APA Tables Part 1

Author

Emorie D Beck

Workspace

Packages

Code

library(knitr)
library(lme4)
library(kableExtra)
library(psych)
library(broom)
library(plyr)
library(tidyverse)

Data

The data we’re going to use are from the teaching sample from the German Socioeconomic Panel Study. These data have been pre-cleaned (see earlier workshop on workflow and creating guidelines for tips).

The data we’ll use fall into three categories:
1. Personality trait composites: Negative Affect, Positive Affect, Self-Esteem, CESD Depression, and Optimism. These were cleaned, reversed coded, and composited prior to being included in this final data set.
2. Outcomes: Moving in with a partner, marriage, divorce, and child birth. These were cleaned, coded as 1 (occurred) or 0 (did not occur) according to whether an outcome occurred for each individual or not after each possible measured personality year. Moreover, people who experienced these outcomes prior to the target personality year are excluded.
3. Covariates: Age, gender (0 = male, 1 = female, education (0 = high school or below, 1 = college, 2 = higher than college), gross wages, self-rated health, smoking (0 = never smoked 1 = ever smoked), exercise, BMI, religion, parental education, and parental occupational prestige (ISEI). Each of these were composited for all available data up to the measured personality years.

First, the code below will download a .zip file when you run it. Once, you do, navigate to your Desktop to unzip the folder. You should now be able to run the rest of the code.

Code

data_source <- "https://github.com/emoriebeck/R-tutorials/raw/master/09_tables-p1/09_tables-p1.zip"
data_dest <- "~/Desktop/09_tables-p1.zip"
download.file(data_source, data_dest)
unzip(data_dest, exdir = "~/Desktop")

Code

wd <- "~/Desktop/tables"
(gsoep <- sprintf("%s/data/gsoep.csv", wd) %>% read_csv())

APA Tables

In psychology, we must work within the confines of APA style. Although these guidelines have been updated, the style guide remains quite similar to earlier guidelines with respect to tables.

But psychology research is heterogeneous and expectations for modern tables require combining multiple models in creative ways.

Small tweaks to data or model arguments can spell disaster for creating a table. It’s easy to make mistakes in copying values or matching different models to their respective rows and columns.

Thankfully, the increasing popularity of R has been coupled with more methods for creating a reproducible workflow that includes tables.

Outline

In this tutorial, we will directly cover 3 different use cases, while a few others will be included in supplementary materials.

Personally, I favor non-automated tools, so we will cover the following packages:
- kable + kableExtra (html and LaTeX)
- papaja

Using these packages will build on earlier tutorials using tidyr, dplyr, workflow, and purrr and round out our discuss on data presentation using ggplot2.

For less flexible but more accessible tables see:
- apaTable
- sjPlot
- corrplot

Important Tools

Although it doesn’t cover all models, the broom and broom.mixed family of packages will provide easy to work with estimates of nearly all types of models and will also provide the model terms that are ideal for most APA tables, including estimates, standard errors, and confidence intervals.

lavaan models are slightly more complicated, but it’s actually relatively easy to deal with them (and how to extract their terms), assuming that you understand the models you are running.

Basic Lessons: One DV/Outcome, Multiple Model Terms

We’ll start with a basic case, predicting who has a child from personality, both with and without control variables.

Becauce outcome variables are binary, we’ll use logistic regression.

The basic form of the model is: \(\log\Big(\frac{p_i}{1-p_i}\Big) = b_0 + b_1X_1 + b_2X_2 ... b_pXp\)

In other words, we’re predicting the log odds of having a child from a linear combination of predictor variables.

Set up the data frame

First, we’ll use some of what we learned in the purrr workshop to set ourselves up to be able to create these tables easily, using group_by() and nest() to create nested data frames for our target personality + outcome combinations. To do this, we’ll also use what you learned about filter() and mutate().

Code

gsoep_nested1 <- gsoep %>%
  filter(Outcome == "chldbrth") %>%
  group_by(Trait, Outcome) %>%
  nest() %>%
  ungroup()
gsoep_nested1

First, let’s pause and see what we have. We now have a data frame with 3 columns (Outcome, Trait, and data) and 4 rows. The data column is of class list, meaning it’s a “list column” that contains a tibble in each cell. This means that we can use purrr functions to run operations on each of these data frames individually but without having to copy and paste the same operation multiple times for each model we want to run.

Run Models

To run the models, I like to write short functions that are easier to read than including a local function within a call to purrr::map(). Here, we’re just going to write a simple function to predict child birth from personality.

Code

mod1_fun <- function(d){
  d$o_value <- factor(d$o_value)
  glm(o_value ~ p_value, data = d, family = binomial(link = "logit"))
}

gsoep_nested1 <- gsoep_nested1 %>%
  mutate(m = map(data, mod1_fun))
gsoep_nested1

Now, when we look at the nested frame, we see an additional column, which is also a list, but this column contains <glm> objects rather than tibbles.

Get Key Terms

Now that we have the models, we want to get our key terms. I’m a big fan of using the function tidy from the broom package to do this. Bonus because it plays nicely with purrr. Double bonus because it will give us confidence intervals, which I generally prefer over p-values and standard erorrs because I find them more informative.

Code

gsoep_nested1 <- gsoep_nested1 %>%
  mutate(tidy = map(m, ~tidy(., conf.int = T)))
gsoep_nested1

Note that what I’ve used here is a local function, meaning that I’ve used the notation ~function(., arguments). The tilda tells R we want a local function, and the . tells R to use the mapped m column as the function input.

Now we have a fifth column, which is a list column called tidy that contains a tibble, just like the data column.

Creating a Table

Now we are finally ready to create a table! I’m going to use kable + kableExtra to do this in steps.

First, we’ll unnest the tidy column from our data frame. Before doing so, we will drop the data and m columns because they’ve done their work for now.

Code

tidy1 <- gsoep_nested1 %>%
  select(-data, -m) %>%
  unnest(tidy)
tidy1

As you can see, we now have lots of information about our model terms, which are already nicely indexed by Outcome and Trait combinations.

But before we’re ready to create a table, we have to make a few considerations: - What is our target term? In this case “p_value” which is the change in log odds associated with a 1 unit increase/decrease in p_value.

Code

tidy1 <- tidy1 %>% filter(term == "p_value")
tidy1

How will we denote significance? In this case, we’ll use confidence intervals whose signs match. We’ll then bold these terms for our table.

Code

tidy1 <- tidy1 %>% mutate(sig = ifelse(sign(conf.low) == sign(conf.high), "sig", "ns"))
tidy1

What is the desired final structure for the table? I’d like columns for Trait, estimate (b), and confidence intervals (CI) formatted to two decimal places and bolded if significant. I’d also like a span header denoting that the outcome measure is child birth.

Before we do this, though, we need to convert our log odds to odds ratios, using the exp() function.

Code

tidy1 <- tidy1 %>%
  mutate_at(vars(estimate, conf.low, conf.high), exp) 
tidy1

Now, we can format them.

Code

tidy1 <- tidy1 %>%
  mutate_at(vars(estimate, conf.low, conf.high), ~sprintf("%.2f", .)) 
tidy1

sprintf() is my favorite base R formatting function. “%.2f” means I’m asking it to take a floating point number and include 2 digits after the “.” and 0 before. We can now see that the estimate, conf.low, and conf.high columns are of class <chr> instead of <dbl>.

But now we need to create our confidence intervals.

Code

tidy1 <- tidy1 %>%
  mutate(CI = sprintf("[%s, %s]", conf.low, conf.high))
tidy1

And bold the significant confidence intervals and estimates.

Code

tidy1 <- tidy1 %>%
  mutate_at(vars(estimate, CI), ~ifelse(sig == "sig", sprintf("<strong>%s</strong>", .), .))
tidy1

This reads as “for both the estimate and the CI columns, if the sig column is equal to”sig”, then let’s format it as bold using html. Otherwise, let’s leave it alone.” And indeed, we can see that the final result formats 3/4 rows.

Thankfully, these can be achieved without considerable reshaping of the data, which is why we’ve started here, so we’re almost done. We just need to get rid of some unnecessary columnns.

Code

tidy1 <- tidy1 %>%
  select(Trait, OR = estimate, CI)

Because we just have one target term and one outcome, we don’t need those columns, so we’re just keeping Trait, OR, which I renamed as such within in the select call, and CI.

Now let’s kable. You’ve likely used the kable() function from the knitr before. It’s a very useful and simple function in most occasions.

Code

kable(tidy1)

Trait	OR	CI
OP	<strong>1.68</strong>	<strong>[1.50, 1.88]</strong>
DEP	<strong>1.15</strong>	<strong>[1.07, 1.24]</strong>
NegAff	<strong>1.11</strong>	<strong>[1.01, 1.22]</strong>
PA	<strong>1.97</strong>	<strong>[1.77, 2.20]</strong>
SE	1.09	[0.97, 1.22]

It will automatically generate the html code needed to create a table. But if we look closely at the code, it gives us some gobbledigook where we inputted html, so we need a way around that. I’m also going to throw in kable_styling(full_width = F) from the kableExtra package to help out here. It’s not doing much, but it will make the formatted table print in your Viewer.

Code

kable(tidy1, escape = F) %>%
  kable_styling(full_width = F)

Trait	OR	CI
OP	1.68	[1.50, 1.88]
DEP	1.15	[1.07, 1.24]
NegAff	1.11	[1.01, 1.22]
PA	1.97	[1.77, 2.20]
SE	1.09	[0.97, 1.22]

Much better. But this still doesn’t look like an APA table, so let’s keep going.

APA tables usually write out long names for our predictors, so let’s change those first. I’m going to create a reference tibble and use mapvalues() from the plyr function for this.

Code

p_names <- tibble(
  old = c("NegAff", "PA", "SE", "OP", "DEP"),
  new = c("Negative Affect", "Positive Affect", "Self-Esteem", "Optimism", "Depression")
)

tidy1 %>%
  mutate(Trait = mapvalues(Trait, from = p_names$old, to = p_names$new),
         Trait = factor(Trait, levels = p_names$new)) %>%
  arrange(Trait) %>%
  kable(., escape = F) %>%
  kable_styling(full_width = F)

Trait	OR	CI
Negative Affect	1.11	[1.01, 1.22]
Positive Affect	1.97	[1.77, 2.20]
Self-Esteem	1.09	[0.97, 1.22]
Optimism	1.68	[1.50, 1.88]
Depression	1.15	[1.07, 1.24]

The combinatin of factor plus arrange here is super helpful for ordering your table.

The alignment of the columns isn’t quite right. Let’s fix that. We’ll change the trait to right justified and b and CI to centered.

Code

tidy1 %>%
  mutate(Trait = mapvalues(Trait, from = p_names$old, to = p_names$new),
         Trait = factor(Trait, levels = p_names$new)) %>%
  arrange(Trait) %>%
  kable(., escape = F,
        align = c("r", "c", "c")) %>%
  kable_styling(full_width = F)

Trait	OR	CI
Negative Affect	1.11	[1.01, 1.22]
Positive Affect	1.97	[1.77, 2.20]
Self-Esteem	1.09	[0.97, 1.22]
Optimism	1.68	[1.50, 1.88]
Depression	1.15	[1.07, 1.24]

But we’re still missing our span header. There’s a great function in the kableExtra package for this add_header_above. This function takes a named vector as argument, where the elements of the vector refer to the number of columns the named element should span.

Code

tidy1 %>%
  mutate(Trait = mapvalues(Trait, from = p_names$old, to = p_names$new),
         Trait = factor(Trait, levels = p_names$new)) %>%
  arrange(Trait) %>%
  kable(., escape = F,
        align = c("r", "c", "c")) %>%
  kable_styling(full_width = F) %>%
  add_header_above(c(" " = 1, "Birth of a Child" = 2))

	Birth of a Child
Trait	OR	CI
Negative Affect	1.11	[1.01, 1.22]
Positive Affect	1.97	[1.77, 2.20]
Self-Esteem	1.09	[0.97, 1.22]
Optimism	1.68	[1.50, 1.88]
Depression	1.15	[1.07, 1.24]

Note that what the " " = 1 does is skip the Trait column. This is very useful because it let’s us not have a span header over every column.

APA style requires we note how we denote significance and have a title, so let’s add a title and a note.

Code

tidy1 %>%
  mutate(Trait = mapvalues(Trait, from = p_names$old, to = p_names$new),
         Trait = factor(Trait, levels = p_names$new)) %>%
  arrange(Trait) %>%
  kable(., escape = F,
        align = c("r", "c", "c"),
        caption = "<strong>Table 1</strong><br><em>Estimated Personality-Outcome Associations</em>") %>%
  kable_styling(full_width = F) %>%
  add_header_above(c(" " = 1, "Birth of a Child" = 2)) %>%
  add_footnote(label = "Bold values indicate terms whose confidence intervals did not overlap with 0", notation = "none")

**Table 1**
*Estimated Personality-Outcome Associations*
	Birth of a Child
Trait	OR	CI
Negative Affect	1.11	[1.01, 1.22]
Positive Affect	1.97	[1.77, 2.20]
Self-Esteem	1.09	[0.97, 1.22]
Optimism	1.68	[1.50, 1.88]
Depression	1.15	[1.07, 1.24]
Bold values indicate terms whose confidence intervals did not overlap with 0

We did it!

A Quick Note: HTML v. LaTeX

When creating tables, I prefer using HTML when I need the resulting tables to be in HTML and LaTeX when I can place the tables in a PDF. The syntax using kable and kableExtra is the same with the following exceptions:

The format argument in kable() would need to be set as format = "latex".
The chunk option for a table to render would need to be set as {r, results = 'asis'}.
Bolding would need to be done as \\textbf{}, rather than the html <strong></strong> tag.
When using collapse_rows(), which we’ll get to later, you’d want to set the latex_hline argument to latex_hline = "none".

Intermediate Lessons: Multiple DVs/Outcomes, Multiple Model Terms

Often, our models are not quite so simple. So what happens when we mix multiple outcomes/DVs and multiple predictors / IVs? Thankfully, not much is different!

Below, we’ll go through the steps. I’ll skip over explaining ones that were explained in detail in the first example and focus on the new pieces.

Set Up Data

Code

gsoep_nested2 <- gsoep %>%
  group_by(Trait, Outcome) %>%
  nest() %>%
  ungroup()
gsoep_nested2

Run the models

Code

mod1_fun <- function(d){
  d$o_value <- factor(d$o_value)
  glm(o_value ~ p_value, data = d, family = binomial(link = "logit"))
}

gsoep_nested2 <- gsoep_nested2 %>%
  mutate(m = map(data, mod1_fun),
         tidy = map(m, ~tidy(., conf.int = T)))
gsoep_nested2

Create the Table

Code

tidy2 <- gsoep_nested2 %>%
  select(Outcome, Trait, tidy) %>%
  unnest(tidy)
tidy2

The basic steps from here are similar: filter target terms, index significance, exponentiate, format values, create CI’s, bold significance, select needed columns.

Code

tidy2 <- tidy2 %>%
  filter(term == "p_value") %>%
  mutate(sig = ifelse(sign(conf.low) == sign(conf.high), "sig", "ns")) %>%
  mutate_at(vars(estimate, conf.low, conf.high), exp) %>%
  mutate_at(vars(estimate, conf.low, conf.high), ~sprintf("%.2f", .)) %>%
  mutate(CI = sprintf("[%s, %s]", conf.low, conf.high)) %>%
  mutate_at(vars(estimate, CI), ~ifelse(sig == "sig", sprintf("<strong>%s</strong>", .), .)) %>%
  select(Outcome, Trait, OR = estimate, CI)
tidy2

Great, we’re all set right? Not quite. If we want to do a span header, we need our data in shape for that. But right now, our outcomes are rows, not columns. To get them as columns, we will need to: (1) pivot_longer() the OR’s and CI’s, (2) unite() the outcomes and type of estimate, (3) pivot_wider() these united terms, (4) reorder these columns as we want.

Let’s do each in turn:
(1) Long format

Code

tidy2 <- tidy2 %>%
  pivot_longer(cols = c(OR, CI), names_to = "est", values_to = "value")
tidy2

Unite!

Code

tidy2 <- tidy2 %>%
  unite(tmp, Outcome, est, sep = "_")
tidy2

Pivot wider

Code

tidy2 <- tidy2 %>%
  pivot_wider(names_from = "tmp", values_from = "value")

Create the order of columns

Code

O_names <- tibble(
  old = c("mvInPrtnr", "married", "divorced", "chldbrth"),
  new = c("Move in with Partner", "Married", "Divorced", "Birth of a Child")
)

levs <- paste(rep(O_names$old, each = 2), rep(c("OR","CI"), times = 4), sep = "_")
tidy2 <- tidy2 %>%
  select(Trait, all_of(levs))

Now we’re ready to kable()! This will proceed almost exactly as before. The only difference from the previous example is that we have multiple different columns we want to span. Thankfully, we know what these are because we carefully ordered them when we factored them.

For our named vector, we’ll take advantage of our O_names object to create the vector in advance:

Code

heads <- c(1, rep(2, 4))
heads

[1] 1 2 2 2 2

Code

names(heads) <- c(" ", O_names$new)
heads

                     Move in with Partner              Married 
                   1                    2                    2 
            Divorced     Birth of a Child 
                   2                    2

Code

tidy2 %>%
  mutate(Trait = mapvalues(Trait, from = p_names$old, to = p_names$new),
         Trait = factor(Trait, levels = p_names$new)) %>%
  arrange(Trait) %>%
  kable(., escape = F,
        align = c("r", rep("c", 8)),
        caption = "<strong>Table 2</strong><br><em>Estimated Personality-Outcome Associations</em>") %>%
  kable_styling(full_width = F) %>%
  add_header_above(heads) %>%
  add_footnote(label = "Bold values indicate terms whose confidence intervals did not overlap with 0", notation = "none")

**Table 2**
*Estimated Personality-Outcome Associations*
	Move in with Partner		Married		Divorced		Birth of a Child
Trait	mvInPrtnr_OR	mvInPrtnr_CI	married_OR	married_CI	divorced_OR	divorced_CI	chldbrth_OR	chldbrth_CI
Negative Affect	1.33	[1.21, 1.47]	1.29	[1.19, 1.40]	1.58	[1.38, 1.80]	1.11	[1.01, 1.22]
Positive Affect	1.36	[1.21, 1.52]	1.63	[1.49, 1.78]	0.85	[0.74, 0.97]	1.97	[1.77, 2.20]
Self-Esteem	1.00	[0.88, 1.13]	0.86	[0.79, 0.94]	0.95	[0.81, 1.12]	1.09	[0.97, 1.22]
Optimism	1.58	[1.40, 1.79]	1.32	[1.19, 1.46]	1.10	[0.93, 1.30]	1.68	[1.50, 1.88]
Depression	0.91	[0.85, 0.98]	0.99	[0.93, 1.06]	0.74	[0.67, 0.82]	1.15	[1.07, 1.24]
Bold values indicate terms whose confidence intervals did not overlap with 0

Ew, but those column names are terrible. Let’s fix them using the col.names argument in kable():

Code

tidy2 %>%
  mutate(Trait = mapvalues(Trait, from = p_names$old, to = p_names$new),
         Trait = factor(Trait, levels = p_names$new)) %>%
  arrange(Trait) %>%
  kable(., escape = F,
        align = c("r", rep("c", 8)),
        col.names = c("Trait", rep(c("OR", "CI"), times = 4)),
        caption = "<strong>Table 2</strong><br><em>Estimated Personality-Outcome Associations</em>") %>%
  kable_styling(full_width = F) %>%
  add_header_above(heads) %>%
  add_footnote(label = "Bold values indicate terms whose confidence intervals did not overlap with 0", notation = "none")

**Table 2**
*Estimated Personality-Outcome Associations*
	Move in with Partner		Married		Divorced		Birth of a Child
Trait	OR	CI	OR	CI	OR	CI	OR	CI
Negative Affect	1.33	[1.21, 1.47]	1.29	[1.19, 1.40]	1.58	[1.38, 1.80]	1.11	[1.01, 1.22]
Positive Affect	1.36	[1.21, 1.52]	1.63	[1.49, 1.78]	0.85	[0.74, 0.97]	1.97	[1.77, 2.20]
Self-Esteem	1.00	[0.88, 1.13]	0.86	[0.79, 0.94]	0.95	[0.81, 1.12]	1.09	[0.97, 1.22]
Optimism	1.58	[1.40, 1.79]	1.32	[1.19, 1.46]	1.10	[0.93, 1.30]	1.68	[1.50, 1.88]
Depression	0.91	[0.85, 0.98]	0.99	[0.93, 1.06]	0.74	[0.67, 0.82]	1.15	[1.07, 1.24]
Bold values indicate terms whose confidence intervals did not overlap with 0

Advanced Lessons: Multiple DVs/Outcomes, Multiple Model Terms, Additional Formatting

Often, we want to do things like model comparison tests in which we add additional covariates and test if they significantly improve the model or see if they change the direction and magnitude of key terms. Generally, the resulting terms would then be placed in a table.

Below, I’ll cover two cases – covariates and moderators. For the case of moderators, I’ll follow up with simple effects (although these may be added at a later date).

Specifically, we’ll test this for age, gender, parental education, and self-rated health.

To be able to do this, we’ll need to set our data up just a little bit differently. First, this is because we need to center values for testing moderators for variables that aren’t factors or don’t have natural 0 points. Second, the covariates / moderators are currently in wide format, so we’ll need to make them long for our purposes.

Let’s do that first.

Code

gsoep_long <- gsoep %>%
  select(SID, Outcome, o_value, Trait, p_value, age, gender, parEdu, SRhealth) %>%
  mutate_at(vars(age, SRhealth), ~as.numeric(scale(., center = T, scale = F))) %>%
  pivot_longer(
    cols = c(age, gender, parEdu, SRhealth), 
    values_to = "c_value", names_to = "Covariate"
    )

We’ll need to worry about changing gender and parental education into factor variables later. I’m going to show you my favorite trick.

Covariates

Set up Data

This time, we need to add a new grouping variable – namely, the target covariates.

Only trick is we also need the combination with no covariate. There’s lots of ways to add this on. I’ll show you one way.

Code

gsoep_nested3 <- gsoep_long %>%
  full_join(gsoep %>% select(SID, Outcome, o_value, Trait, p_value) %>%
              mutate(Covariate = "none")) %>% 
  group_by(Trait, Outcome, Covariate) %>%
  nest() %>%
  ungroup() %>%
  arrange(Outcome, Trait, Covariate)
gsoep_nested3

Run the models

Code

factor_fun <- function(x){if(is.numeric(x)){diff(range(x, na.rm = T)) %in% 1:2 & length(unique(x)) <= 4} else{F}}

mod3_fun <- function(d, cov){
  d$o_value <- factor(d$o_value)
  d <- d %>% mutate_if(factor_fun, factor)
  if(cov == "none"){f <- formula(o_value ~ p_value)} else{f <- formula(o_value ~ p_value + c_value)}
  glm(f, data = d, family = binomial(link = "logit"))
}

gsoep_nested3 <- gsoep_nested3 %>%
  mutate(m = map2(data, Covariate, mod3_fun),
         tidy = map(m, ~tidy(., conf.int = T)))
gsoep_nested3

Looking specifically at the tidy column, notice that there are different numbers of rows. This is good! We should see 3 rows for age, gender, and SRhealth because we have one new term – a continuous covariate of two level binary covariate. We should see 4 rows for parEdu because we have two new terms – for a three level categorical covariate. Finally, when there is no covariate, we should just have 2 rows like in the previous example.

Create the Table

Code

tidy3 <- gsoep_nested3 %>%
  select(Outcome, Trait, Covariate, tidy) %>%
  unnest(tidy)
tidy3

The basic steps from here are similar: filter target terms, index significance, exponentiate, format values, create CI’s, bold significance, select needed columns.

Code

tidy3 <- tidy3 %>%
  filter(term == "p_value") %>%
  mutate(sig = ifelse(sign(conf.low) == sign(conf.high), "sig", "ns")) %>%
  mutate_at(vars(estimate, conf.low, conf.high), exp) %>%
  mutate_at(vars(estimate, conf.low, conf.high), ~sprintf("%.2f", .)) %>%
  mutate(CI = sprintf("[%s, %s]", conf.low, conf.high)) %>%
  mutate_at(vars(estimate, CI), ~ifelse(sig == "sig", sprintf("<strong>%s</strong>", .), .)) %>%
  select(Outcome, Trait, Covariate, OR = estimate, CI)

Now we’re ready for the pivot_longer(), unite(), pivot_wider(), order columns (using select()) combo from before. The only difference is that I’m going to use pivot_wider() to do the uniting for me this time!

Code

O_names <- tibble(
  old = c("mvInPrtnr", "married", "divorced", "chldbrth"),
  new = c("Move in with Partner", "Married", "Divorced", "Birth of a Child")
)
levs <- paste(rep(O_names$old, each = 2), rep(c("OR","CI"), times = 4), sep = "_")

tidy3 <- tidy3 %>%
  pivot_longer(cols = c(OR, CI), names_to = "est", values_to = "value") %>%
  pivot_wider(names_from = c("Outcome", "est"), values_from = "value", names_sep = "_") %>%
  select(Trait, Covariate, all_of(levs))
tidy3

All right, time to use kable()! This will proceed almost exactly as before. The only difference from the previous example is that we have multiple different models with different combinations of p_value terms depending on what we controlled for. So we’ll introduce a new function collapse_rows() from kableExtra.

Given that we have multiple covariates, we’ll also want to order them. So we’ll make the Covariate column a factor as well. But we’ll do so after we’ve given our covariates nicer names. While we’re at it, we’ll go ahead and do the same things for our Trait column.

Code

c_names <- tibble(
  old = c("none", "age", "SRhealth", "gender", "parEdu"),
  new = c("None", "Age", "Self-Rated Health", "Gender", "Parental Education")
)

tidy3 <- tidy3 %>%
  mutate(Trait = mapvalues(Trait, from = p_names$old, to = p_names$new),
         Trait = factor(Trait, levels = p_names$new),
         Covariate = mapvalues(Covariate, from = c_names$old, to = c_names$new),
         Covariate = factor(Covariate, levels = c_names$new)) %>%
  arrange(Trait, Covariate)
tidy3

And again, for our spanned columns, we’ll take advantage of our O_names object to create the vector in advance:

Code

heads <- rep(2, 5)
heads

[1] 2 2 2 2 2

Code

names(heads) <- c(" ", O_names$new)
heads

                     Move in with Partner              Married 
                   2                    2                    2 
            Divorced     Birth of a Child 
                   2                    2

Now, this will proceed as before, with just a few small tweaks to the align and col.names arguments to account for the additional Covariate column.

Code

tidy3 %>%
  kable(., escape = F,
        align = c("r", "r", rep("c", 8)),
        col.names = c("Trait", "Covariate", rep(c("OR", "CI"), times = 4)),
        caption = "<strong>Table 3</strong><br><em>Estimated Personality-Outcome Associations</em>") %>%
  kable_styling(full_width = F) %>%
  add_header_above(heads) %>%
  add_footnote(label = "Bold values indicate terms whose confidence intervals did not overlap with 0", notation = "none")

**Table 3**
*Estimated Personality-Outcome Associations*
		Move in with Partner		Married		Divorced		Birth of a Child
Trait	Covariate	OR	CI	OR	CI	OR	CI	OR	CI
Negative Affect	None	1.33	[1.21, 1.47]	1.29	[1.19, 1.40]	1.58	[1.38, 1.80]	1.11	[1.01, 1.22]
Negative Affect	Age	1.20	[1.08, 1.33]	1.17	[1.08, 1.27]	1.51	[1.32, 1.73]	0.99	[0.90, 1.09]
Negative Affect	Self-Rated Health	1.58	[1.42, 1.75]	1.53	[1.40, 1.66]	1.70	[1.48, 1.95]	1.39	[1.26, 1.53]
Negative Affect	Gender	1.34	[1.21, 1.48]	1.30	[1.20, 1.41]	1.59	[1.39, 1.82]	1.11	[1.01, 1.22]
Negative Affect	Parental Education	1.35	[1.22, 1.49]	1.32	[1.21, 1.43]	1.61	[1.40, 1.84]	1.12	[1.02, 1.22]
Positive Affect	None	1.36	[1.21, 1.52]	1.63	[1.49, 1.78]	0.85	[0.74, 0.97]	1.97	[1.77, 2.20]
Positive Affect	Age	1.11	[0.99, 1.24]	1.35	[1.23, 1.48]	0.77	[0.68, 0.89]	1.61	[1.44, 1.80]
Positive Affect	Self-Rated Health	1.16	[1.03, 1.31]	1.42	[1.29, 1.56]	0.79	[0.68, 0.91]	1.61	[1.44, 1.81]
Positive Affect	Gender	1.35	[1.21, 1.52]	1.63	[1.49, 1.78]	0.85	[0.74, 0.97]	1.96	[1.76, 2.19]
Positive Affect	Parental Education	1.31	[1.17, 1.47]	1.55	[1.42, 1.70]	0.87	[0.75, 1.00]	1.87	[1.68, 2.09]
Self-Esteem	None	1.00	[0.88, 1.13]	0.86	[0.79, 0.94]	0.95	[0.81, 1.12]	1.09	[0.97, 1.22]
Self-Esteem	Age	1.07	[0.94, 1.22]	0.90	[0.83, 0.99]	0.97	[0.82, 1.15]	1.18	[1.05, 1.33]
Self-Esteem	Self-Rated Health	0.94	[0.83, 1.07]	0.79	[0.73, 0.87]	0.92	[0.78, 1.09]	0.98	[0.87, 1.10]
Self-Esteem	Gender	1.00	[0.88, 1.13]	0.86	[0.79, 0.94]	0.94	[0.81, 1.12]	1.09	[0.98, 1.23]
Self-Esteem	Parental Education	0.99	[0.87, 1.12]	0.86	[0.79, 0.93]	0.96	[0.82, 1.15]	1.08	[0.97, 1.22]
Optimism	None	1.58	[1.40, 1.79]	1.32	[1.19, 1.46]	1.10	[0.93, 1.30]	1.68	[1.50, 1.88]
Optimism	Age	1.23	[1.08, 1.41]	1.08	[0.97, 1.20]	1.00	[0.85, 1.19]	1.33	[1.18, 1.50]
Optimism	Self-Rated Health	1.33	[1.17, 1.51]	1.11	[1.00, 1.24]	1.04	[0.87, 1.24]	1.31	[1.16, 1.48]
Optimism	Gender	1.58	[1.40, 1.79]	1.32	[1.19, 1.46]	1.10	[0.93, 1.30]	1.68	[1.50, 1.89]
Optimism	Parental Education	1.50	[1.32, 1.70]	1.32	[1.19, 1.47]	1.07	[0.90, 1.27]	1.63	[1.45, 1.83]
Depression	None	0.91	[0.85, 0.98]	0.99	[0.93, 1.06]	0.74	[0.67, 0.82]	1.15	[1.07, 1.24]
Depression	Age	0.88	[0.81, 0.95]	0.99	[0.92, 1.06]	0.73	[0.66, 0.80]	1.16	[1.07, 1.25]
Depression	Self-Rated Health	0.63	[0.59, 0.69]	0.74	[0.69, 0.80]	0.63	[0.56, 0.70]	0.77	[0.71, 0.83]
Depression	Gender	0.91	[0.85, 0.98]	0.99	[0.93, 1.06]	0.74	[0.67, 0.82]	1.16	[1.08, 1.25]
Depression	Parental Education	0.89	[0.83, 0.96]	0.97	[0.91, 1.04]	0.76	[0.68, 0.84]	1.12	[1.05, 1.21]
Bold values indicate terms whose confidence intervals did not overlap with 0

So this looks pretty good, except that it’s annoying how the Trait name is repeated five times. This is where we’ll use collapse_rows().

Code

tidy3 %>%
  kable(., escape = F,
        align = c("r", "r", rep("c", 8)),
        col.names = c("Trait", "Covariate", rep(c("OR", "CI"), times = 4)),
        caption = "<strong>Table 3</strong><br><em>Estimated Personality-Outcome Associations</em>") %>%
  kable_styling(full_width = F) %>%
  collapse_rows(1, valign = "top") %>%
  add_header_above(heads) %>%
  add_footnote(label = "Bold values indicate terms whose confidence intervals did not overlap with 0", notation = "none")

**Table 3**
*Estimated Personality-Outcome Associations*
		Move in with Partner		Married		Divorced		Birth of a Child
Trait	Covariate	OR	CI	OR	CI	OR	CI	OR	CI
Negative Affect	None	1.33	[1.21, 1.47]	1.29	[1.19, 1.40]	1.58	[1.38, 1.80]	1.11	[1.01, 1.22]
	Age	1.20	[1.08, 1.33]	1.17	[1.08, 1.27]	1.51	[1.32, 1.73]	0.99	[0.90, 1.09]
	Self-Rated Health	1.58	[1.42, 1.75]	1.53	[1.40, 1.66]	1.70	[1.48, 1.95]	1.39	[1.26, 1.53]
	Gender	1.34	[1.21, 1.48]	1.30	[1.20, 1.41]	1.59	[1.39, 1.82]	1.11	[1.01, 1.22]
	Parental Education	1.35	[1.22, 1.49]	1.32	[1.21, 1.43]	1.61	[1.40, 1.84]	1.12	[1.02, 1.22]
Positive Affect	None	1.36	[1.21, 1.52]	1.63	[1.49, 1.78]	0.85	[0.74, 0.97]	1.97	[1.77, 2.20]
	Age	1.11	[0.99, 1.24]	1.35	[1.23, 1.48]	0.77	[0.68, 0.89]	1.61	[1.44, 1.80]
	Self-Rated Health	1.16	[1.03, 1.31]	1.42	[1.29, 1.56]	0.79	[0.68, 0.91]	1.61	[1.44, 1.81]
	Gender	1.35	[1.21, 1.52]	1.63	[1.49, 1.78]	0.85	[0.74, 0.97]	1.96	[1.76, 2.19]
	Parental Education	1.31	[1.17, 1.47]	1.55	[1.42, 1.70]	0.87	[0.75, 1.00]	1.87	[1.68, 2.09]
Self-Esteem	None	1.00	[0.88, 1.13]	0.86	[0.79, 0.94]	0.95	[0.81, 1.12]	1.09	[0.97, 1.22]
	Age	1.07	[0.94, 1.22]	0.90	[0.83, 0.99]	0.97	[0.82, 1.15]	1.18	[1.05, 1.33]
	Self-Rated Health	0.94	[0.83, 1.07]	0.79	[0.73, 0.87]	0.92	[0.78, 1.09]	0.98	[0.87, 1.10]
	Gender	1.00	[0.88, 1.13]	0.86	[0.79, 0.94]	0.94	[0.81, 1.12]	1.09	[0.98, 1.23]
	Parental Education	0.99	[0.87, 1.12]	0.86	[0.79, 0.93]	0.96	[0.82, 1.15]	1.08	[0.97, 1.22]
Optimism	None	1.58	[1.40, 1.79]	1.32	[1.19, 1.46]	1.10	[0.93, 1.30]	1.68	[1.50, 1.88]
	Age	1.23	[1.08, 1.41]	1.08	[0.97, 1.20]	1.00	[0.85, 1.19]	1.33	[1.18, 1.50]
	Self-Rated Health	1.33	[1.17, 1.51]	1.11	[1.00, 1.24]	1.04	[0.87, 1.24]	1.31	[1.16, 1.48]
	Gender	1.58	[1.40, 1.79]	1.32	[1.19, 1.46]	1.10	[0.93, 1.30]	1.68	[1.50, 1.89]
	Parental Education	1.50	[1.32, 1.70]	1.32	[1.19, 1.47]	1.07	[0.90, 1.27]	1.63	[1.45, 1.83]
Depression	None	0.91	[0.85, 0.98]	0.99	[0.93, 1.06]	0.74	[0.67, 0.82]	1.15	[1.07, 1.24]
	Age	0.88	[0.81, 0.95]	0.99	[0.92, 1.06]	0.73	[0.66, 0.80]	1.16	[1.07, 1.25]
	Self-Rated Health	0.63	[0.59, 0.69]	0.74	[0.69, 0.80]	0.63	[0.56, 0.70]	0.77	[0.71, 0.83]
	Gender	0.91	[0.85, 0.98]	0.99	[0.93, 1.06]	0.74	[0.67, 0.82]	1.16	[1.08, 1.25]
	Parental Education	0.89	[0.83, 0.96]	0.97	[0.91, 1.04]	0.76	[0.68, 0.84]	1.12	[1.05, 1.21]
Bold values indicate terms whose confidence intervals did not overlap with 0

That’s a pretty good-looking table!

Moderators

All right, time to introduce a moderator!

The procedure for this is going to be very close to above, with the main change being that the key term will no longer be p_value but p_value:moderator.

But first, the set up
### Set up Data
This time, we need to add a new grouping variable – namely, the target moderators. We aren’t going to include the “none” condition here, as we assume we’ve already tested and presented those results separately.

Code

gsoep_nested4 <- gsoep_long %>%
  group_by(Trait, Outcome, Covariate) %>%
  nest() %>%
  ungroup() %>%
  arrange(Outcome, Trait, Covariate)
gsoep_nested4

Run the models

Code

factor_fun <- function(x){if(is.numeric(x)){diff(range(x, na.rm = T)) %in% 1:2 & length(unique(x)) <= 4} else{F}}

mod4_fun <- function(d, cov){
  d$o_value <- factor(d$o_value)
  d <- d %>% mutate_if(factor_fun, factor)
  if(cov == "none"){f <- formula(o_value ~ p_value)} else{f <- formula(o_value ~ p_value* c_value)}
  glm(f, data = d, family = binomial(link = "logit"))
}

gsoep_nested4 <- gsoep_nested4 %>%
  mutate(m = map2(data, Covariate, mod4_fun),
         tidy = map(m, ~tidy(., conf.int = T)))
gsoep_nested4

Like before, we are going to have different numbers of rows. Again this is good becuase it means that the appropriate moderators and main effects were added.

Create the Table

Code

tidy4 <- gsoep_nested4 %>%
  select(Outcome, Trait, Moderator = Covariate, tidy) %>%
  unnest(tidy)
tidy4

The basic steps from here are similar: filter target terms, index significance, exponentiate, format values, create CI’s, bold significance, select needed columns.

We do need to a bit of work on how we index our moderators. Specifically, for the factor variables, we want to make sure we indicate what the levels are.

Code

tidy4 <- tidy4 %>%
  filter(grepl("p_value:", term)) %>%
  mutate(term = str_replace(term, "c_value", Moderator),
         Moderator = str_remove(term, "p_value:"),
         sig = ifelse(sign(conf.low) == sign(conf.high), "sig", "ns")) %>%
  mutate_at(vars(estimate, conf.low, conf.high), exp) %>%
  mutate_at(vars(estimate, conf.low, conf.high), ~sprintf("%.2f", .)) %>%
  mutate(CI = sprintf("[%s, %s]", conf.low, conf.high)) %>%
  mutate_at(vars(estimate, CI), ~ifelse(sig == "sig", sprintf("<strong>%s</strong>", .), .)) %>%
  select(Outcome, Trait, Moderator, OR = estimate, CI)

Code

O_names <- tibble(
  old = c("mvInPrtnr", "married", "divorced", "chldbrth"),
  new = c("Move in with Partner", "Married", "Divorced", "Birth of a Child")
)
levs <- paste(rep(O_names$old, each = 2), rep(c("OR","CI"), times = 4), sep = "_")

tidy4 <- tidy4 %>%
  pivot_longer(cols = c(OR, CI), names_to = "est", values_to = "value") %>%
  pivot_wider(names_from = c("Outcome", "est"), values_from = "value", names_sep = "_") %>%
  select(Trait, Moderator, all_of(levs))

All right, time to use kable()! This will proceed almost exactly as the example with covariates, with the main difference being that our target term is now the interaction.

Given that we have multiple moderators, some of which have multiple levels, we’ll also want to order them. So we’ll make the Moderator column a factor as well. But we’ll do so after we’ve given our covariates nicer names. While we’re at it, we’ll go ahead and do the same things for our Trait column.

Code

m_names <- tibble(
  old = c("age", "SRhealth", "gender1", "parEdu1", "parEdu2"),
  new = c("Age", "Self-Rated Health", "Gender (Female)", 
          "Parental Education (College)", "Parental Education (Beyond College)")
)

tidy4 <- tidy4 %>%
  mutate(Trait = mapvalues(Trait, from = p_names$old, to = p_names$new),
         Trait = factor(Trait, levels = p_names$new),
         Moderator = mapvalues(Moderator, from = m_names$old, to = m_names$new),
         Moderator = factor(Moderator, levels = m_names$new)) %>%
  arrange(Trait, Moderator)

And again, for our spanned columns, we’ll take advantage of our O_names object to create the vector in advance:

Code

heads <- rep(2, 5)
heads

[1] 2 2 2 2 2

Code

names(heads) <- c(" ", O_names$new)
heads

                     Move in with Partner              Married 
                   2                    2                    2 
            Divorced     Birth of a Child 
                   2                    2

Now, this will proceed as before, including using collapse_rows(). The change this time will simply be to our table caption.

Code

tidy4 %>%
  kable(., escape = F,
        align = c("r", "r", rep("c", 8)),
        col.names = c("Trait", "Moderator", rep(c("OR", "CI"), times = 4)),
        caption = "<strong>Table 4</strong><br><em>Estimated Moderators of Personality-Outcome Associations</em>") %>%
  kable_styling(full_width = F) %>%
  collapse_rows(1, valign = "top") %>%
  add_header_above(heads) %>%
  add_footnote(label = "Bold values indicate terms whose confidence intervals did not overlap with 0", notation = "none")

**Table 4**
*Estimated Moderators of Personality-Outcome Associations*
		Move in with Partner		Married		Divorced		Birth of a Child
Trait	Moderator	OR	CI	OR	CI	OR	CI	OR	CI
Negative Affect	Age	1.00	[1.00, 1.01]	1.00	[1.00, 1.01]	1.00	[0.99, 1.01]	1.00	[1.00, 1.01]
	Self-Rated Health	0.95	[0.83, 1.09]	0.97	[0.87, 1.09]	0.94	[0.80, 1.11]	0.91	[0.80, 1.05]
	Gender (Female)	1.07	[0.87, 1.31]	1.13	[0.96, 1.32]	0.77	[0.59, 1.01]	0.99	[0.82, 1.20]
	Parental Education (College)	1.07	[0.85, 1.35]	0.83	[0.69, 1.00]	1.13	[0.76, 1.65]	1.11	[0.89, 1.39]
	Parental Education (Beyond College)	0.85	[0.58, 1.22]	0.80	[0.55, 1.14]	1.39	[0.82, 2.33]	1.09	[0.76, 1.56]
Positive Affect	Age	0.99	[0.99, 1.00]	1.00	[1.00, 1.01]	1.01	[1.00, 1.02]	1.01	[1.00, 1.02]
	Self-Rated Health	1.22	[1.06, 1.40]	0.95	[0.83, 1.07]	1.01	[0.85, 1.19]	0.92	[0.78, 1.08]
	Gender (Female)	1.09	[0.87, 1.37]	1.16	[0.97, 1.39]	1.13	[0.86, 1.48]	1.26	[1.01, 1.57]
	Parental Education (College)	0.92	[0.71, 1.20]	0.88	[0.71, 1.10]	0.98	[0.65, 1.49]	0.93	[0.71, 1.22]
	Parental Education (Beyond College)	1.06	[0.70, 1.63]	1.14	[0.75, 1.78]	0.47	[0.28, 0.79]	0.49	[0.34, 0.73]
Self-Esteem	Age	1.00	[0.99, 1.01]	1.00	[1.00, 1.01]	1.00	[0.99, 1.01]	1.00	[0.99, 1.01]
	Self-Rated Health	1.03	[0.86, 1.21]	0.99	[0.88, 1.11]	1.13	[0.92, 1.35]	0.99	[0.83, 1.17]
	Gender (Female)	0.77	[0.59, 0.99]	0.90	[0.76, 1.07]	1.15	[0.83, 1.60]	0.73	[0.57, 0.94]
	Parental Education (College)	0.97	[0.73, 1.32]	1.00	[0.83, 1.21]	0.88	[0.59, 1.36]	0.91	[0.71, 1.18]
	Parental Education (Beyond College)	0.95	[0.62, 1.53]	0.97	[0.65, 1.51]	NA	NA	0.82	[0.51, 1.43]
Optimism	Age	1.00	[0.99, 1.01]	1.01	[1.00, 1.01]	1.01	[1.00, 1.02]	1.02	[1.01, 1.02]
	Self-Rated Health	0.89	[0.74, 1.05]	0.93	[0.81, 1.07]	1.01	[0.82, 1.24]	0.77	[0.65, 0.92]
	Gender (Female)	1.03	[0.81, 1.32]	0.97	[0.79, 1.18]	1.06	[0.76, 1.48]	1.06	[0.84, 1.33]
	Parental Education (College)	0.91	[0.68, 1.24]	0.85	[0.65, 1.13]	0.74	[0.44, 1.25]	1.04	[0.76, 1.42]
	Parental Education (Beyond College)	1.03	[0.62, 1.75]	1.30	[0.82, 2.15]	1.73	[0.78, 4.28]	0.77	[0.51, 1.19]
Depression	Age	1.00	[1.00, 1.01]	1.01	[1.00, 1.01]	1.00	[1.00, 1.01]	1.00	[1.00, 1.01]
	Self-Rated Health	0.97	[0.89, 1.06]	0.87	[0.80, 0.95]	0.91	[0.80, 1.02]	0.88	[0.79, 0.97]
	Gender (Female)	1.05	[0.91, 1.21]	1.03	[0.91, 1.17]	1.21	[1.00, 1.48]	1.04	[0.90, 1.20]
	Parental Education (College)	0.87	[0.73, 1.04]	0.94	[0.79, 1.10]	0.99	[0.73, 1.34]	0.80	[0.67, 0.96]
	Parental Education (Beyond College)	1.05	[0.80, 1.40]	1.06	[0.80, 1.43]	0.85	[0.56, 1.33]	1.12	[0.84, 1.52]
Bold values indicate terms whose confidence intervals did not overlap with 0

Simple Effects

With moderator analyses, we typically need to unpack the results and present them as simple effects. In many cases, plots are super useful. I demonstrated how to make simple effects plots in the purrr tutorial, so check there for more!

We’re going to start here with our same nested data frame from the previous example. We’ll basically want to get estimates of the personality-outcome associations for different levels of our factor or continuous moderators.

To do this, we’ll use the predict() functions.

** In progress **

Other Lessons

Descriptives

Descriptives tables are slightly tricky to add generalizable code for becuase the needs of those tables vary. Below, I’m going to give some code for a couple of basic descriptive tables I’d be likely to include for a paper looking at predictive personality-outcome associations.

First, one indexing demographic characteristics and samples sizes. Second, one indexing means and standard deviations. Then finally wrapping up with a correlation table of study measures.

Demographic Charateristics

First, let’s look at demographic characteristics, including sample sizes, age, and gender.

Because I’m interested in events, I’m going to specifically look at sample sizes of those who did and did experience events as well as any gender or age differences.

Code

gsoep %>%
  select(p_year, SID, Outcome, o_value, gender, age) %>%
  group_by(p_year, Outcome, o_value) %>%
  summarize(N = n(), 
            Female = sum(gender, na.rm = T), 
            Age = mean(age, na.rm = T), 
            SD_Age = sd(age, na.rm = T)) %>%
  ungroup() %>%
  pivot_longer(cols = N:SD_Age, names_to = "measure", values_to = "value") %>%
  pivot_wider(names_from = c("measure", "o_value"), values_from = "value",
              names_sep = "_") %>%
  mutate(N = sprintf("%s (%s)", N_1, N_0),
         `% Female` = sprintf("%.2f (%.2f)", Female_1/N_1*100, Female_0/N_0*100),
         `Mean` = sprintf("%.1f (%.1f)", Age_1, Age_0),
         `SD` = sprintf("%.1f (%.1f)", SD_Age_1, SD_Age_0),
         Outcome = mapvalues(Outcome, O_names$old, O_names$new),
         Outcome = factor(Outcome, levels = O_names$new)) %>%
  select(Outcome, Year = p_year, N:`SD`) %>%
  arrange(Outcome, Year) %>%
  kable(., escape = F,
        caption = "<strong>Table 7</strong><br><em>Descriptive Statistics of Those Who Experienced Outcomes versus Those Who Did Not",
        align = c("r", "r", rep("c", 4))) %>%
  kable_styling(full_width = F) %>%
  add_header_above(c(" " = 4, "Age" = 2)) %>%
  collapse_rows(1, "top") %>%
  add_footnote(label = "Values in parentheses indicate demographics of those who did not experience outcomes after the noted year", notation = "none")

**Table 7**
*Descriptive Statistics of Those Who Experienced Outcomes versus Those Who Did Not*
				Age
Outcome	Year	N	% Female	Mean	SD
Move in with Partner	2002	734 (10381)	52.72 (51.61)	21.5 (41.4)	12.6 (17.8)
	2005	460 (8802)	54.35 (52.08)	18.8 (39.6)	12.4 (18.2)
	2006	433 (9387)	53.58 (52.43)	20.8 (41.2)	13.8 (18.2)
	2007	671 (17433)	55.14 (52.41)	20.1 (40.4)	13.9 (18.3)
	2010	626 (31164)	50.32 (52.14)	21.3 (40.7)	15.1 (18.9)
Married	2002	837 (10309)	52.21 (51.14)	24.3 (41.0)	12.3 (18.1)
	2005	632 (8766)	51.58 (51.87)	23.2 (39.1)	12.8 (18.6)
	2006	602 (9370)	51.00 (52.27)	24.1 (40.8)	13.2 (18.6)
	2007	1042 (17410)	51.06 (52.19)	22.9 (40.0)	13.5 (18.6)
	2010	1210 (31095)	52.73 (52.00)	19.9 (40.6)	14.7 (19.0)
Divorced	2002	320 (11374)	53.12 (51.70)	31.4 (39.6)	11.5 (18.3)
	2005	216 (9924)	52.78 (52.19)	30.1 (37.5)	10.7 (18.6)
	2006	204 (10564)	53.43 (52.52)	31.3 (39.0)	11.7 (18.7)
	2007	360 (19822)	54.44 (52.52)	30.7 (38.2)	11.8 (18.7)
	2010	336 (35446)	52.38 (52.34)	31.1 (38.6)	12.4 (19.1)
Birth of a Child	2002	779 (10153)	53.02 (51.62)	21.9 (41.9)	10.2 (17.9)
	2005	543 (8649)	54.51 (51.95)	20.4 (40.0)	10.7 (18.4)
	2006	483 (9264)	54.66 (52.29)	21.1 (41.6)	11.0 (18.4)
	2007	795 (17267)	52.83 (52.30)	20.8 (40.8)	11.3 (18.5)
	2010	821 (30967)	57.25 (52.02)	19.5 (41.2)	12.3 (18.9)
Values in parentheses indicate demographics of those who did not experience outcomes after the noted year

APA Tables Part 1

Workspace

Packages

Data

APA Tables

Outline

Important Tools

Basic Lessons: One DV/Outcome, Multiple Model Terms

Set up the data frame

Run Models

Get Key Terms

Creating a Table

A Quick Note: HTML v. LaTeX

Intermediate Lessons: Multiple DVs/Outcomes, Multiple Model Terms

Set Up Data

Run the models

Create the Table

Advanced Lessons: Multiple DVs/Outcomes, Multiple Model Terms, Additional Formatting

Covariates

Set up Data

Run the models

Create the Table

Moderators

Run the models

Create the Table

Simple Effects

Other Lessons

Descriptives

Demographic Charateristics

Means and Standard Deviations

Zero-Order Correlations