This vignette demonstrates how to incorporate categorical predictors
in discordant-kinship regression analyses using the discord
package. Drawing on dyadic modeling strategies described by Kenny et al.
(Kenny, Kashy, and Cook 2006) and their
extension to kinship settings by Hwang and Garrison (Hwang 2022), we present a several approaches to
handling categorical variables like sex and race in these specialized
regression models.
We focus on:
discord
package
functionsTo illustrate these concepts, we examine whether sex and race predict socioeconomic status (SES) at age 40, using different categorical coding schemes with data from the 1979 National Longitudinal Survey of Youth (NLSY79).
In dyadic analysis, categorical predictors can operate at different levels:
We have summarized these variable types in the table below:
Variable Type | Definition | Examples | Analytic Implications |
---|---|---|---|
Between-dyads | Members of the same pair have identical values | Race in same-race siblings; Length of marriage in couples | Simplifies analysis; Functions like individual-level variable |
Within-dyads | Varies within pairs but constant across pairs | Division of chores between roommates (must sum to 100%) | Rare for categorical variables; Requires specialized handling |
Mixed | Varies both within and across pairs | Sex in sibling pairs; Age; Personality traits | Most complex; Requires transformation to dyad-level variables |
When analyzing continuous predictors, we typically calculate mean scores and difference scores. However, as Kenny et al. (Kenny, Kashy, and Cook 2006) note, categorical variables require alternative approaches since mean and difference calculations aren’t meaningful.
The discord
package implements two primary coding
strategies for categorical predictors:
Binary match coding creates a simple indicator of whether pairs match (1) or differ (0) on the categorical variable: - Same-sex pairs (male-male or female-female) → 1 - Mixed-sex pairs (male-female) → 0
When to use: When your research question focuses on similarity versus difference rather than specific category effects.
Multi-match coding preserves specific category information: - Male-male pairs → “MALE” - Female-female pairs → “FEMALE” - Mixed pairs → “mixed”
When to use: When you hypothesize different effects for different categories (e.g., male pairs vs. female pairs).
Following Hwang and Garrison (Hwang 2022), the approach you select should align with your specific research questions and theoretical framework.
We’ll demonstrate categorical predictor handling using sibling data from the NLSY79. We first load necessary packages and prepare our dataset.
# Loading necessary packages and data
# For easy data manipulation
library(dplyr)
# For kinship linkages
library(NlsyLinks)
# For discordant-kinship regression
library(discord)
# pipe
library(magrittr)
# data
data(data_flu_ses)
We begin by filtering and cleaning the dataset, creating kinship links, and recoding categorical variables. In this example, we use full siblings (R = 0.5) from the Gen1 cohort.
# for reproducibility
set.seed(2023)
link_vars <- c("S00_H40", "RACE", "SEX")
# Specify NLSY database and kin relatedness
link_pairs <- Links79PairExpanded %>%
filter(RelationshipPath == "Gen1Housemates" & RFull == 0.5)
Next, we use CreatePairLinksSingleEntered()
from the
NlsyLinks
package to merge kinship links with our target
variables. This merge creates a sibling-pair dataset in “wide” format,
with suffixes distinguishing the two individuals in each pair.
df_link <- CreatePairLinksSingleEntered(
outcomeDataset = data_flu_ses,
linksPairDataset = link_pairs,
outcomeNames = link_vars
)
To ensure that the dependent variable (SES at age 40) is available for both members of the pair, we filter out missing values. We also recode sex and race into string labels and retain only same-race pairs to make race a between-dyads variable.
# We removed the pair when the Dependent Variable is missing.
df_link <- df_link %>%
filter(!is.na(S00_H40_S1) & !is.na(S00_H40_S2)) %>%
mutate(
SEX_S1 = case_when(
SEX_S1 == 0 ~ "MALE",
SEX_S1 == 1 ~ "FEMALE"
),
SEX_S2 = case_when(
SEX_S2 == 0 ~ "MALE",
SEX_S2 == 1 ~ "FEMALE"
),
RACE_S1 = case_when(
RACE_S1 == 0 ~ "NONMINORITY",
RACE_S1 == 1 ~ "MINORITY"
),
RACE_S2 = case_when(
RACE_S2 == 0 ~ "NONMINORITY",
RACE_S2 == 1 ~ "MINORITY"
)
)
# we only include same-race pairs in this example for demonstration purpose
df_link <- df_link %>%
dplyr::filter(RACE_S1 == RACE_S2)
To avoid violating independence assumptions, we retain only one sibling pair per household:
##@ Mixed Variables: Gender as an Example
Sex is a classic example of a mixed variable in sibling studies because it can vary both within and between dyads. Siblings can be of the same or different sexes, and the pattern varies across families.
