# Overview

This documents reanalysis a dataset from an Experiment performed by Singmann and Klauer (2011) using the ANOVA functionality of afex followed by post-hoc tests using package emmeans (Lenth, 2017). After a brief description of the dataset and research question, the code and results are presented.

# Description of Experiment and Data

Singmann and Klauer (2011) were interested in whether or not conditional reasoning can be explained by a single process or whether multiple processes are necessary to explain it. To provide evidence for multiple processes we aimed to establish a double dissociation of two variables: instruction type and problem type. Instruction type was manipulated between-subjects, one group of participants received deductive instructions (i.e., to treat the premises as given and only draw necessary conclusions) and a second group of participants received probabilistic instructions (i.e., to reason as in an everyday situation; we called this “inductive instruction” in the manuscript). Problem type consisted of two different orthogonally crossed variables that were manipulated within-subjects, validity of the problem (formally valid or formally invalid) and plausibility of the problem (inferences which were consisted with the background knowledge versus problems that were inconsistent with the background knowledge). The critical comparison across the two conditions was among problems which were valid and implausible with problems that were invalid and plausible. For example, the next problem was invalid and plausible:

If a person is wet, then the person fell into a swimming pool.
A person fell into a swimming pool.
How valid is the conclusion/How likely is it that the person is wet?

For those problems we predicted that under deductive instructions responses should be lower (as the conclusion does not necessarily follow from the premises) as under probabilistic instructions. For the valid but implausible problem, an example is presented next, we predicted the opposite pattern:

If a person is wet, then the person fell into a swimming pool.
A person is wet.
How valid is the conclusion/How likely is it that the person fell into a swimming pool?

Our study also included valid and plausible and invalid and implausible problems.

In contrast to the analysis reported in the manuscript, we initially do not separate the analysis into affirmation and denial problems, but first report an analysis on the full set of inferences, MP, MT, AC, and DA, where MP and MT are valid and AC and DA invalid. We report a reanalysis of our Experiment 1 only. Note that the factor plausibility is not present in the original manuscript, there it is a results of a combination of other factors.

# Data and R Preperation

library("afex")     # needed for ANOVA functions.
library("emmeans")  # emmeans must now be loaded explicitly for follow-up tests.
library("multcomp") # for advanced control for multiple testing/Type 1 errors.
library("ggplot2")  # for customizing plots.
afex_options(emmeans_model = "multivariate") # use multivariate model for all follow-up tests.

Note that for ANOVAs involving repeated-measures factors, follow-up tests based on the multivariate model are generally preferrably to univariate follow-up tests. Consequently, we set this option globally. Future versions of afex will likely use the multivariate model as the default.

