Labcoat Leni solutions Chapter 13

Don’t forget your toothbrush

Load the data

To load the data from the CSV file (assuming you have set up a project folder as suggested in the book) and set the factor and its levels:

davey_tib <- readr::read_csv("../data/davey_2003.csv") %>% 
  dplyr::mutate(
    stop_rule = forcats::as_factor(stop_rule),
    mood = forcats::as_factor(mood) %>% forcats::fct_relevel(., "Neutral")
  )

Alternative, load the data directly from the discovr package:

davey_tib <- discovr::davey_2003

Plot the data

ggplot2::ggplot(davey_tib, aes(x = mood, y = checks, colour = stop_rule)) +
  geom_violin(alpha = 0.5) +
  stat_summary(fun.data = "mean_cl_normal", position = position_dodge(width = 0.9)) +
  scale_y_continuous(breaks = seq(0, 40, 5)) +
  labs(y = "Checks (out of 10)", x = "Mood induction", colour = "Stop rule") +
  discovr::scale_colour_ssoass() +
  theme_minimal()

The plot shows that when in a negative mood people performed more checks when using an as many as can stop rule than when using a feel like continuing stop rule. In a positive mood the opposite was true, and in neutral moods the number of checks was very similar in the two stop rule conditions.

Fit the model

Fitting the model using afex::aov_4()

davey_afx <- afex::aov_4(checks ~ mood*stop_rule + (1|id), data = davey_tib)
davey_afx

## Anova Table (Type 3 tests)
## 
## Response: checks
##           Effect    df   MSE       F  ges p.value
## 1           mood 2, 54 24.97    0.68 .025    .509
## 2      stop_rule 1, 54 24.97    2.09 .037    .154
## 3 mood:stop_rule 2, 54 24.97 6.35 ** .190    .003
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Fitting the model using lm()

Set contrasts for stop_rule:

many_vs_feel <- c(-0.5, 0.5)
contrasts(davey_tib$stop_rule) <- many_vs_feel

Set contrasts for mood. What we really want here is a contrast that compares positive mood to neutral and negative mood to neutral, but this contrast isn’t orthogonal and we need orthogonal contrasts for the type III sums of squares. Instead we’ll use the build in contr.sum, which will give us sum to zero contrasts, which are orthogonal.

contrasts(davey_tib$mood) <- contr.sum(3)

Fit the model and print Type III sums of squares:

davey_lm <- lm(checks ~ mood*stop_rule, data = davey_tib)
car::Anova(davey_lm, type = 3)

## Anova Table (Type III tests)
## 
## Response: checks
##                Sum Sq Df  F value    Pr(>F)    
## (Intercept)    6448.1  1 258.1904 < 2.2e-16 ***
## mood             34.1  2   0.6834  0.509222    
## stop_rule        52.3  1   2.0928  0.153771    
## mood:stop_rule  316.9  2   6.3452  0.003349 ** 
## Residuals      1348.6 54                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The resulting output can be interpreted as follows.

• The main effect of mood was not significant, F(2, 54) = 0.68, p = .51, indicating that the number of checks (when we ignore the stop rule adopted) was roughly the same regardless of whether the person was in a positive, negative or neutral mood.
• Similarly, the main effect of stop rule was not significant, F(1, 54) = 2.09, p = .15, indicating that the number of checks (when we ignore the mood induced) was roughly the same regardless of whether the person used an ‘as many as can’ or a ‘feel like continuing’ stop rule.
• The mood × stop rule interaction was significant, F(2, 54) = 6.35, p = .003, indicating that the mood combined with the stop rule significantly affected checking behaviour. Looking at the graph, a negative mood in combination with an ‘as many as can’ stop rule increased checking, as did the combination of a ‘feel like continuing’ stop rule and a positive mood, just as Davey et al. predicted.