We use the discord_data()
function to prepare the data
for analysis.
cat_sex <- discord_data(
data = df_link,
outcome = "S00_H40",
sex = "SEX",
race = "RACE",
demographics = "sex",
predictors = NULL,
pair_identifiers = c("_S1", "_S2"),
coding_method = "both"
)
In the restructured data, the individual with the higher SES (the dependent variable) is labeled “_1” and the other is labeled “_2”. This gives us the following sex compositions:
id | S00_H40_1 | S00_H40_2 | S00_H40_diff | S00_H40_mean | SEX_1 | SEX_2 | SEX_binarymatch | SEX_multimatch |
---|---|---|---|---|---|---|---|---|
359 | 80.83837 | 46.83232 | 34.006052 | 63.83535 | FEMALE | FEMALE | same-sex | FEMALE |
363 | 79.39371 | 74.43080 | 4.962906 | 76.91225 | FEMALE | MALE | mixed-sex | mixed |
492 | 70.61552 | 50.76235 | 19.853172 | 60.68894 | FEMALE | MALE | mixed-sex | mixed |
85 | 48.54822 | 35.09636 | 13.451863 | 41.82229 | FEMALE | FEMALE | same-sex | FEMALE |
131 | 49.05986 | 35.82118 | 13.238680 | 42.44052 | MALE | FEMALE | mixed-sex | mixed |
299 | 47.78226 | 31.52980 | 16.252455 | 39.65603 | MALE | MALE | same-sex | MALE |
The possible sex combinations in the dataset are:
SEX_1 | SEX_2 | sample_size |
---|---|---|
FEMALE | FEMALE | 416 |
FEMALE | MALE | 358 |
MALE | FEMALE | 429 |
MALE | MALE | 396 |
By default, the SEX_1
variable indicates the sex of the
individual who has the higher DV within the pair, and the
SEX_2
variable indicates the sex of the other member of the
dyad.
As shown above, the discord_data()
function generates
SEX_binarymatch
and SEX_multimatch
for the sex
variable. By doing so, the sex variable (which was initially a mixed
variable) becomes a between-dyad variable as follows:
binary | multi | SEX_1 | SEX_2 | sample_size |
---|---|---|---|---|
mixed-sex | mixed | FEMALE | MALE | 358 |
mixed-sex | mixed | MALE | FEMALE | 429 |
same-sex | FEMALE | FEMALE | FEMALE | 416 |
same-sex | MALE | MALE | MALE | 396 |
Researchers can choose between these options based on their research question:
For demonstration purposes, we’ve already filtered our dataset to include only same-race pairs, making race a between-dyads variable. Let’s prepare the data specifically for race analysis:
set.seed(2023) # for reproducibility
# Prepare data with race as demographic variable
cat_race <- discord_data(
data = df_link,
outcome = "S00_H40",
predictors = NULL,
sex = "SEX",
race = "RACE",
demographics = "race",
pair_identifiers = c("_S1", "_S2"),
coding_method = "both"
)
The race compositions in the dataset are:
RACE_binarymatch | RACE_multimatch | RACE_1 | RACE_2 | sample_size |
---|---|---|---|---|
same-race | MINORITY | MINORITY | MINORITY | 778 |
same-race | NONMINORITY | NONMINORITY | NONMINORITY | 821 |
Since we filtered for same-race pairs only, all pairs have RACE_binarymatch = “same-race”. When using NLSY data, the RACE_multimatch variable distinguishes between the three groupings that the bureau of labor statistics uses (Black, Hispanic, and Non-Black, Non-Hispanic).
The RACE_binarymatch
variable indicates whether the pair
is the same-race pair or mixed-race pair. Because the race variable is a
between-dyads variable, all the pairs in this example are same-race
pairs. The RACE_multimatch
variable indicates whether the
pair is minority-minority, nonminority-nonminority, or mixed-race.
