Blog Post Template

blog post

Author

Caitlin Rowley

Published

September 18, 2022

What research questions do you want to study using computational text analysis?

While I am still working on narrowing the scope of a potential research question, I would like to study public perception of farm animal welfare laws, and whether there is a correlation between perception and regulatory change.

Academic article 1:

A Sytematic Review of Public Attitudes, Perceptions, and Behaviours Towards Production Diseases Associated with Farm Animal Welfare (https://link.springer.com/article/10.1007/s10806-016-9615-x)

What is the research question?

This research was designed to (1) establish the public’s attitudes toward farm animal welfare and (2) determine the public’s attitudes towards interventions related to reducing production-related diseases that are prevalent in intensive farming environments.

What data are used?

The data referenced in this article come from four databases: (1) Scopus, (2) ISI Web of Knowledge, (3) AgEcon Search, and (4) Google Scholar.

How are the data collected?

Data from these sources were selected using combinations of search terms and strings specific to each database. Authors within the field were also contacted for insight regarding ‘grey literature’ or unpublished works. Only studies conducted within the past 20 years were included; those within that pool were further screened based on predetermined selection criteria. 80 studies in total were used, 62 of which were quantitative. Among these, 43 were surveys, 17 were studies relative to consumers’ willingness to pay (WTP), 1 was based on modelling existing data, and 1 was a display matrix. Among the 9 qualitative study, 4 were focus groups, 4 were interviews, and 1 was a citizens’ panel. The remaining 9 studies applied mixed methods research.

Hypotheses?

The authors indicated that data illustrates a disconnect between citizens’ concern for farm animal welfare and consumers’ willingness to pay for products that meet or exceed minimum welfare standards.

What methods are used?

Researchers used open-coding analysis in NVivo, as well as a separate tool that was developed to analyze both qualitative and quantitative studies. This tool considered construct validity, internal and external validity, and the reliability of included studies.

What are the findings?

Findings from this study indicated that despite the majority of respondents expressing concern for farm animal welfare (46%-86%), this was not considered to be a priority within the context of consumerism. This was largely due to barriers such as the cost and availability of such products. There was also minimal concerns associated with animal diseases, other than discontent regarding the use of antibiotics.

What’s my take on this?

I am not particularly surprised by these findings, having conducted an extensive research project on the topic of farm animal welfare policy several years ago. However, I’d be interested to see if there has been a shift in public opinion by referencing more recent and widespread survey data. I’d also like apply some of the research methods used in this study to a more local context (the majority of the data came studies conducted in Europe).

My research questions and potential data sources:

This study has helped inform potential revisions in my research question, specifically in terms of narrowing the scope so that I can identify pertinent data sources. For example, I will consider factors that may contribute to public perception relative to farm animal welfare, such as knowledge/awareness, health concerns, economic concerns, and concerns about human treatment. Similar to this study, I could use AgEcon Search as a reference, as well as the NIH’s National Library of Medicine, which contains a variety of resources (both qualitative and quantitative) used to inform literature on public perceptions of farm animal welfare (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5126776/).

Remaining questions:

I would like to learn more about alternative forms of qualitative coding. I am familiar with NVivo and manual open-coding, but I’d like to familiarize myself with more exact methods.

Academic article 2:

Ag-Gag Laws: A Shift in the Wrong Direction for Animal Welfare on Farms - https://digitalcommons.law.ggu.edu/cgi/viewcontent.cgi?article=2126&context=ggulrev

What is the research question?

This study seeks to establish that despite increased awareness and concern for farm animal welfare in Western society, variation in terms of perception of farm animal welfare is largely dependent on citizens’ involvement with agriculture and livestock production.

What data are used?

This study uses survey data comprising 459 respondents.

How are the data collected?

These data were collected through self-administered questionnaires. A quota sampling procedure was applied, using gender, age, living environment, and province as control characteristics. Respondents were first selected through a series of web-based questionnaires which were then supplemented by a more targeted distribution of paper questionnaires.

Hypotheses?

The authors indicate that there is a disconnect between the livestock production industry and the general public related to animal welfare, specifically in terms of (1) convictions, (2) values, (3) norms, (4) knowledge, and (5) interests.

What methods are used?

In addition to establishing selection criteria, the authors utilized SPSS for running segmentation analyses. Specifically, the authors used hierarchical clustering using Ward’s Method, and conducted K-means cluster analyses to obtain segments. They also conducted bivariate analyses including cross tabulations with Chi^2-statistics, Independent Samples T-test and One-Way ANOVA comparison of means.

What are the findings?

The findings of this study were based on six clusters of respondents that were categorized based on their perceptions of the following determinants: (1) structural determinants of animal welfare, (2) consumption frequency of meat and meat substitutes, (3) knowledge about animal welfare, and (4) attitudes toward information about animal welfare. Results were largely in line with the authors’ hypothesis; there was a significant link between respondents’ interpretations, and thus their perceptions, on farm animal welfare and their level of involvement with animal welfare (e.g., those involved with agriculture and livestock production). Additionally, the authors also evaluated the efficacy of marketing opportunities based on clusters’ perceptions and willingness to pay for ethically-sourced products.

What’s my take on this?

I was very intrigued by the multi-pronged approach to this research question, as well as the identified determinants used as a foundation for the segmentation of survey results. I also appreciated that the ways in which the authors clustered respondents provided a path for further research, such as marketing opportunities.

My research questions and potential data sources:

This study provides a framework that I think will help refine my research question, specifically in terms of established literature on the topic of farm animal welfare (e.g., the established determinants). In terms of data sources, because this study referenced data from the authors’ own survey, I would seek out data from public surveys through resources such as Pew Research Center, the Journal of the American Veterinary Medical Association, and Johns Hopkins Bloomberg School of Public Health.