data(sk2011.1)
str(sk2011.1)
## 'data.frame':    640 obs. of  9 variables:
##  $id : Factor w/ 40 levels "8","9","10","12",..: 3 3 3 3 3 3 3 3 3 3 ... ##$ instruction : Factor w/ 2 levels "deductive","probabilistic": 2 2 2 2 2 2 2 2 2 2 ...
##  $plausibility: Factor w/ 2 levels "plausible","implausible": 1 2 2 1 2 1 1 2 1 2 ... ##$ inference   : Factor w/ 4 levels "MP","MT","AC",..: 4 2 1 3 4 2 1 3 4 2 ...
##  $validity : Factor w/ 2 levels "valid","invalid": 2 1 1 2 2 1 1 2 2 1 ... ##$ what        : Factor w/ 2 levels "affirmation",..: 2 2 1 1 2 2 1 1 2 2 ...
##  $type : Factor w/ 2 levels "original","reversed": 2 2 2 2 1 1 1 1 2 2 ... ##$ response    : int  100 60 94 70 100 99 98 49 82 50 ...
##  $content : Factor w/ 4 levels "C1","C2","C3",..: 1 1 1 1 2 2 2 2 3 3 ... An important feature in the data is that each participant provided two responses for each cell of the design (the content is different for each of those, each participant saw all four contents). These two data points will be aggregated automatically by afex. with(sk2011.1, table(inference, id, plausibility)) ## , , plausibility = plausible ## ## id ## inference 8 9 10 12 13 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 ## MP 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ## MT 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ## AC 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ## DA 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ## id ## inference 37 38 39 40 41 42 43 44 46 47 48 49 50 ## MP 2 2 2 2 2 2 2 2 2 2 2 2 2 ## MT 2 2 2 2 2 2 2 2 2 2 2 2 2 ## AC 2 2 2 2 2 2 2 2 2 2 2 2 2 ## DA 2 2 2 2 2 2 2 2 2 2 2 2 2 ## ## , , plausibility = implausible ## ## id ## inference 8 9 10 12 13 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 ## MP 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ## MT 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ## AC 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ## DA 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ## id ## inference 37 38 39 40 41 42 43 44 46 47 48 49 50 ## MP 2 2 2 2 2 2 2 2 2 2 2 2 2 ## MT 2 2 2 2 2 2 2 2 2 2 2 2 2 ## AC 2 2 2 2 2 2 2 2 2 2 2 2 2 ## DA 2 2 2 2 2 2 2 2 2 2 2 2 2 # ANOVA To get the full ANOVA table for the model, we simply pass it to aov_ez using the design as described above. We save the returned object for further analysis. a1 <- aov_ez("id", "response", sk2011.1, between = "instruction", within = c("inference", "plausibility")) ## Warning: More than one observation per cell, aggregating the data using mean (i.e, ## fun_aggregate = mean)! ## Contrasts set to contr.sum for the following variables: instruction a1 # the default print method prints a data.frame produced by nice  ## Anova Table (Type 3 tests) ## ## Response: response ## Effect df MSE F ges p.value ## 1 instruction 1, 38 2027.42 0.31 .003 .583 ## 2 inference 2.66, 101.12 959.12 5.81 ** .063 .002 ## 3 instruction:inference 2.66, 101.12 959.12 6.00 ** .065 .001 ## 4 plausibility 1, 38 468.82 34.23 *** .068 <.001 ## 5 instruction:plausibility 1, 38 468.82 10.67 ** .022 .002 ## 6 inference:plausibility 2.29, 87.11 318.91 2.87 + .009 .055 ## 7 instruction:inference:plausibility 2.29, 87.11 318.91 3.98 * .013 .018 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1 ## ## Sphericity correction method: GG The equivalent calls (i.e., producing exactly the same output) of the other two ANOVA functions aov_car or aov4 is shown below. aov_car(response ~ instruction + Error(id/inference*plausibility), sk2011.1) aov_4(response ~ instruction + (inference*plausibility|id), sk2011.1) As mentioned before, the two responses per cell of the design and participants are aggregated for the analysis as indicated by the warning message. Furthermore, the degrees of freedom are Greenhouse-Geisser corrected per default for all effects involving inference, as inference is a within-subject factor with more than two levels (i.e., MP, MT, AC, & DA). In line with our expectations, the three-way interaction is significant. The object printed per default for afex_aov objects (produced by nice) can also be printed nicely using knitr: knitr::kable(nice(a1)) Effect df MSE F ges p.value instruction 1, 38 2027.42 0.31 .003 .583 inference 2.66, 101.12 959.12 5.81 ** .063 .002 instruction:inference 2.66, 101.12 959.12 6.00 ** .065 .001 plausibility 1, 38 468.82 34.23 *** .068 <.001 instruction:plausibility 1, 38 468.82 10.67 ** .022 .002 inference:plausibility 2.29, 87.11 