Class-Based Zero-Sum Beliefs

March 2024a: Analysis

Background

This is a preregistered study, intended to draw boundaries around our effect: hold for advantage-gain frame and not for disadvantage-gain frame.

Procedure

In an experimental design, participants were assigned to one of two conditions: Advantage-gain frame (upper class gaining at working class expense) and disadvantage-gain frame (working class gaining at upper class expense).

They completed the following scales: (1) class-based zero-sum beliefs (frame consistent with randomly-assigned condition); (2) class solidarity; and (3) general zero-sum mindset.

They also read about one neutrally-framed economic progressive policy proposal (randomly-selected out of a set of four policy descriptions) and indicate their support for that policy.

Finally, they will indicate their political ideology, party identification, and demographic information.

Hypotheses

1. Class-based zero-sum beliefs, in an advantage-gain frame, positively predict support for progressive economic policy, when compared to a disadvantage-gain frame.
   
   
2. Class-based zero-sum beliefs, in an advantage-gain frame, positively predict class solidarity, when compared to a disadvantage-gain frame.
   
   
3. The relationship between class-based zero-sum beliefs, in an advantage-gain, and support for progressive economic policy is at least partially mediated by class solidarity, when to a disadvantage-gain frame.

Analysis plan

1. Linear Model 1: Support for policy as the outcome variable; Class Zero-Sum Beliefs as the primary predictor; Condition as the moderator; General zero-sum mindset as a control variable.
   
   
2. Linear Model 2: Class solidarity as the outcome variable; Class Zero-Sum Beliefs as the primary predictor; Condition as the moderator; General zero-sum mindset as a control variable.
   
   
3. Moderation mediation: Support for policy as the outcome variable; Class Zero-Sum Beliefs as the primary predictor; Class solidarity as the mediator; General zero-sum mindset as a control variable. Condition as the moderator of the a-path and direct link.

Exploratory hypotheses

The disadvantage-gain frame condition will either not predict, or weakly predict, class solidarity and support for policy.

Attention Check

But first, let’s exclude participants who failed a very simple attention check.

df_cbzs %>% 
  group_by(att_1) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

att_1	N	Perc
0	8	2.00
1	392	97.76
NA	1	0.25

Alright, that leaves us with 392. Cool.

Demographics

Race

df_cbzs_elg %>% 
  group_by(race) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

race	N	Perc
American Indian or Alaska Native	2	0.51
Asian	24	6.12
Black or African American	48	12.24
Hispanic, Latino, or Spanish origin	15	3.83
Middle Eastern or North African	1	0.26
Native Hawaiian or Other Pacific Islander	1	0.26
Other (please specify)	3	0.77
White	276	70.41
multiracial	22	5.61

Gender

df_cbzs_elg %>% 
  mutate(gender = ifelse(is.na(gender) | gender == "","other",gender)) %>% 
  group_by(gender) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

gender	N	Perc
man	174	44.39
other	6	1.53
woman	212	54.08

Age

df_cbzs_elg %>% 
  summarise(age_mean = round(mean(age,na.rm = T),2),
            age_sd = round(sd(age,na.rm = T),2)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

age_mean	age_sd
39.54	13.01

Education

df_cbzs_elg %>% 
  group_by(edu) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

edu	N	Perc
noHS	2	0.51
GED	100	25.51
2yearColl	46	11.73
4yearColl	162	41.33
MA	59	15.05
PHD	22	5.61
NA	1	0.26

Subjective SES

df_cbzs_elg %>% 
  group_by(ses) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

ses	N	Perc
Lower Class	43	10.97
Lower Middle Class	119	30.36
Middle Class	177	45.15
Upper Middle Class	51	13.01
Upper Class	2	0.51

Working Class

To what extent do you see yourself as part of the working class? (1 = Not at all part of the working class to 5 = Entirely part of the working class)

df_cbzs_elg %>% 
  ggplot(aes(x = wrkclass)) +
  geom_bar() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank())

Income

df_cbzs_elg %>% 
  ggplot(aes(x = income)) +
  geom_bar() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank()) +
  coord_flip()

Politics

Ideology

Participants were asked about the extent to which they subscribe to the following ideologies on a scale of 1-7 (select NA if unfamiliar): Conservatism, Liberalism, Democratic Socialism, Libertarianism, Progressivism.

means <- df_cbzs_elg %>%
  dplyr::select(PID,ideo_con:ideo_prog) %>% 
  pivot_longer(-PID,
               names_to = "ideo",
               values_to = "score") %>% 
  filter(!is.na(score)) %>% 
  group_by(ideo) %>% 
  summarise(score = mean(score)) %>% 
  ungroup()

df_cbzs_elg %>%
  dplyr::select(PID,ideo_con:ideo_prog) %>% 
  pivot_longer(-PID,
               names_to = "ideo",
               values_to = "score") %>% 
  filter(!is.na(score)) %>%  
  ggplot() +
  geom_density(aes(x = score), fill = "lightblue") +
  scale_x_continuous(limits = c(1,7),
                     breaks = seq(1,7,1)) +
  geom_vline(data = means,mapping = aes(xintercept = score),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold")) +
  facet_wrap(~ideo,nrow = 2)

