2A Lab 1 Week 2

This is the pair coding activity related to Chapter 1.

The data we will be using in the upcoming lab activities is a randomised controlled trials experiment by Binfet et al. (2021) that was conducted in Canada.

Citation

Binfet, J. T., Green, F. L. L., & Draper, Z. A. (2021). The Importance of Client–Canine Contact in Canine-Assisted Interventions: A Randomized Controlled Trial. Anthrozoös, 35(1), 1–22. https://doi.org/10.1080/08927936.2021.1944558

Abstract

Researchers have claimed that canine-assisted interventions (CAIs) contribute significantly to bolstering participants’ wellbeing, yet the mechanisms within interactions have received little empirical attention. The aim of this study was to assess the impact of client–canine contact on wellbeing outcomes in a sample of 284 undergraduate college students (77% female; 21% male, 2% non-binary). Participants self-selected to participate and were randomly assigned to one of two canine interaction treatment conditions (touch or no touch) or to a handler-only condition with no therapy dog present. To assess self-reports of wellbeing, measures of flourishing, positive and negative affect, social connectedness, happiness, integration into the campus community, stress, homesickness, and loneliness were administered. Exploratory analyses were conducted to assess whether these wellbeing measures could be considered as measuring a unidimensional construct. This included both reliability analysis and exploratory factor analysis. Based on the results of these analyses we created a composite measure using participant scores on a latent factor. We then conducted the tests of the four hypotheses using these factor scores. Results indicate that participants across all conditions experienced enhanced wellbeing on several measures; however, only those in the direct contact condition reported significant improvements on all measures of wellbeing. Additionally, direct interactions with therapy dogs through touch elicited greater wellbeing benefits than did no touch/indirect interactions or interactions with only a dog handler. Similarly, analyses using scores on the wellbeing factor indicated significant improvement in wellbeing across all conditions (handler-only, d = 0.18, p = 0.041; indirect, d = 0.38, p < 0.001; direct, d = 0.78, p < 0.001), with more benefit when a dog was present (d = 0.20, p < 0.001), and the most benefit coming from physical contact with the dog (d = 0.13, p = 0.002). The findings hold implications for post-secondary wellbeing programs as well as the organization and delivery of CAIs.

However, we accessed the data via Ciaran Evans’ github (https://github.com/ciaran-evans/dog-data-analysis). Evans et al. (2023) published a paper that reused the Binfet data for teaching statistics and research methods. If anyone is interested, the accompanying paper is:

Evans, C., Cipolli, W., Draper, Z. A., & Binfet, J. T. (2023). Repurposing a Peer-Reviewed Publication to Engage Students in Statistics: An Illustration of Study Design, Data Collection, and Analysis. Journal of Statistics and Data Science Education, 31(3), 236–247. https://doi.org/10.1080/26939169.2023.2238018

There are a few changes that Evans and we made to the data:

• Evans removed the demographics ethnicity and gender to make the study data available while protecting participant privacy. Which means we’ll have limited demographic variables available, but we will make do with what we’ve got.
• We modified some of the responses in the raw data csv - for example, we took out impossible response values and replaced them with NA.
• We replaced some of the numbers with labels to increase the difficulty in the dataset for Chapter 2 and Chapter 3.

Now, let’s get started!!!

Task 1: Create a project folder for the lab activities

Since we will be working with the same data throughout semester 1, create a separate project for the lab data. Name it something useful, like lab_data or dogs_in_the_lab. Make sure you are not placing it within the project you have already created today. If you need guidance, see Section 1.2.

Task 2: Create a new .Rmd file

… and name it something useful. If you need help, have a look at Section 1.3.

Task 3: Download the data

Download the data here: data_pair_coding. The zip folder contains the raw data file with responses to individual questions, a cleaned version of the same data in long format and wide format, and the codebook describing the variables in the raw data file and the long format.

Unzip the folder and place the data files in the same folder as your project.

Task 4: Familiarise yourself with the data

Open the data files, look at the codebook, and perhaps skim over the original Binfet article (methods in particular) to see what kind of measures they used.

Read in the raw data file as dog_data_raw and the cleaned-up data (long format) as dog_data_long. See if you can answer the following questions.

library(tidyverse)

dog_data_raw <- read_csv("dog_data_raw.csv")
dog_data_long <- read_csv("dog_data_clean_long.csv")

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.2     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.1.0     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Rows: 284 Columns: 136
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (41): GroupAssignment, L2_1, L2_2, L2_3, L2_4, L2_5, L2_6, L2_7, L2_8, L...
dbl (95): RID, Age_Yrs, Year_of_Study, Live_Pets, Consumer_BARK, S1_1, HO1_1...

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.
Rows: 568 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (4): GroupAssignment, Year_of_Study, Live_Pets, Stage
dbl (12): RID, Age_Yrs, Consumer_BARK, Flourishing, PANAS_PA, PANAS_NA, SHS,...

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.

Task 5: Question Time

Now that you have familiarised yourself with the data, you can answer the following questions.

Question 1

How many participants took part in the study?

Explain this answer

You can see this from dog_data_raw. Each participant ID is on a single row meaning the number of observations is the number of participants.

If you look at dog_data_long, there are 568 observations. Each participant answered the questionnaires pre and post intervention, resulting in 2 rows per participant ID. This means you’d have to divide the number of observations by 2 to get to the number of participants.

Question 2

How many different questionnaires did the participants answer?

Explain this answer

The Binfet paper (e.g., Methods section and/or abstract), the codebook, and dog_data_long show it’s 9 questionnaires - Flourishing scale (variable Flourishing), the UCLS Loneliness scale Version 3 (Loneliness), Positive and Negative affect scale (PANAS_PA and PANAS_NA), the Subjective Happiness scale (SHS), the Social connectedness scale (SCS), and 3 scales with 1 question each, i.e., perception of stress levels (Stress), self-reported level of homesickness (Homesick), and integration into the campus community (Engagement).

However, if you thought PANAS_PA and PANAS_NA are a single questionnaire, 8 was also acceptable as an answer here.

Question 3

dog_data_raw has character columns and that are labeled as double.

Explain this answer

You can get information on column specification when reading in the data.

message from read_csv() when reading in the data

Question 4

Select from the dropdown menu the variable type and their data types for each of the columns.

In dog_data_raw:

Column	Variable type	Data type
RID	continuous nominal ordinal	numeric character logical factor
GroupAssignment	continuous nominal ordinal	numeric character logical factor
Age_Yrs	continuous nominal ordinal	numeric character logical factor
Year_of_Study	continuous nominal ordinal	numeric character logical factor
PN1_1	continuous nominal ordinal	numeric character logical factor
L2_1	continuous nominal ordinal	numeric character logical factor
SC2_1	continuous nominal ordinal	numeric character logical factor

In dog_data_long:

Column	Variable type	Data type
Year_of_Study	continuous nominal ordinal	numeric character logical factor
Stage	continuous nominal ordinal	numeric character logical factor
Loneliness	continuous nominal ordinal	numeric character logical factor
SCS	continuous nominal ordinal	numeric character logical factor

Explain this answer

The column SC2_1 contains both numbers and words. Because R can store numbers as characters but cannot store words as numbers, the whole column is coded as chr.

Most of the questionnaire items were answered on a Likert scale (e.g., strongly disagree to strongly agree, or never, rarely, sometimes, often). These are ordinal variables because the categories (whether shown as words or numbers) represent an order but not equal distances (see dog_data_raw). However, when you add up or average several items to calculate an overall questionnaire score (like Loneliness or SCS in dog_data_long), that score can be treated as continuous data.