We can also prepare data that includes both sex and race composition variables:
# for reproducibility
set.seed(2023)
cat_both <- discord_data(
data = df_link,
outcome = "S00_H40",
predictors = NULL,
sex = "SEX",
race = "RACE",
demographics = "both",
pair_identifiers = c("_S1", "_S2"),
coding_method = "both"
)
The combined race and sex compositions in the dataset are:
RACE_multi | RACE_1 | RACE_2 | SEX_binary | SEX_multi | SEX_1 | SEX_2 | sample_size |
---|---|---|---|---|---|---|---|
MINORITY | MINORITY | MINORITY | mixed-sex | mixed | FEMALE | MALE | 179 |
MINORITY | MINORITY | MINORITY | mixed-sex | mixed | MALE | FEMALE | 201 |
MINORITY | MINORITY | MINORITY | same-sex | FEMALE | FEMALE | FEMALE | 204 |
MINORITY | MINORITY | MINORITY | same-sex | MALE | MALE | MALE | 194 |
NONMINORITY | NONMINORITY | NONMINORITY | mixed-sex | mixed | FEMALE | MALE | 179 |
NONMINORITY | NONMINORITY | NONMINORITY | mixed-sex | mixed | MALE | FEMALE | 228 |
NONMINORITY | NONMINORITY | NONMINORITY | same-sex | FEMALE | FEMALE | FEMALE | 212 |
NONMINORITY | NONMINORITY | NONMINORITY | same-sex | MALE | MALE | MALE | 202 |
First, we examine whether same-sex versus mixed-sex pairs differ in SES discordance:
discord_sex_binary <- discord_regression(
data = df_link,
outcome = "S00_H40",
sex = "SEX",
race = "RACE",
demographics = "sex",
predictors = NULL,
pair_identifiers = c("_S1", "_S2"),
coding_method = "binary"
)
Term | Estimate | Standard Error | T Statistic | P Value |
---|---|---|---|---|
(Intercept) | 23.952 | 1.166 | 20.534 | p<0.001 |
S00_H40_mean | -0.090 | 0.020 | -4.561 | p<0.001 |
SEX_binarymatch | 0.024 | 0.750 | 0.031 | p=0.975 |
Interpretation:
The mean SES score for the siblings (S00_H40_diff
)
is a significant control variable (p =0). S00_H40_mean
is
negatively associated with the difference in SES score between siblings
at age 40, S00_H40_diff
, controlling for another variable
(in this case, SEX_binarymatch
). It is estimated that for
one unit increase of S00_H40_mean
,
S00_H40_diff
is expected to decrease approximately
-0.09.
The binary sex match variable SEX_binarymatch
is not
a significant predictor (p = 0.975) when controlling for
S00_H40_mean
. There is no significant differences between
same-sex pairs and mixed-sex pairs in S00_H40_diff
. This
means that the difference between same-sex pairs and mixed-sex pairs
does not significantly predict the S00_H40_diff
in the pair
when controlling for S00_H40_mean
.
Next, we test whether male-male, female-female, and mixed-sex pairs differ. The regression model can be conducted as such:
discord_sex_multi <- discord_regression(
data = df_link,
outcome = "S00_H40",
sex = "SEX",
race = "RACE",
predictors = NULL,
pair_identifiers = c("_S1", "_S2"),
coding_method = "multi"
)
Term | Estimate | Standard Error | T Statistic | P Value |
---|---|---|---|---|
(Intercept) | 24.523 | 1.248 | 19.647 | p<0.001 |
S00_H40_mean | -0.075 | 0.020 | -3.673 | p<0.001 |
SEX_multimatchMALE | -0.380 | 1.053 | -0.361 | p=0.718 |
SEX_multimatchmixed | -0.192 | 0.907 | -0.212 | p=0.832 |
RACE_multimatchNONMINORITY | -2.277 | 0.772 | -2.950 | p=0.003 |
The term S00_H40_mean
was a significant control
variable (p = 0). This means that the mean SES score for the sibling
pairs (S00_H40_mean
) is negatively associated with the
difference in SES between siblings (S00_H40_diff
),
controlling for other variables (in this case,
SEX_multimatch
). It is estimated that for one unit increase
of S00_H40_mean
, S00_H40_diff
is expected to
decrease approximately 0.075.
There was no significant difference between female-female pairs
and male-male pairs (p=0.718) to predict S00_H40_diff
.
Similarly, there were no significant differences between mixed-sex pairs
and female-female pairs (p = 0.832).
The coefficient 0.38 is the difference between the expected
S00_H40_diff
for the reference group (in this case, the
female-female pairs) and the male-male pairs.
The coefficient 0.192 is the difference between the expected
S00_H40_diff
for the reference group (in this case, the
female-female pairs) and the mixed-sex pairs. However, these
coefficients are not significant, so it is not advisable to interpret
the coefficients.