Party ID

df_cbzs_elg %>% 
  group_by(party_id) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

party_id	N	Perc
Democrat	191	48.72
Independent	111	28.32
Republican	90	22.96

Vote in 2020

df_cbzs_elg %>% 
  group_by(vote_2020) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  arrange(desc(N)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

vote_2020	N	Perc
Joe Biden	212	54.08
Donald Trump	95	24.23
I did not vote	60	15.31
Third-party candidate	25	6.38

Vote in 2024

df_cbzs_elg %>% 
  group_by(vote_2024) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  arrange(desc(N)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

vote_2024	N	Perc
Joe Biden	192	48.98
Donald Trump	99	25.26
I will not vote	51	13.01
Robert F. Kennedy Jr.	25	6.38
Other	21	5.36
Cornel West	2	0.51
Jill Stein	2	0.51

Measures

Class-based Zero-Sum Beliefs

1. If the upper (working) class becomes richer, this comes at the expense of the working (upper) class
   
2. If the upper (working) class makes more money, then the working (upper) class makes less money
   
3. If the upper (working) class does better economically, this does NOT come at the expense of the working (upper) class [R]
   
   alpha = 0.94

means <- df_cbzs_elg %>% 
  group_by(cond) %>% 
  summarise(score = mean(zs_class,na.rm = T)) %>% 
  ungroup()

df_cbzs_elg %>% 
  ggplot(aes(x = zs_class)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(data = means,mapping = aes(xintercept = score),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold")) + 
  facet_wrap(~cond,
             nrow = 2)

Zero-Sum Mindset

1. The success of one person is usually the failure of another person
   
2. Life is such that when one person gains, someone else has to lose
   
3. When someone does much for others, they lose
   
4. In most situations, different people’s interests are incompatible
   
5. When one person is winning, it does not mean that someone else is losing [R]
   
6. Life is like a tennis game - A person wins only when another person loses
   
7. One person’s success is not another person’s failure [R]
   
   alpha = 0.88

df_cbzs_elg %>% 
  ggplot(aes(x = zsm)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_cbzs_elg$zsm,na.rm = T),
             color = "grey15",
             size = 1,
             linetype = "dashed") +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Class Solidarity

1. I feel a sense of solidarity with the working class
   
2. I support policy that helps the working class
   
3. I stand united with the working class
   
4. Policies negatively affecting the working class should be changed
   
5. More people should know about how the working class are negatively affected by economic issues
   
6. It’s important to challenge the power structures that disadvantage the working class
   
   alpha = 0.9

df_cbzs_elg %>% 
  ggplot(aes(x = soli)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_cbzs_elg$soli,na.rm = T),
             color = "grey15",
             size = 1,
             linetype = "dashed") +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Policy Support

Participants saw one of four policy descriptions:

Minimum wage: Congress has not increased the federal minimum wage, currently set at 7.25 dollars, since 2009. Some Congresspeople are proposing a policy that would gradually raise the federal minimum wage to 15 dollars an hour by 2025. After 2025, the minimum wage would be adjusted each year to keep pace with growth in the median wage, a measure of wages for typical workers.
Student debt: Some Congresspeople are proposing a policy that would help to address the student loan debt crisis by forgiving up to 50,000 dollars in loans per borrower. Approximately 42 million Americans, or about 1 in 6 American adults, owe a cumulative 1.6 trillion dollars in student loans. Student loans are now the second-largest slice of household debt after mortgages, bigger than credit card debt.
Housing: Some Congresspeople are proposing a housing affordability policy that would help ensure that every American has a place to live. The policy would allow for smaller, lower cost homes like duplexes, townhouses, and garden apartments to be built and developed, allowing new nonprofit homes and reducing overall housing prices.
Climate: Some Congresspeople are proposing a Green New Deal bill which would phase out the use of fossil fuels, with the government providing clean energy jobs for people who can’t find employment in the private sector. All jobs would pay at least 15 dollars an hour, and include healthcare benefits and collective bargaining rights.

Support

To what extent do you oppose or support this policy? (1 = Strongly Oppose to 7 = Strongly Support)

means <- df_cbzs_elg %>%
  dplyr::select(PID,support,policy) %>% 
  filter(!is.na(support)) %>% 
  group_by(policy) %>% 
  summarise(score = mean(support)) %>% 
  ungroup()

df_cbzs_elg %>%
  dplyr::select(PID,support,policy) %>% 
  filter(!is.na(support)) %>% 
  ggplot(aes(x = support)) +
  geom_histogram(fill = "lightblue",
                 color = "black",
                 binwidth = 1) +
  scale_x_continuous(limits = c(0,8),
                     breaks = seq(1,7,1)) +
  geom_vline(data = means,mapping = aes(xintercept = score),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold")) +
  facet_wrap(~policy,nrow = 2)

Analysis

Correlation Matrix: Advantage-gain condition

df_cbzs_elg <- df_cbzs_elg %>% 
  mutate(income_num = as.numeric(income),
         edu_num = as.numeric(edu),
         ses_num = as.numeric(ses))

df_cbzs_elg %>%
  filter(cond == "adgain") %>% 
  dplyr::select(zs_class:support,ideo_con,white,man,wrkclass,income_num:ses_num) %>%
  corPlot(upper = TRUE,stars = TRUE,xsrt = 270)

Correlation Matrix: Disdvantage-gain condition

df_cbzs_elg <- df_cbzs_elg %>% 
  mutate(income_num = as.numeric(income),
         edu_num = as.numeric(edu),
         ses_num = as.numeric(ses))

df_cbzs_elg %>%
  filter(cond == "disadgain") %>% 
  dplyr::select(zs_class:support,ideo_con,white,man,wrkclass,income_num:ses_num) %>%
  corPlot(upper = TRUE,stars = TRUE,xsrt = 270)

One note: I love that they’re both equally correlated with general zero-sum mindset. And yeah, the disadvantage-gain frame is not correlated with anything. V cool.

Analysis Plan

Linear Model 1

Support for policy as the outcome variable; Class Zero-Sum Beliefs as the primary predictor; Condition as the moderator; General zero-sum mindset as a control variable.

m1 <- lm(support ~ zs_class*cond + zsm,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-23)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	3.00	[2.20, 3.79]	7.40	387	< .001
Zs class	0.57	[0.40, 0.73]	6.70	387	< .001
Conddisadgain	2.24	[1.32, 3.16]	4.79	387	< .001
Zsm	0.00	[-0.17, 0.17]	-0.05	387	.961
Zs class \(\times\) Conddisadgain	-0.49	[-0.72, -0.27]	-4.28	387	< .001

df_cbzs_elg %>% 
  ggplot(aes(x = zs_class,y = support,color = cond)) +
  scale_color_manual(values = c("darkblue",
                                "darkred")) +
  geom_jitter(alpha = 0.5) +
  geom_smooth(method = "lm") +
    theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.x = element_text(color = "black",
                                   face = "bold",
                                   size = 12),
        axis.title.x = element_text(color = "black",
                                   face = "bold",
                                   size = 12),
        axis.text.y = element_text(color = "black",
                                   face = "bold",
                                   size = 12),
        axis.title.y = element_text(color = "black",
                                   face = "bold",
                                   size = 12))

that’s a pretty plot.

Linear Model 2

Class solidarity as the outcome variable; Class Zero-Sum Beliefs as the primary predictor; Condition as the moderator; General zero-sum mindset as a control variable.

m1 <- lm(soli ~ zs_class*cond + zsm,data = df_cbzs_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-25)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	5.19	[4.83, 5.56]	27.89	387	< .001
Zs class	0.21	[0.14, 0.29]	5.47	387	< .001
Conddisadgain	1.06	[0.63, 1.48]	4.90	387	< .001
Zsm	-0.06	[-0.14, 0.02]	-1.45	387	.148
Zs class \(\times\) Conddisadgain	-0.26	[-0.37, -0.16]	-5.00	387	< .001

df_cbzs_elg %>% 
  ggplot(aes(x = zs_class,y = soli,color = cond)) +
  scale_color_manual(values = c("darkblue",
                                "darkred")) +
  geom_jitter(alpha = 0.5) +
  geom_smooth(method = "lm") +
    theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.x = element_text(color = "black",
                                   face = "bold",
                                   size = 12),
        axis.title.x = element_text(color = "black",
                                   face = "bold",
                                   size = 12),
        axis.text.y = element_text(color = "black",
                                   face = "bold",
                                   size = 12),
        axis.title.y = element_text(color = "black",
                                   face = "bold",
                                   size = 12))

wow.

Can I also take a moment to acknowledge the main effect of condition? That’s wild. All we changed is the framing of the measure, and look at that effect. That’s very encouraging ahead of our experimental designs.