318.91 2.87 + .009 .055 instruction:inference:plausibility 2.29, 87.11 318.91 3.98 * .013 .018 Alternatively, the anova method for afex_aov objects returns a data.frame of class anova that can be passed to, for example, xtable for nice formatting: print(xtable::xtable(anova(a1), digits = c(rep(2, 5), 3, 4)), type = "html") num Df den Df MSE F ges Pr(>F) instruction 1.00 38.00 2027.42 0.31 0.003 0.5830 inference 2.66 101.12 959.12 5.81 0.063 0.0016 instruction:inference 2.66 101.12 959.12 6.00 0.065 0.0013 plausibility 1.00 38.00 468.82 34.23 0.068 0.0000 instruction:plausibility 1.00 38.00 468.82 10.67 0.022 0.0023 inference:plausibility 2.29 87.11 318.91 2.87 0.009 0.0551 instruction:inference:plausibility 2.29 87.11 318.91 3.98 0.013 0.0177 # Post-Hoc Contrasts and Plotting To further analyze the data we need to pass it to package emmeans, a package that offers great functionality for both plotting and contrasts of all kind. A lot of information on emmeans can be obtained in its vignettes and faq. emmeans can work with afex_aov objects directly as afex comes with the necessary methods for the generic functions defined in emmeans. When using the multivariate options as described above, emmeans uses the ANOVA model estimated via base R’s lm method (which in the case of a multivariate response is an object of class c("mlm", "lm")). In the default setting (i.e., emmeans_model = "univariate"), emmeans uses the object created by base R’s aov function, which for now is also part of an afex_aov object. ## Some First Contrasts ### Main Effects Only This object can now be passed to emmeans, for example to obtain the marginal means of the four inferences: m1 <- emmeans(a1, ~ inference) m1 ## inference emmean SE df lower.CL upper.CL ## MP 87.5 1.80 38 83.9 91.2 ## MT 76.7 4.06 38 68.5 84.9 ## AC 69.4 4.77 38 59.8 79.1 ## DA 83.0 3.84 38 75.2 90.7 ## ## Results are averaged over the levels of: instruction, plausibility ## Confidence level used: 0.95 This object can now also be used to compare whether or not there are differences between the levels of the factor: pairs(m1) ## contrast estimate SE df t.ratio p.value ## MP - MT 10.83 4.33 38 2.501 0.0759 ## MP - AC 18.10 5.02 38 3.607 0.0047 ## MP - DA 4.56 4.20 38 1.086 0.7002 ## MT - AC 7.27 3.98 38 1.825 0.2778 ## MT - DA -6.28 4.70 38 -1.334 0.5473 ## AC - DA -13.54 5.30 38 -2.556 0.0672 ## ## Results are averaged over the levels of: instruction, plausibility ## P value adjustment: tukey method for comparing a family of 4 estimates To obtain more powerful p-value adjustments, we can furthermore pass it to multcomp (Bretz, Hothorn, & Westfall, 2011): summary(as.glht(pairs(m1)), test=adjusted("free")) ## ## Simultaneous Tests for General Linear Hypotheses ## ## Linear Hypotheses: ## Estimate Std. Error t value Pr(>|t|) ## MP - MT == 0 10.831 4.331 2.501 0.05878 . ## MP - AC == 0 18.100 5.018 3.607 0.00469 ** ## MP - DA == 0 4.556 4.196 1.086 0.31350 ## MT - AC == 0 7.269 3.984 1.825 0.19418 ## MT - DA == 0 -6.275 4.703 -1.334 0.31350 ## AC - DA == 0 -13.544 5.299 -2.556 0.05878 . ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## (Adjusted p values reported -- free method) ### A Simple interaction We could now also be interested in the marginal means of the inferences across the two instruction types. emmeans offers two ways to do so. The first splits the contrasts across levels of the factor using the by argument. m2 <- emmeans(a1, "inference", by = "instruction") ## equal: emmeans(a1, ~ inference|instruction) m2 ## instruction = deductive: ## inference emmean SE df lower.CL upper.CL ## MP 97.3 2.54 38 92.1 102.4 ## MT 70.4 5.75 38 58.8 82.0 ## AC 61.5 6.75 38 47.8 75.1 ## DA 81.8 5.43 38 70.8 92.8 ## ## instruction = probabilistic: ## inference emmean SE df lower.CL upper.CL ## MP 77.7 2.54 38 72.6 82.9 ## MT 83.0 5.75 38 71.3 94.6 ## AC 77.3 6.75 38 63.7 91.0 ## DA 84.1 5.43 38 73.1 95.1 ## ## Results are averaged over the levels of: plausibility ## Confidence level used: 0.95 Consequently, tests are also only performed within each level of the by factor: pairs(m2) ## instruction = deductive: ## contrast estimate SE df t.ratio p.value ## MP - MT 26.89 6.13 38 4.389 0.0005 ## MP - AC 35.80 7.10 38 5.045 0.0001 ## MP - DA 15.47 5.93 38 2.608 0.0599 ## MT - AC 8.91 5.63 38 1.582 0.4007 ## MT - DA -11.41 6.65 38 -1.716 0.3297 ## AC - DA -20.32 7.49 38 -2.712 0.0471 ## ## instruction = probabilistic: ## contrast estimate SE df t.ratio