We can also examine whether sex composition predicts mean SES levels (rather than SES differences):
Term | Estimate | Standard Error | T Statistic | P Value |
---|---|---|---|---|
(Intercept) | 52.399 | 0.675 | 77.576 | p<0.001 |
SEX_binarymatchsame-sex | 0.018 | 0.948 | 0.019 | p=0.985 |
In this regression model, the mean SES score for the siblings
(S00_H40_mean
) was regressed on the SEX-composition
variable (SEX_binarymatch
).
There is no significant difference between same-sex pairs and mixed-sex pairs in the mean SES score for the siblings (p=0.985)
It is estimated that compared to the mixed-sex pairs, the same-sex
pairs would have approximately 0.018 higher S00_H40_mean
.
However, this coefficient is not significant, so it is not advisable to
interpret the coefficient.
Term | Estimate | Standard Error | T Statistic | P Value |
---|---|---|---|---|
(Intercept) | 50.529 | 0.927 | 54.516 | p<0.001 |
SEX_multimatchMALE | 3.872 | 1.327 | 2.917 | p=0.004 |
SEX_multimatchmixed | 1.870 | 1.146 | 1.632 | p=0.103 |
There is a significant difference between female-female pairs and
male-male pairs (0.004) to predict the S00_H40_mean
.
However, there is no significant difference between mixed-sex pairs and
female-female pairs (p = 0.103).
The coefficient 3.872 is the difference between the expected
S00_H40_mean
(the mean SES score for the siblings) for the
reference group (in this case, the female-female pairs) and the
male-male pairs. It can be concluded that male-male pairs and
female-female pair has significant differences in
S00_H40_mean
.
The coefficient 1.87 is the difference between the expected
S00_H40_mean
for the reference group (in this case, the
female-female pairs) and the mixed-sex pairs. However, these
coefficients are not significant, so it is not advisable to interpret
the coefficients.
For race variables, we use the multi-match coding to examine differences between minority and non-minority pairs:
The regression model with a multi-match race variable as a predictor can be conducted as such:
# perform kinship regressions
cat_race_reg <- discord_regression(
data = df_link,
outcome = "S00_H40",
sex = "SEX",
race = "RACE",
demographics = "race",
predictors = NULL,
pair_identifiers = c("_S1", "_S2"),
coding_method = "multi"
)
Term | Estimate | Standard Error | T Statistic | P Value |
---|---|---|---|---|
(Intercept) | 24.361 | 1.108 | 21.987 | p<0.001 |
S00_H40_mean | -0.076 | 0.020 | -3.712 | p<0.001 |
RACE_multimatchNONMINORITY | -2.272 | 0.771 | -2.945 | p=0.003 |
The mean SES score for the siblings (S00_H40_mean
) is a
significant control variable (p =0. The term S00_H40_mean
is negatively associated with the difference score of SES between
siblings (S00_H40_diff
), controlling for another variable
(in this case, RACE_multimatchNONMINORITY
). It is estimated
that for one unit increase of S00_H40_mean
, the DV
(S00_H40_diff
) is expected to decrease by approximately
0.076.
The term RACE_multimatchNONMINORITY
was a significant
predictor of S00_H40_diff
(p = 0.003) after controlling for
S00_H40_mean
. This means that the difference between the
“Minority-minority” sibling pairs and “nonminority-non-minority” sibling
pairs significantly predicts S00_H40_diff
. Specifically,
compared to the reference group (the “minority” pairs), “nonminority”
pairs are expected to have approximately 2.272 lower
S00_H40_diff
.
We can also examine whether race composition predicts mean SES levels:
Term | Estimate | Standard Error | T Statistic | P Value |
---|---|---|---|---|
(Intercept) | 47.645 | 0.659 | 72.335 | p<0.001 |
RACE_multimatchNONMINORITY | 9.277 | 0.919 | 10.092 | p<0.001 |
There is significant difference between “minority” pairs and
“nonminority” pairs in S00_H40_mean
(p =0). It is estimated
that, compared to the reference group (minority pairs), the nonminority
pairs would have approximately 9.277 lower
S00_H40_mean
.