Moderated Mediation Model

Support for policy as the outcome variable; Class Zero-Sum Beliefs as the primary predictor; Class solidarity as the mediator; General zero-sum mindset as a control variable. Condition as the moderator of the a-path and direct link.

df_cbzs_elg <- df_cbzs_elg %>% 
  mutate(cond_num = ifelse(cond == "adgain",1,0))

fit <- PROCESS(df_cbzs_elg, y="support", x="zs_class",
        meds="soli",
        mods="cond",
        mod.path=c("x-m", "x-y"),
        ci="boot", nsim=100, seed=1)

****************** PART 1. Regression Model Summary ******************

PROCESS Model Code : 8 (Hayes, 2018; www.guilford.com/p/hayes3) PROCESS Model Type : Moderated Mediation - Outcome (Y) : support - Predictor (X) : zs_class - Mediators (M) : soli - Moderators (W) : cond - Covariates (C) : - - HLM Clusters : -

All numeric predictors have been grand-mean centered. (For details, please see the help page of PROCESS.)

Formula of Mediator: - soli ~ zs_classcond Formula of Outcome: - support ~ zs_classcond + soli

CAUTION: Fixed effect (coef.) of a predictor involved in an interaction denotes its “simple effect/slope” at the other predictor = 0. Only when all predictors in an interaction are mean-centered can the fixed effect denote the “main effect”!

Model Summary

───────────────────────────────────────────────────────────── (1) support (2) soli (3) support ───────────────────────────────────────────────────────────── (Intercept) 5.543 *** 5.835 *** 5.252 (0.086) (0.065) (0.126)
zs_class 0.267 0.192 *** 0.377 (0.049) (0.036) (0.073)
conddisadgain 0.044 0.363
(0.093) (0.179)
zs_class:conddisadgain -0.265 * -0.231 *
(0.053) (0.106)
soli 0.980 (0.098)
───────────────────────────────────────────────────────────── R^2 0.070 0.077 0.301
Adj. R^2 0.068 0.070 0.294
Num. obs. 392 392 392
───────────────────────────────────────────────────────────── Note. p < .05, p < .01, *** p < .001.

************ PART 2. Mediation/Moderation Effect Estimate ************

Package Use : ‘mediation’ (v4.5.0), ‘interactions’ (v1.1.5) Effect Type : Moderated Mediation (Model 8) Sample Size : 392 Random Seed : set.seed(1) Simulations : 100 (Bootstrap)

Interaction Effect on “support” (Y) ─────────────────────────────────────── F df1 df2 p
─────────────────────────────────────── zs_class * cond 4.79 1 387 .029 *
───────────────────────────────────────

Simple Slopes: “zs_class” (X) ==> “support” (Y) (Conditional Direct Effects [c’] of X on Y) ───────────────────────────────────────────────────────── “cond” Effect S.E. t p [95% CI] ───────────────────────────────────────────────────────── adgain 0.377 (0.073) 5.183 <.001 *** [ 0.234, 0.521] disadgain 0.146 (0.075) 1.960 .051 . [-0.000, 0.293] ─────────────────────────────────────────────────────────

Interaction Effect on “soli” (M) ──────────────────────────────────────── F df1 df2 p
──────────────────────────────────────── zs_class * cond 25.17 1 388 <.001 *** ────────────────────────────────────────

Simple Slopes: “zs_class” (X) ==> “soli” (M) (Conditional Effects [a] of X on M) ────────────────────────────────────────────────────────── “cond” Effect S.E. t p [95% CI] ────────────────────────────────────────────────────────── adgain 0.192 (0.036) 5.301 <.001 *** [ 0.121, 0.264] disadgain -0.073 (0.038) -1.891 .059 . [-0.148, 0.003] ──────────────────────────────────────────────────────────

Running 100 * 2 simulations… Indirect Path: “zs_class” (X) ==> “soli” (M) ==> “support” (Y) (Conditional Indirect Effects [ab] of X through M on Y) ─────────────────────────────────────────────────────────── “cond” Effect S.E. z p [Boot 95% CI] ─────────────────────────────────────────────────────────── adgain 0.189 (0.043) 4.354 <.001 *** [0.115, 0.266]
disadgain -0.071 (0.035) -2.043 .041 * [-0.142, -0.007] ─────────────────────────────────────────────────────────── Percentile Bootstrap Confidence Interval (SE and CI are estimated based on 100 Bootstrap samples.)

Note. The results based on bootstrapping or other random processes are unlikely identical to other statistical software (e.g., SPSS). To make results reproducible, you need to set a seed (any number). Please see the help page for details: help(PROCESS) Ignore this note if you have already set a seed. :)

fit$results[1] %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

F df1 df2 pval zs_class * cond 4.791892 1 387 0.0291908 cond adgain disadgain “cond” Effect S.E. LLCI ULCI t pval [95% CI] adgain 0.3774501 0.0728197 0.2342783 0.5206219 5.183349 0.0000004 [ 0.234, 0.521] disadgain 0.1463171 0.0746704 -0.0004934 0.2931276 1.959505 0.0507711 [-0.000, 0.293]