wagner (2020), who considers it superior to the distance from the most-liked party, and has since been used by a number of scholars to measure affective polarization. It measures the average spread of party like-dislike scores within respondents, weighted by the size of the parties. To aggregate to the party system, one simply calculates the unweighted average of all respondents’ spread scores. Wagner computes the (weighted) spread of like-dislike scores based on the CSES dataset, but in principle it can be applied to other datasets that contain the required items, i.e., like-dislike scores.

Operationalization

Wagner proceeds in two steps. First, they calculate the (weighted) spread of like-dislike scores for each respondent. Second, they aggregate to the party-system level by taking the mean of the respondents’ distance scores. The unweighted and weighted distance measures are calculated as follows:

Individual-level

Spread: $Spread_i = \sqrt{\frac{\sum_{p=1}^p (like_{ip} - \bar{like_i})^2}{n_p}}$

Weighted spread: $Spread_i = \sqrt{\sum_{p=1}^p v_p (like_{ip} - \bar{like_i})^2}$

where $i$ = individual, $p$ = party, $v_p$ voteshare of party $p$, and $\bar{like_i} = \sum_{p=1}^p (v_{p} like_{ip})$

Aggregation to the party system is done by taking the unweighted mean of all respondents’ affective polarization scores:

$Spread_k = \frac{\sum_{i=1}^n Spread_{ik}}{N_{ik}}$

This means that the spread of party like-dislike scores at the party system level $k$ is obtained by summing the spread score of all respondents $i$ in a country-year sample $k$ and dividing by the number of respondents $N_k$.

polaR

# Import data
cses_imd <- polaR_import(source = "cses_imd",
						 path = "datasets/cses/cses_imd.dta")

# Use 'weighted' to toggle between the weighted and unweighted measure
# The data output can be provided with individual scores or aggregated to a country/year level with 'aggregate'
cses_imd <- spread_likedislike(cses_imd,
							  weighted = TRUE,
							  aggregate = FALSE)

Visualization

Use cases

Publications that use this measure: