# Chapter 55 Estimating a Mediation Model Using Path Analysis

In this chapter, we will learn how to estimate a mediation analysis using path analysis.

## 55.1 Conceptual Overview

**Mediation analysis** is used to investigate whether an intervening variable transmits the effects of a predictor variable on an outcome variable. Both multiple regression and structural equation modeling (path analysis) can be used to perform mediation analysis, but structural equation modeling (path analysis) offers a single-step process that often more efficient. When implemented using path analysis, mediation analysis follows the same model specification, model identification, and model fit considerations as general applications of path analysis. For more information on path analysis, please refer to the previous chapter on path analysis.

Just as in a conventional path analysis model, we often represent our mediation analysis model visually as a *path diagram*. For illustration purposes, let’s begin with a path diagram of the direct relation between a predictor variable called Attitude with an outcome variable called Behavior (see Figure 1). This path diagram specifies Attitude (towards the Behavior) as a predictor of the Behavior, with any error in prediction captured in the (residual) error term. By convention, we commonly refer to this path as the **c** path.

In the path diagram depicted in Figure 2, we introduce an intervening variable, which we commonly refer to as the **mediator variable** – or just as the **mediator**. As we saw in Figure 1, a direct relation between the predictor variable Attitude and the outcome variable Behavior is shown; however, note that we now refer to this relation as the **c’** path (spoken: “c prime”) because it represents the **direct effect** of the predictor variable on the outcome variable in the presence of (i.e., statistically controlling) for the effect of the mediator variable. The mediator variable is called Intention, and as you can see, it intervenes between the predictor and outcome variables. The direct relation between the predictor variable and mediator variable is commonly referred to as the **a** path, and note that because the mediator variable is an **endogenous variable** (i.e., has a specified cause in the model), it as a (residual) error term. The direct relation between the mediator variable and the outcome variable is commonly referred to as the **b** path. The effect of the predictor variable on the outcome variable via the mediating variable is referred to as the **indirect effect** given that the predictor variable indirectly influences the outcome variable through another variable; in other words, the mediator variable transmits the effect of the predictor variable to the outcome variable.

The mediation model shown in Figure 2 is an example of a partial mediation. We can distinguish between a partial and a complete mediation. A **partial mediation** is a mediation in which the direct relation (**c’**) between the predictor variable and the outcome variable is *non-zero*; in other words, a direct relation between the predictor and the outcome is estimated as a free parameter. A **complete mediation** (i.e., **full mediation**) is a mediation in which the direct relation (**c’**) between the predictor variable and the outcome variable is *zero*; in other words, the direct relation between the predictor and the outcome is fixed at zero, which means we don’t specify the direct relation at all in our model – or we specify the direct relation but fix it to zero. A complete mediation is shown in Figure 3.

### 55.1.1 Estimation of Indirect Effect

There are different approaches to estimating the indirect effect, such as *causal steps* (Baron and Kenny 1986), *difference in coefficients*, and *product of coefficients* (MacKinnon and Dwyer 1993). Because the **product of coefficients** approach is efficient to implement, we will focus on that approach in this tutorial. Using this approach, the indirect effect is estimated by computing the product of the coefficients for directional relations (i.e., paths), which include the relation between the predictor variable and the mediator variable (**a** path) and the relation between the mediator variable and the outcome variable (**b**). The statistical significance of the indirect effect can be computed by dividing the product of the coefficients by its standard error, and then the resulting ratio (quotient) can be compared to a standard normal distribution – or another distribution.

There are different methods for implementing the product of coefficients approach, and we will focus on the (multivariate) delta method and a resampling method. First, in the **(multivariate) delta method** (where the Sobel test is a specific form), the standard error for the product of coefficients is compared to a standard normal distribution to determine the indirect effect’s significance level (i.e., *p*-value). This method does tend to show bias in smaller samples [e.g., *N* < 50; MacKinnon, Warsi, and Dwyer (1995); MacKinnon et al. (2002)], however, and assumes the sampling distribution of the indirect effect is normal, which is not often a tenable assumption when dealing with the product of two coefficients. Second, **resampling methods** constitute a broad family of methods in which data are re-sampled (with replacement) a specified number of times (e.g., 5,000), and a normal sampling distribution need not be assumed. Examples of resampling methods broadly include bootstrapping, the permutation method, and the Monte Carlo method. **Bootstrapping** is perhaps one of the most common a popular resampling methods today, and bootstrapping can be used to estimate confidence intervals for an indirect effect estimated using the product of coefficients approach. Many resampling approaches tend to show some bias in small samples [e.g., *N* < 20-80; Koopman et al. (2015)]; however, resampling methods like bootstrapping are often preferred over the delta method because they do not hold the strict assumption that the sampling distribution is normal. There are a multiple types of bootstrapping, such as percentile, bias-corrected, and bias-corrected and accelerated, most of which can be implemented with relative ease in R.

### 55.1.2 Model Identification

Because we are testing for mediation using path analysis in this chapter, please refer to the Model Identification section from previous chapter on path analysis.

### 55.1.3 Model Fit

please refer to the Model Fit section from previous chapter on path analysis.

### 55.1.4 Parameter Estimates

In mediation analysis, parameter estimates (e.g., direct relations, covariances, variances) can be interpreted like those from a regression model, where the associated *p*-values or confidence intervals can be used as indicators of statistical significance.

### 55.1.5 Statistical Assumptions

The statistical assumptions that should be met prior to estimating and/or interpreting a mediation analysis model are the same that should be met when running a path analysis model; for a review of those assumptions please review to the previous chapter path analysis.

### 55.1.6 Conceptual Video

For a more in-depth review of path analysis, please check out the following conceptual video.

Link to conceptual video: https://youtu.be/3zA4iB0aAvg

## 55.2 Tutorial

This chapter’s tutorial demonstrates perform mediation analysis in a path analysis framework using R.

### 55.2.1 Video Tutorial

As usual, you have the choice to follow along with the written tutorial in this chapter or to watch the video tutorial below.

Link to video tutorial: https://youtu.be/za-KTX1A5us

### 55.2.2 Functions & Packages Introduced

Function | Package |
---|---|

`sem` |
`lavaan` |

`summary` |
base R |

`set.seed` |
base R |

`parameterEstimates` |
`lavaan` |

### 55.2.3 Initial Steps

If you haven’t already, save the file called **“PlannedBehavior.csv”** into a folder that you will subsequently set as your working directory. Your working directory will likely be different than the one shown below (i.e., `"H:/RWorkshop"`

). As a reminder, you can access all of the data files referenced in this book by downloading them as a compressed (zipped) folder from the my GitHub site: https://github.com/davidcaughlin/R-Tutorial-Data-Files; once you’ve followed the link to GitHub, just click “Code” (or “Download”) followed by “Download ZIP”, which will download all of the data files referenced in this book. For the sake of parsimony, I recommend downloading all of the data files into the same folder on your computer, which will allow you to set that same folder as your working directory for each of the chapters in this book.

Next, using the `setwd`

function, set your working directory to the folder in which you saved the data file for this chapter. Alternatively, you can manually set your working directory folder in your drop-down menus by going to *Session > Set Working Directory > Choose Directory…*. Be sure to create a new R script file (.R) or update an existing R script file so that you can save your script and annotations. If you need refreshers on how to set your working directory and how to create and save an R script, please refer to Setting a Working Directory and Creating & Saving an R Script.

Next, read in the .csv data file called **“PlannedBehavior.csv”** using your choice of read function. In this example, I use the `read_csv`

function from the `readr`

package (Wickham, Hester, and Bryan 2024). If you choose to use the `read_csv`

function, be sure that you have installed and accessed the `readr`

package using the `install.packages`

and `library`

functions. *Note: You don’t need to install a package every time you wish to access it; in general, I would recommend updating a package installation once ever 1-3 months.* For refreshers on installing packages and reading data into R, please refer to Packages and Reading Data into R.

```
# Install readr package if you haven't already
# [Note: You don't need to install a package every
# time you wish to access it]
install.packages("readr")
```

```
# Access readr package
library(readr)
# Read data and name data frame (tibble) object
df <- read_csv("PlannedBehavior.csv")
```

```
## Rows: 199 Columns: 5
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (5): attitude, norms, control, intention, behavior
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

`## [1] "attitude" "norms" "control" "intention" "behavior"`

```
## spc_tbl_ [199 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ attitude : num [1:199] 2.31 4.66 3.85 4.24 2.91 2.99 3.96 3.01 4.77 3.67 ...
## $ norms : num [1:199] 2.31 4.01 3.56 2.25 3.31 2.51 4.65 2.98 3.09 3.63 ...
## $ control : num [1:199] 2.03 3.63 4.2 2.84 2.4 2.95 3.77 1.9 3.83 5 ...
## $ intention: num [1:199] 2.5 3.99 4.35 1.51 1.45 2.59 4.08 2.58 4.87 3.09 ...
## $ behavior : num [1:199] 2.62 3.64 3.83 2.25 2 2.2 4.41 4.15 4.35 3.95 ...
## - attr(*, "spec")=
## .. cols(
## .. attitude = col_double(),
## .. norms = col_double(),
## .. control = col_double(),
## .. intention = col_double(),
## .. behavior = col_double()
## .. )
## - attr(*, "problems")=<externalptr>
```

```
## # A tibble: 6 × 5
## attitude norms control intention behavior
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2.31 2.31 2.03 2.5 2.62
## 2 4.66 4.01 3.63 3.99 3.64
## 3 3.85 3.56 4.2 4.35 3.83
## 4 4.24 2.25 2.84 1.51 2.25
## 5 2.91 3.31 2.4 1.45 2
## 6 2.99 2.51 2.95 2.59 2.2
```

`## [1] 199`

There are 5 variables and 199 cases (i.e., employees) in the `df`

data frame: `attitude`

, `norms`

, `control`

, `intention`

, and `behavior`

. Per the output of the `str`

(structure) function above, all of the variables are of type *numeric* (continuous: interval/ratio). All of the variables are self-reports from a survey, where respondents rated their level of agreement (1 = strongly disagree, 5 = strongly agree) for the items associated with the `attitude`

, `norms`

, `control`

, and `intention`

scales, and where respondents rated the frequency with which they enact the behavior associated with the `behavior`

variable (1 = never, 5 = all of the time). The `attitude`

variable reflects an employee’s attitude toward the behavior in question. The `norms`

variable reflects an employee’s perception of norms pertaining to the enactment of the behavior. The `control`

variable reflects an employee’s feeling of control over being able to perform the behavior. The `intention`

variable reflects an employee’s intention to enact the behavior. The `behavior`

variable reflects an employee’s perception of the frequency with which they actually engage in the behavior.

### 55.2.4 Specify & Estimate a Mediation Analysis Model

We will use functions from the `lavaan`

package (Rosseel 2012) to specify and estimate our mediation analysis model. The `lavaan`

package also allows structural equation modeling with latent variables, but we won’t cover that in this tutorial. If you haven’t already install and access the `lavaan`

package using the `install.packages`

and `library`

functions, respectively. For background information on the `lavaan`

package, check out the package website.

Let’s specify a *partial* mediation model in which `attitude`

is the predictor variable, `intention`

is the mediator variable, and `behavior`

is the outcome variable. [*Note*: If we wanted to specify a *complete* mediation model, then we would *not* specify the direct relation from the predictor variable to the outcome variable.] We will first apply the *delta method* for estimating the indirect effect and then follow it up with the **percentile bootstrapping** method.

#### 55.2.4.1 Delta Method

To apply the delta method, which is a product of coefficients method, we will do the following.

- We will specify the model and name it
`specmod`

using the`<-`

operator.

- If we precede a predictor variable with a letter or a series of characters without spaces between them and then follow that up with an asterisk (
`*`

), we can label a path (or covariance or variance) so that later we can reference it. In this case, we will label the direct relation between the predictor variable (`attitude`

) and the outcome variable as`c`

(i.e.,`behavior ~ c*attitude`

), and we will label the paths between the predictor variable and the mediator variable and the mediator variable and the outcome variable as`a`

and`b`

(respectively). - To estimate the indirect effect using the product of coefficients method, we will type an arbitrary name for the indirect effect (such as
`ab`

) followed by the`:=`

operator, and then followed by the product of the`a`

and`b`

paths we previously labeled (i.e.,`a*b`

).

- We will create a model estimation object (which here we label
`fitmod`

), and apply the`sem`

function from`lavaan`

, with the model specification object (`specmod`

) as the first argument. As the second argument, type`data=`

followed by the name of the data from which the variables named in the model specification object come (`df`

). - Type the name of the
`summary`

function from base R. As the first argument, type the name of the model estimation object we created in the previous step (`fitmod`

). As the second and third arguments, respectively, type`fit.measures=TRUE`

and`rsquare=TRUE`

to request the model fit indices and the unadjusted*R*^{2}values for the endogenous variables.

```
# Specify mediation analysis model
specmod <- "
# Path c' (direct effect)
behavior ~ c*attitude
# Path a
intention ~ a*attitude
# Path b
behavior ~ b*intention
# Indirect effect (a*b)
ab := a*b
"
# Estimate model
fitmod <- sem(specmod, data=df)
# Request summary of results
summary(fitmod, fit.measures=TRUE, rsquare=TRUE)
```

```
## lavaan 0.6.15 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 5
##
## Number of observations 199
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 103.700
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -481.884
## Loglikelihood unrestricted model (H1) -481.884
##
## Akaike (AIC) 973.767
## Bayesian (BIC) 990.234
## Sample-size adjusted Bayesian (SABIC) 974.393
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## behavior ~
## attitude (c) 0.029 0.071 0.412 0.680
## intention ~
## attitude (a) 0.484 0.058 8.333 0.000
## behavior ~
## intention (b) 0.438 0.075 5.832 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .behavior 0.698 0.070 9.975 0.000
## .intention 0.623 0.062 9.975 0.000
##
## R-Square:
## Estimate
## behavior 0.199
## intention 0.259
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ab 0.212 0.044 4.778 0.000
```

Again, we have 199 observations (employees) used in this analysis, and the degrees of freedom is equal to 0, which indicates that our model is just-identified. Because the model is just-identified, there are no available degrees of freedom with which to estimate some of the common model fit indices (e.g., RMSEA), so we will not evaluate model fit. Instead, we will move on to interpreting model parameter estimates. The path coefficient (i.e., direct relation, presumed causal relation) between `attitude`

and `intention`

(i.e., path **a**) is statistically significant and positive (*b* = .484, *p* < .001). Further, the path coefficient between `intention`

and `behavior`

(i.e., path **b**) is also statistically significant and positive (*b* = .484, *p* < .001). The direct effect path between `attitude`

and `behavior`

, however, is nonsignificant (*b* = .029, *p* = .680). The (residual) error variances for `behavior`

and `intentions`

are not of substantive interest, so we will ignore them. The unadjusted *R*^{2} values for the endogenous variable `intention`

is .259, which indicates that `attitude`

explains 25.9% of the variance in `intention`

, and the unadjusted *R*^{2} value for the outcome variable `behavior`

is .199, which indicates that, collectively `attitude`

and `intention`

explain 19.9% of the variance in `behavior`

. Moving on to the estimate of the indirect effect (which you can find in the *Defined Parameters* section), we see that the indirect effect object we specified (`ab`

) is positive and statistically significant (IE = .212, *p* < .001), which indicates that `intention`

mediates the association between `attitude`

and `intention`

. Again, this estimate was computed using the delta method.

#### 55.2.4.2 Percentile Bootstrapping Method

The percentile bootstrapping method is generally recommended over the delta method, and **thus, my recommendation is to use the percentile bootstrapping method when estimating indirect effects.** Depending upon the processing speed and power of your machine, the approach make take a minute or multiple minutes to perform the number of bootstrap iterations (i.e., re-samples) requested. The initial model specification and model estimation steps are the same as those for the delta method, as we will use these to get the model fit information and direct effect parameter estimates, which don’t require bootstrapping. However, after that, differences emerge.

- Specify the model in the same way as was done previously in preparation for the delta method, and name the object something (e.g.,
`specmod`

). - Estimate the model using the
`sem`

function, just as you would with the delta method, as this will generate the model fit indices and parameter estimates that we will interpret for directional relations; let’s name the model estimation object`fitmod1`

in this example. - Just as you would with the delta method, type the name of the
`summary`

function from base R. As the first argument, type the name of the model estimation object we created in the previous step (`fitmod1`

). As the second and third arguments, respectively, type`fit.measures=TRUE`

and`rsquare=TRUE`

to request the model fit indices and the unadjusted*R*^{2}values for the endogenous variables. - Bootstrapping involves random draws (i.e., re-samples) so the results may vary each time unless we set a “random seed” to allow us to reproduce the results; using the
`set.seed`

function from base R, let’s choose`2019`

as the arbitrary seed value, and enter it as the sole parenthetical argument in the`set.seed`

function. - Now it’s time to generate the bootstrap re-sampling by once again applying the
`sem`

function. This time, let’s use the`<-`

operator to name the model estimation object`fitmod2`

to distinguish it from the one we previously created.

- As the first argument in the
`sem`

function, the name of the model specification object is entered (`specmod`

). - As the second argument,
`data=`

is entered, followed by the name of the data frame object (`df`

). - As the third argument, the approach for estimating the standard errors is indicated by typing
`se=`

followed by`bootstrap`

to request bootstrap standard errors. - As the fourth argument, type
`bootstrap=`

followed by the number of bootstrap re-samples (with replacement) we wish to request. Generally, requesting at least 5,000 re-sampling iterations is advisable, so let’s type`5000`

after`bootstrap=`

. It may take a few minutes for your requested bootstraps to run.

- Type the name of the
`parameterEstimates`

function from the`lavaan`

package to request that percentile bootstrapping be applied to the 5,000 re-sampling iterations requested earlier.

- As the first argument, type the name of the model estimation object (
`fitmod`

). - As the second argument, type
`ci=TRUE`

to request confidence intervals. - As the third argument, type
`level=0.95`

to request the 95% confidence interval be estimated. - As the fourth argument, type
`boot.ci.type="perc"`

to apply the percentile bootstrap approach.

```
# Specify mediation analysis model
specmod <- "
# Path c' (direct effect)
behavior ~ c*attitude
# Path a
intention ~ a*attitude
# Path b
behavior ~ b*intention
# Indirect effect (a*b)
ab := a*b
"
# Estimate model (direct effect parameter estimates)
fitmod1 <- sem(specmod, data=df)
# Request summary of results
summary(fitmod1, fit.measures=TRUE, rsquare=TRUE)
```

```
## lavaan 0.6.15 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 5
##
## Number of observations 199
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 103.700
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -481.884
## Loglikelihood unrestricted model (H1) -481.884
##
## Akaike (AIC) 973.767
## Bayesian (BIC) 990.234
## Sample-size adjusted Bayesian (SABIC) 974.393
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## behavior ~
## attitude (c) 0.029 0.071 0.412 0.680
## intention ~
## attitude (a) 0.484 0.058 8.333 0.000
## behavior ~
## intention (b) 0.438 0.075 5.832 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .behavior 0.698 0.070 9.975 0.000
## .intention 0.623 0.062 9.975 0.000
##
## R-Square:
## Estimate
## behavior 0.199
## intention 0.259
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ab 0.212 0.044 4.778 0.000
```

```
# Set seed
set.seed(2022)
# Estimate model (bootstrap)
fitmod2 <- sem(specmod, data=df, se="bootstrap", bootstrap=5000)
# Request percentile bootstrap 95% confidence intervals
parameterEstimates(fitmod2, ci=TRUE, level=0.95, boot.ci.type="perc")
```

```
## lhs op rhs label est se z pvalue ci.lower ci.upper
## 1 behavior ~ attitude c 0.029 0.065 0.451 0.652 -0.099 0.156
## 2 intention ~ attitude a 0.484 0.054 8.980 0.000 0.378 0.587
## 3 behavior ~ intention b 0.438 0.071 6.166 0.000 0.297 0.576
## 4 behavior ~~ behavior 0.698 0.056 12.375 0.000 0.581 0.800
## 5 intention ~~ intention 0.623 0.055 11.351 0.000 0.515 0.730
## 6 attitude ~~ attitude 0.928 0.000 NA NA 0.928 0.928
## 7 ab := a*b ab 0.212 NA NA NA 0.136 0.300
```

*Note:* If you receive the following error message, you can safely ignore it, as it simply means that some of the bootstrap draws (i.e., re-samples) did not work successfully but most did; we can still be confident in our findings, and this warning does not apply to the original model we estimated prior to the bootstrapping.

**lavaan WARNING: the optimizer warns that a solution has NOT been found!lavaan WARNING: the optimizer warns that a solution has NOT been found!lavaan WARNING: only 4997 bootstrap draws were successful.**

The model fit information and direct relation (non-bootstrapped) parameter estimates are the same as when we estimated the model as part of the delta method. Nonetheless, I repeat those findings here and then extend them with the percentile bootstrap findings for the indirect effect. We have 199 observations (employees) used in this analysis, and the degrees of freedom is equal to 0, which indicates that our model is just-identified. Because the model is just-identified, there are no available degrees of freedom with which to estimate some of the common model fit indices (e.g., RMSEA), so we will not evaluate model fit. Instead, we will move on to interpreting model parameter estimates. The path coefficient (i.e., direct relation, presumed causal relation) between `attitude`

and `intention`

(i.e., path **a**) is statistically significant and positive (*b* = .484, *p* < .001). Further, the path coefficient between `intention`

and `behavior`

(i.e., path **b**) is also statistically significant and positive (*b* = .484, *p* < .001). The direct effect path between `attitude`

and `behavior`

, however, is nonsignificant (*b* = .029, *p* = .680). The (residual) error variances for `behavior`

and `intentions`

are not of substantive interest, so we will ignore them. The unadjusted *R*^{2} values for the endogenous variable `intention`

is .259, which indicates that `attitude`

explains 25.9% of the variance in `intention`

, and the unadjusted *R*^{2} value for the outcome variable `behavior`

is .199, which indicates that, collectively `attitude`

and `intention`

explain 19.9% of the variance in `behavior`

. Moving on, we get the delta method estimate of the indirect effect (which you can find in the *Defined Parameters* section), and we see that the indirect effect object we specified (`ab`

) is positive and statistically significant (IE = .212, *p* < .001), which indicates that `intention`

mediates the association between `attitude`

and `intention`

. Again, however, that is the delta method estimate of the indirect effect, so let’s now take a look at the percentile bootstrap estimate of the indirect effect. At the very end of the output appears a table containing, among other things, the 95% confidence intervals for various parameters. We are only interested in the very last row which displays the 95% confidence interval for the `ab`

object we specified using the product of coefficients approach. The point estimate for the indirect effect is .212, and 95% confidence interval does *not* include zero and positive, so we can conclude that the indirect effect of `attitude`

on `behavior`

via `intention`

is statistically significant and positive (95% CI[.136, 300]). In other words, the percentile bootstrapping method indicates that `intention`

mediates the association between `attitude`

and `behavior`

. If this confidence interval included zero in its range, then we would conclude that there is no evidence that `intention`

is a mediator.

### References

*Journal of Personality and Social Psychology*51 (6): 1173–82.

*Journal of Applied Psychology*100 (1): 194–202.

*Evaluation Review*17 (2): 144–58.

*Psychological Methods*7 (1): 83–104.

*Multivariate Behavioral Research*30 (1): 41–62.

*Journal of Statistical Software*48 (2): 1–36. https://www.jstatsoft.org/v48/i02/.

*Readr: Read Rectangular Text Data*. https://CRAN.R-project.org/package=readr.