p.value ## MP - MT -5.22 6.13 38 -0.853 0.8287 ## MP - AC 0.40 7.10 38 0.056 0.9999 ## MP - DA -6.36 5.93 38 -1.072 0.7084 ## MT - AC 5.62 5.63 38 0.998 0.7512 ## MT - DA -1.14 6.65 38 -0.171 0.9982 ## AC - DA -6.76 7.49 38 -0.902 0.8036 ## ## Results are averaged over the levels of: plausibility ## P value adjustment: tukey method for comparing a family of 4 estimates The second version considers all factor levels together. Consequently, the number of pairwise comparisons is a lot larger: m3 <- emmeans(a1, c("inference", "instruction")) ## equal: emmeans(a1, ~inference*instruction) m3 ## inference instruction emmean SE df lower.CL upper.CL ## MP deductive 97.3 2.54 38 92.1 102.4 ## MT deductive 70.4 5.75 38 58.8 82.0 ## AC deductive 61.5 6.75 38 47.8 75.1 ## DA deductive 81.8 5.43 38 70.8 92.8 ## MP probabilistic 77.7 2.54 38 72.6 82.9 ## MT probabilistic 83.0 5.75 38 71.3 94.6 ## AC probabilistic 77.3 6.75 38 63.7 91.0 ## DA probabilistic 84.1 5.43 38 73.1 95.1 ## ## Results are averaged over the levels of: plausibility ## Confidence level used: 0.95 pairs(m3) ## contrast estimate SE df t.ratio p.value ## MP deductive - MT deductive 26.89 6.13 38 4.389 0.0020 ## MP deductive - AC deductive 35.80 7.10 38 5.045 0.0003 ## MP deductive - DA deductive 15.47 5.93 38 2.608 0.1848 ## MP deductive - MP probabilistic 19.55 3.59 38 5.439 0.0001 ## MP deductive - MT probabilistic 14.32 6.29 38 2.279 0.3310 ## MP deductive - AC probabilistic 19.95 7.21 38 2.767 0.1342 ## MP deductive - DA probabilistic 13.19 5.99 38 2.201 0.3741 ## MT deductive - AC deductive 8.91 5.63 38 1.582 0.7577 ## MT deductive - DA deductive -11.41 6.65 38 -1.716 0.6772 ## MT deductive - MP probabilistic -7.34 6.29 38 -1.167 0.9363 ## MT deductive - MT probabilistic -12.56 8.13 38 -1.545 0.7783 ## MT deductive - AC probabilistic -6.94 8.86 38 -0.783 0.9931 ## MT deductive - DA probabilistic -13.70 7.91 38 -1.733 0.6666 ## AC deductive - DA deductive -20.32 7.49 38 -2.712 0.1501 ## AC deductive - MP probabilistic -16.25 7.21 38 -2.254 0.3446 ## AC deductive - MT probabilistic -21.48 8.86 38 -2.423 0.2600 ## AC deductive - AC probabilistic -15.85 9.54 38 -1.661 0.7111 ## AC deductive - DA probabilistic -22.61 8.66 38 -2.611 0.1834 ## DA deductive - MP probabilistic 4.08 5.99 38 0.680 0.9971 ## DA deductive - MT probabilistic -1.15 7.91 38 -0.145 1.0000 ## DA deductive - AC probabilistic 4.47 8.66 38 0.517 0.9995 ## DA deductive - DA probabilistic -2.29 7.68 38 -0.298 1.0000 ## MP probabilistic - MT probabilistic -5.22 6.13 38 -0.853 0.9885 ## MP probabilistic - AC probabilistic 0.40 7.10 38 0.056 1.0000 ## MP probabilistic - DA probabilistic -6.36 5.93 38 -1.072 0.9588 ## MT probabilistic - AC probabilistic 5.62 5.63 38 0.998 0.9719 ## MT probabilistic - DA probabilistic -1.14 6.65 38 -0.171 1.0000 ## AC probabilistic - DA probabilistic -6.76 7.49 38 -0.902 0.9840 ## ## Results are averaged over the levels of: plausibility ## P value adjustment: tukey method for comparing a family of 8 estimates ### Running Custom Contrasts Objects returned from emmeans can also be used to test specific contrasts. For this, we can simply create a list, where each element corresponds to one contrasts. A contrast is defined as a vector of constants on the reference grid (i.e., the object returned from emmeans, here m3). For example, we might be interested in whether there is a difference between the valid and invalid inferences in each of the two conditions. c1 <- list( v_i.ded = c(0.5, 0.5, -0.5, -0.5, 0, 0, 0, 0), v_i.prob = c(0, 0, 0, 0, 0.5, 0.5, -0.5, -0.5) ) contrast(m3, c1, adjust = "holm") ## contrast estimate SE df t.ratio p.value ## v_i.ded 12.194 4.12 38 2.960 0.0105 ## v_i.prob -0.369 4.12 38 -0.090 0.9291 ## ## Results are averaged over the levels of: plausibility ## P value adjustment: holm method for 2 tests summary(as.glht(contrast(m3, c1)), test = adjusted("free")) ## ## Simultaneous Tests for General Linear Hypotheses ## ## Linear Hypotheses: ## Estimate Std. Error t value Pr(>|t|) ## v_i.ded == 0 12.1937 4.1190 2.96 0.0105 * ## v_i.prob == 0 -0.3687 4.1190 -0.09 0.9291 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## (Adjusted p values reported -- free method) The results can be interpreted as in line with expectations. Responses are larger for valid than invalid problems in the deductive, but not the probabilistic condition. ## Plotting Since version 0.22, afex comes with its own plotting function based on