We can include both sex and race as predictors in the same model:
First, we restructure the data for the kinship-discordant regression
using the discord_data()
function.
both_multi <- discord_regression(
data = df_link,
outcome = "S00_H40",
sex = "SEX",
race = "RACE",
demographics = "both",
predictors = NULL,
pair_identifiers = c("_S1", "_S2"),
coding_method = "multi"
)
Term | Estimate | Standard Error | T Statistic | P Value |
---|---|---|---|---|
(Intercept) | 24.523 | 1.248 | 19.647 | p<0.001 |
S00_H40_mean | -0.075 | 0.020 | -3.673 | p<0.001 |
SEX_multimatchMALE | -0.380 | 1.053 | -0.361 | p=0.718 |
SEX_multimatchmixed | -0.192 | 0.907 | -0.212 | p=0.832 |
RACE_multimatchNONMINORITY | -2.277 | 0.772 | -2.950 | p=0.003 |
The mean SES score for the siblings (S00_H40_mean
) is a
significant control variable (p = 0 ). S00_H40_mean
is
negatively associated with the difference score of SES between the
siblings (S00_H40_diff
), controlling for other variables
(in this case, the SEX_multimatchMALE
,
SEX_multimatchmixed
and
RACE_multimatchNONMINORITY
). It is estimated that for one
unit increase of the mean SES score for the sibling pairs(
S00_H40_mean
), the difference score of SES between
siblings(S00_H40_diff
) is expected to decrease
approximately -0.075.
The SEX_multimatchmixed
and
SEX_multimatchMALE
are not significant predictors when
controlling for other variables (i.e., S00_H40_mean
and
RACE_multimatchNONMINORITY
). The coefficient -0.38 is the
difference between the expected DV (S00_H40_diff
) for the
reference group (in this case, the “female-female” pairs) and the
“male-male” pairs. The coefficient -0.192 is the difference between the
expected DV (S00_H40_diff
) for the female-female pairs and
the mixed-sex pairs. However, these coefficients are not significant, so
it is not advisable to interpret the coefficients.
The term RACE_multimatchNONMINORITY
is a significant
predictor (p = 0.003) when controlling for other variables (i.e.,
SEX_multimatchMALE
, SEX_multimatchmixed
, and
S00_H40_mean
). This means that there is a significant
difference between minority race pairs and nonminority race pairs in the
difference score of SES between siblings (S00_H40_diff
)
when controlling for the model covariates (i.e.,
SEX_multimatchMALE
, SEX_multimatchmixed
, and
S00_H40_mean
). Specifically, compared to the minority race
pairs, the nonminority race pairs were expected to have approximately
-2.277 higher difference score of SES between siblings at age 40.
To combine binary and multi-match coding approaches, we can use the
standard lm()
function:
We can perform regression using the binary-match sex variable and multi-match race variable as such:
discord_cat_diff <- lm(
S00_H40_diff ~ S00_H40_mean +
RACE_multimatch + SEX_binarymatch,
data = cat_both
)
Term | Estimate | Standard Error | T Statistic | P Value |
---|---|---|---|---|
(Intercept) | 24.357 | 1.172 | 20.787 | p<0.001 |
S00_H40_mean | -0.076 | 0.020 | -3.711 | p<0.001 |
RACE_multimatchNONMINORITY | -2.272 | 0.771 | -2.944 | p=0.003 |
SEX_binarymatchsame-sex | 0.007 | 0.748 | 0.009 | p=0.993 |
The mean SES score for the siblings at 40 (S00_H40_mean
)
is a significant control variable (p = 0. The mean SES score for the
siblings (S00_H40_mean
) is negatively associated with the
difference score of SES between the siblings
(S00_H40_diff
), controlling for other variables (in this
case, SEX_binarymatchsame-sex
and
RACE_multimatchNONMINORITY
). It is estimated that for one
unit increase of S00_H40_mean
, the DV
(S00_H40_diff
) is expected to decrease approximately
0.076.
The term SEX_binarymatchsame-sex
is not a significant
predictor (p = 0.993) when controlling for other variables (i.e.,
S00_H40_mean
and RACE_multimatchNONMINORITY
).
This means that the difference between same-sex pairs and mixed-sex
pairs does not significantly predict the difference score of SES between
siblings (S00_H40_diff
) when controlling for the mean SES
score for the siblings (S00_H40_mean
) and race-composition
of the pair (RACE_multimatchNONMINORITY
). Compared to the
mixed-sex pairs, it is estimated that the same-sex pairs have
approximately 0.007higher difference score of SES between siblings
(S00_H40_diff
) when controlling for the mean SES score for
the sibling pairs (S00_H40_mean
) and race-composition of
the pairs (RACE_multimatchNONMINORITY
). However, this
variable is not significant, so it is not advisable to interpret the
coefficient.