ggplot2, afex_plot, which works directly with afex_aov objects. As said initially, we are interested in the three-way interaction of instruction with inference, plausibility, and instruction. As we saw above, this interaction was significant. Consequently, we are interested in plotting this interaction. ### Basic Plots For afex_plot, we need to specify the x-factor(s), which determine which factor-levels or combinations of factor-levels are plotted on the x-axis. We can also define trace factor(s), which determine which factor levels are connected by lines. Finally, we can also define panel factor(s), which determine if the plot is split into subplots. afex_plot then plots the estimated marginal means obtained from emmeans, confidence intervals, and the raw data in the background. Note that the raw data in the background is per default drawn using an alpha blending of .5 (i.e., 50% semi-transparency). Thus, in case of several points lying directly on top of each other, this point appears noticeably darker. afex_plot(a1, x = "inference", trace = "instruction", panel = "plausibility") ## Warning: Panel(s) show a mixed within-between-design. ## Error bars do not allow comparisons across all means. ## Suppress error bars with: error = "none" In the default settings, the error bars show 95%-confidence intervals based on the standard error of the underlying model (i.e., the lm model in the present case). In the present case, in which each subplot (defined by x- and trace-factor) shows a combination of a within-subjects factor (i.e., inference) and a between-subjects (i.e., instruction) factor, this is not optimal. The error bars only allow to assess differences regarding the between-subjects factor (i.e., across the lines), but not inferences regarding the within-subjects factor (i.e., within one line). This is also indicated by a warning. An alternative would be within-subject confidence intervals: afex_plot(a1, x = "inference", trace = "instruction", panel = "plausibility", error = "within") ## Warning: Panel(s) show a mixed within-between-design. ## Error bars do not allow comparisons across all means. ## Suppress error bars with: error = "none" However, those only allow inferences regarding the within-subject factors and not regarding the between-subjecta factor. So the same warning is emitted again. A further alternative is to suppress the error bars altogether. This is the approach used in our original paper and probably a good idea in general when figures show both between- and within-subjects factors within the same panel. The presence of the raw data in the background still provides a visual depiction of the variability of the data. afex_plot(a1, x = "inference", trace = "instruction", panel = "plausibility", error = "none") ### Customizing Plots afex_plot allows to customize the plot in a number of different ways. For example, we can easily change the aesthetic mapping associated with the trace factor. So instead of using lineytpe and shape of the symbols, we can use color. Furthermore, we can change the graphical element used for plotting the data points in the background. For example, instead of plotting the raw data, we can replace this with a boxplot. Finally, we can also make both the points showing the means and the lines connecting the means larger. p1 <- afex_plot(a1, x = "inference", trace = "instruction", panel = "plausibility", error = "none", mapping = c("color", "fill"), data_geom = geom_boxplot, data_arg = list(width = 0.4), point_arg = list(size = 1.5), line_arg = list(size = 1)) p1 Note that afex_plot returns a ggplot2 plot object which can be used for further customization. For example, one can easily change the theme to something that does not have a grey background: p1 + theme_light() We can also set the theme globally for the remainder of the R session. theme_set(theme_light()) The full set of customizations provided by afex_plot is beyond the scope of this vignette. The examples on the help page at ?afex_plot provide a good overview. # Replicate Analysis from Singmann and Klauer (2011) However, the plots shown so far are not particularly helpful with respect to the research question. Next, we fit a new ANOVA model in which we separate the data in affirmation and denial inferences. This was also done in the original manuscript. We then lot the data a second time. a2 <- aov_ez("id", "response", sk2011.1, between = "instruction", within = c("validity", "plausibility", "what")) ## Warning: More than one observation per cell, aggregating the data using mean (i.e, ## fun_aggregate = mean)! ## Contrasts set to contr.sum for the following variables: instruction a2 ## Anova Table (Type 3 tests) ## ## Response: response ## Effect df MSE F ges p.value ## 1 instruction 1, 38 2027.42 0.31 .003 .583 ## 2 validity 1, 38 678.65 4.12 * .013 .049 ## 3 instruction:validity 1, 38 678.65 4.65 * .014 .037 ## 4 plausibility 1, 38 468.82 34.23 *** .068 <.001 ## 5 instruction:plausibility 1, 38 468.82 10.67 ** .022 .002 ## 6 what 1, 38 660.52 0.22 <.001 .640 ## 7 instruction:what 1, 38 660.52 2.60 .008 .115 ## 8 validity:plausibility 1, 38 371.87 0.14 <.001 .715 ## 9 instruction:validity:plausibility 1, 38 371.87 4.78 * .008 .035 ## 10 validity:what 1, 38 1213.14 9.80 ** .051 .003 ## 11 instruction:validity:what 1, 38 1213.14 8.60 ** .045 .006 ## 12 plausibility:what 1, 38 204.54 9.97 ** .009 .003 ## 13 instruction:plausibility:what 1, 38 204.54 5.23 * .005 .028 ## 14 validity:plausibility:what 1, 38 154.62 0.03 <.001 .855 ## 15 instruction:validity:plausibility:what 1, 38 154.62 0.42 <.001 .521 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1 Then we plot the data from this ANOVA. Because each panel would again show a mixed-design, we suppress the error bars. afex_plot(a2, x = c("plausibility", "validity"), trace = "instruction", panel = "what", error = "none") We see the critical and predicted cross-over interaction in the left of those two graphs. For implausible but valid problems deductive responses are larger than probabilistic responses. The opposite is true for plausible but invalid problems. We now tests these differences at each of the four x-axis ticks in each plot using custom contrasts (diff_1 to diff_4). Furthermore, we test for a validity effect and plausibility effect in both conditions. (m4 <- emmeans(a2, ~instruction+plausibility+validity|what)) ## what = affirmation: ## instruction plausibility validity emmean SE df lower.CL upper.CL ## deductive plausible valid 99.5 1.16 38 97.1 101.8 ## probabilistic plausible valid 95.3 1.16 38 93.0 97.6 ## deductive implausible valid 95.1 5.01 38 85.0 105.2 ## probabilistic implausible valid 60.2 5.01 38 50.0 70.3 ## deductive plausible invalid 67.0 6.95 38 52.9 81.0 ## probabilistic plausible invalid 90.5 6.95 38 76.5 104.6 ## deductive implausible invalid 56.0 7.97 38 39.9 72.2 ## probabilistic implausible invalid 64.1 7.97 38 48.0 80.3 ## ## what = denial: ## instruction plausibility validity emmean SE df lower.CL upper.CL ## deductive plausible valid 70.5 6.18 38 58.0 83.1 ## probabilistic plausible valid 93.0 6.18 38 80.5 105.5 ## deductive implausible valid 70.2 6.36 38 57.4 83.1 ## probabilistic implausible valid 73.0 6.36 38 60.1 85.8 ## deductive plausible invalid 86.5 5.32 38 75.8 97.3 ## probabilistic plausible invalid 87.5 5.32 38 76.7 98.2 ## deductive implausible invalid 77.1 6.62 38 63.7 90.5 ## probabilistic implausible invalid 80.8 6.62 38 67.4 94.1 ## ## Confidence level used: 0.95 c2 <- list( diff_1 = c(1, -1, 0, 0, 0, 0, 0, 0), diff_2 = c(0, 0, 1, -1, 0, 0, 0, 0), diff_3 = c(0, 0, 0, 0, 1, -1, 0, 0), diff_4 = c(0, 0, 0, 0, 0, 0, 1, -1), val_ded = c(0.5, 0, 0.5, 0, -0.5, 0, -0.5, 0), val_prob = c(0, 0.5, 0, 0.5, 0, -0.5, 0, -0.5), plau_ded = c(0.5, 0, -0.5, 0, -0.5, 0, 0.5, 0), plau_prob = c(0, 0.5, 0, -0.5, 0, 0.5, 0, -0.5) ) contrast(m4, c2, adjust = "holm") ## what = affirmation: ## contrast estimate SE df t.ratio p.value ## diff_1 4.175 1.64 38 2.543 0.0759 ## diff_2 34.925 7.08 38 4.931 0.0001 ## diff_3 -23.600 9.83 38 -2.401 0.0854 ## diff_4 -8.100 11.28 38 -0.718 0.9538 ## val_ded 35.800 7.10 38 5.045 0.0001 ## val_prob 0.400 7.10 38 0.056 0.9553 ## plau_ded -3.275 3.07 38 -1.068 0.8761 ## plau_prob 30.775 4.99 38 6.164 <.0001 ## ## what = denial: ## contrast estimate SE df t.ratio p.value ## diff_1 -22.425 8.74 38 -2.565 0.1007 ## diff_2 -2.700 8.99 38 -0.300 1.0000 ## diff_3 -0.925 7.52 38 -0.123 1.0000 ## diff_4 -3.650 9.36 38 -0.390 1.0000 ## val_ded -11.412 6.65 38 -1.716 0.5658 ## val_prob -1.137 6.65 38 -0.171 1.0000 ## plau_ded -4.562 4.11 38 -1.109 1.0000 ## plau_prob 13.363 2.96 38 4.519 0.0005 ## ## P value adjustment: holm method for 8 tests We can also pass these tests to multcomp which gives us more powerful Type 1 error corrections. summary(as.glht(contrast(m4, c2)), test = adjusted("free")) ## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps

## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps ## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps

## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps ## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps

## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps ##$what = affirmation
##
##   Simultaneous Tests for General Linear Hypotheses
##
## Linear Hypotheses:
##                Estimate Std. Error t value Pr(>|t|)
## diff_1 == 0       4.175      1.641   2.543   0.0652 .
## diff_2 == 0      34.925      7.082   4.931 9.68e-05 ***
## diff_3 == 0     -23.600      9.830  -2.401   0.0708 .
## diff_4 == 0      -8.100     11.275  -0.718   0.6882
## val_ded == 0     35.800      7.096   5.045 6.48e-05 ***
## val_prob == 0     0.400      7.096   0.056   0.9553
## plau_ded == 0    -3.275      3.065  -1.068   0.6036
## plau_prob == 0   30.775      4.992   6.164 1.72e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- free method)
##
##
## \$what = denial
##
##   Simultaneous Tests for General Linear Hypotheses
##
## Linear Hypotheses:
##                Estimate Std. Error t value Pr(>|t|)
## diff_1 == 0     -22.425      8.742  -2.565 0.081050 .
## diff_2 == 0      -2.700      8.987  -0.300 0.984917
## diff_3 == 0      -0.925      7.522  -0.123 0.984917
## diff_4 == 0      -3.650      9.358  -0.390 0.984917
## val_ded == 0    -11.412      6.651  -1.716 0.379251
## val_prob == 0    -1.137      6.651  -0.171 0.984917
## plau_ded == 0    -4.562      4.115  -1.109 0.725899
## plau_prob == 0   13.363      2.957   4.519 0.000518 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- free method)