The term RACE_multimatchNONMINORITY
is a significant
predictor (p = 0.003). This means that there is a significant difference
between minority race pairs and nonminority race pairs to predict the
difference score of SES between the siblings (S00_H40_diff
)
when controlling for the model covariates (i.e.,
SEX_binarymatchsame-sex
and S00_H40_mean
).
Specifically, compared to the minority race pairs, nonminority race
pairs were expected to have approximately 2.272 lower difference scores
of SES between siblings (S00_H40_diff
).
Finally, we examine how sex and race together predict mean SES levels:
Term | Estimate | Standard Error | T Statistic | P Value |
---|---|---|---|---|
(Intercept) | 45.801 | 1.013 | 45.210 | p<0.001 |
RACE_multimatchNONMINORITY | 9.277 | 0.917 | 10.114 | p<0.001 |
SEX_multimatchMALE | 3.867 | 1.287 | 3.005 | p=0.003 |
SEX_multimatchmixed | 1.800 | 1.111 | 1.620 | p=0.105 |
The term SEX_multimatchMALE
is a significant predictor
(p = 0.003) when controlling for other variables (i.e.,
SEX_multimatchmixed
and
RACEe_multimatchNONMINORITY
). This means that the
difference between female-female pairs and male-male pairs significantly
predicted the mean SES score for the siblings when controlling for
race-composition of the pairs. Compared to the female-female pairs, it
is estimated that the male-male pairs have approximately 3.867 higher
mean SES score for the siblings when controlling for and
race-composition of the pairs.
The term SEX_multimatchmixed
was not a significant
predictor (p = 0.105) when controlling for other variables (i.e.,
SEX_multimatchMALE
and
RACE_multimatchNONMINORITY
). This means that the difference
between female-female pairs and mixed-sex pairs does not significantly
predict the mean SES score for the siblings when controlling for
race-composition of the pairs. Compared to the female-female pairs, it
is estimated that the mixed-sex pairs have approximately 1.8 higher mean
SES score for the sibling pairs when controlling for and
race-composition of the pairs. However, this variable is not
significant, so it is not advisable to interpret the coefficient.
The term RACE_multimatchNONMINORITY
is a significant
predictor (p = 0). This means that there is a significant difference
between minority race pairs and nonminority race pairs in the mean SES
score for the sibling pairs (S00_H40_mean
) when controlling
for the other variables (i.e., SEX_multimatchmixed
and
SEX_multimatchMALE
). Specifically, compared to the minority
race pairs, nonminority race pairs were expected to have approximately
9.277 higher mean SES score for siblings
Term | Estimate | Standard Error | T Statistic | P Value |
---|---|---|---|---|
(Intercept) | 47.601 | 0.809 | 58.803 | p<0.001 |
RACE_multimatchNONMINORITY | 9.278 | 0.920 | 10.090 | p<0.001 |
SEX_binarymatchsame-sex | 0.086 | 0.919 | 0.093 | p=0.926 |
The term SEX_binarymatchsame-sex
is not a significant
predictor (p = 0.926) when controlling for the race-composition variable
(i.e., RACE_multimatchNONMINORITY
). This means that the
difference between mixed-sex pairs and same sex pairs does not
significantly predict the mean SES score for the siblings when
controlling for race-composition of the pairs. Compared to the mixed-sex
pairs, it is estimated that the same-sex pairs have approximately 0.086
higher mean SES score for the sibling pairs (S00_H40_mean
)
when controlling for and race-composition of the pairs
(RACE_multimatchNONMINORITY
). However, this variable is not
significant, so it is not advisable to interpret the coefficient.
The term RACE_multimatchNONMINORITY
is a significant
predictor (p = 0). This means that there is a significant difference
between minority race pairs and nonminority race pairs in the the mean
SES score for the siblings when controlling for the sex-composition
variable (i.e., SEX_binarymatchsame-sex
). Specifically,
compared to the minority race pairs, nonminority race pairs were
expected to have approximately 9.278 higher mean SES score for
siblings
This vignette has demonstrated how to incorporate categorical
predictors in discordant-kinship regression analyses using the
discord
package.
Key findings and recommendations include:
For implementation in your own research, we recommend: - Consider the
theoretical nature of your categorical predictors - Use
discord_data()
to prepare categorical variables
appropriately - Choose coding schemes based on your specific research
questions - Carefully interpret results in light of variable coding
decisions