Unfortunately, in the present case this function throws several warnings. Nevertheless, the p-values from both methods are very similar and agree on whether or not they are below or above .05. Because of the warnings it seems advisable to use the one provided by emmeans directly and not use the ones from multcomp.

The pattern for the affirmation problems is in line with the expectations: We find the predicted differences between the instruction types for valid and implausible (diff_2) and invalid and plausible (diff_3) and the predicted non-differences for the other two problems (diff_1 and diff_4). Furthermore, we find a validity effect in the deductive but not in the probabilistic condition. Likewise, we find a plausibility effect in the probabilistic but not in the deductive condition.

# Some Cautionary Notes

• Choosing the right correction for multiple testing can be difficult. In fact multcomp comes with an accompanying book (Bretz et al., 2011). If the degrees-of-freedom of all contrasts are identical using multcomp’s method free is more powerful than simply using the Bonferroni-Holm method. free is a generalization of the Bonferroni-Holm method that takes the correlations among the model parameters into account and uniformly more powerful.
• For data sets with many within-subject factors, creating the aov object can take some time (i.e., compared to producing the ANOVA table which is usually very fast). Those objects are also comparatively large, often multiple MB. If speed is important and one is not interested in employing emmeans one can set return = "nice" in the call to the ANOVA function to only receive the ANOVA table (this can also be done globally: afex_options(return_aov = "nice")).

# References

• Bretz, F., Hothorn, T., & Westfall, P. H. (2011). Multiple comparisons using R. Boca Raton, FL: CRC Press. https://CRAN.R-project.org/package=multcomp
• Singmann, H., & Klauer, K. C. (2011). Deductive and inductive conditional inferences: Two modes of reasoning. Thinking & Reasoning, 17(3), 247-281. doi: 10.1080/13546783.2011.572718
• Lenth, R. (2017). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 0.9.1. https://CRAN.R-project.org/package=emmeans