Remaining questions:

I would like to understand the best analyses to run in terms of closed-ended survey questions. Are the bivariate analyses listed above considered best practice?

Code

library(tidyverse)

knitr::opts_chunk$set(echo = TRUE)

Instructions

This document provides yaml header inforamtion you will need to replicate each week to submit your homework or other blog posts. Please observe the following conventions:

Save your own copy of this template as a blog post in the posts folder, naming it FirstLast_hwX.qmd
Edit the yaml header to change your author name - use the same name each week
include a description that is reader friendly
update the category list to indicate the type of submission, the data used, the main packages or techniques, your name, or any thing else to make your document easy to find
edit as a normal qmd/rmd file

Code

x <- c(2,3,4,5)
mean(x)

[1] 3.5

Rendering your post

When you click the Render button a document will be generated that includes both content and the output of embedded code.

Warning

Be sure that you have moved your *.qmd file into the posts folder BEFORE you render it, so that all files are stored in the correct location.

Important

Only render a single file - don’t try to render the whole website!

Pilot Student Blogs

We are piloting a workflow including individual student websites with direted and limited pull requests back to course blogs. Please let us know if you would like to participate.

Reading in data files

The easiest data source to use - at least initially - is to choose something easily accessible, either from our _data folder provided, or from an online source that is publicly available.

Using Other Data

If you would like to use a source that you have access to and it is small enough and you don’t mind making it public, you can copy it into the _data file and include in your commit and pull request.

Using Private Data

If you would like to use a proprietary source of data, that should be possible using the same process outlined above. There may initially be a few issues. We hope to have this feature working smoothly soon!

--- title: "Blog Post Template" author: "Caitlin Rowley" desription: "Blog Post 1" date: "09/18/2022" format: html: toc: true code-fold: true code-copy: true code-tools: true categories: - blog post --- # What research questions do you want to study using computational text analysis? While I am still working on narrowing the scope of a potential research question, I would like to study public perception of farm animal welfare laws, and whether there is a correlation between perception and regulatory change. # Academic article 1: A Sytematic Review of Public Attitudes, Perceptions, and Behaviours Towards Production Diseases Associated with Farm Animal Welfare (https://link.springer.com/article/10.1007/s10806-016-9615-x) # What is the research question? This research was designed to (1) establish the public's attitudes toward farm animal welfare and (2) determine the public's attitudes towards interventions related to reducing production-related diseases that are prevalent in intensive farming environments. # What data are used? The data referenced in this article come from four databases: (1) Scopus, (2) ISI Web of Knowledge, (3) AgEcon Search, and (4) Google Scholar. # How are the data collected? Data from these sources were selected using combinations of search terms and strings specific to each database. Authors within the field were also contacted for insight regarding 'grey literature' or unpublished works. Only studies conducted within the past 20 years were included; those within that pool were further screened based on predetermined selection criteria. 80 studies in total were used, 62 of which were quantitative. Among these, 43 were surveys, 17 were studies relative to consumers' willingness to pay (WTP), 1 was based on modelling existing data, and 1 was a display matrix. Among the 9 qualitative study, 4 were focus groups, 4 were interviews, and 1 was a citizens' panel. The remaining 9 studies applied mixed methods research. # Hypotheses? The authors indicated that data illustrates a disconnect between citizens' concern for farm animal welfare and consumers' willingness to pay for products that meet or exceed minimum welfare standards. # What methods are used? Researchers used open-coding analysis in NVivo, as well as a separate tool that was developed to analyze both qualitative and quantitative studies. This tool considered construct validity, internal and external validity, and the reliability of included studies. # What are the findings? Findings from this study indicated that despite the majority of respondents expressing concern for farm animal welfare (46%-86%), this was not considered to be a priority within the context of consumerism. This was largely due to barriers such as the cost and availability of such products. There was also minimal concerns associated with animal diseases, other than discontent regarding the use of antibiotics. # What's my take on this? I am not particularly surprised by these findings, having conducted an extensive research project on the topic of farm animal welfare policy several years ago. However, I'd be interested to see if there has been a shift in public opinion by referencing more recent and widespread survey data. I'd also like apply some of the research methods used in this study to a more local context (the majority of the data came studies conducted in Europe). # My research questions and potential data sources: This study has helped inform potential revisions in my research question, specifically in terms of narrowing the scope so that I can identify pertinent data sources. For example, I will consider factors that may contribute to public perception relative to farm animal welfare, such as knowledge/awareness, health concerns, economic concerns, and concerns about human treatment. Similar to this study, I could use AgEcon Search as a reference, as well as the NIH's National Library of Medicine, which contains a variety of resources (both qualitative and quantitative) used to inform literature on public perceptions of farm animal welfare (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5126776/). # Remaining questions: I would like to learn more about alternative forms of qualitative coding. I am familiar with NVivo and manual open-coding, but I'd like to familiarize myself with more exact methods. # Academic article 2: Ag-Gag Laws: A Shift in the Wrong Direction for Animal Welfare on Farms - https://digitalcommons.law.ggu.edu/cgi/viewcontent.cgi?article=2126&context=ggulrev # What is the research question? This study seeks to establish that despite increased awareness and concern for farm animal welfare in Western society, variation in terms of perception of farm animal welfare is largely dependent on citizens' involvement with agriculture and livestock production. # What data are used? This study uses survey data comprising 459 respondents. # How are the data collected? These data were collected through self-administered questionnaires. A quota sampling procedure was applied, using gender, age, living environment, and province as control characteristics. Respondents were first selected through a series of web-based questionnaires which were then supplemented by a more targeted distribution of paper questionnaires. # Hypotheses? The authors indicate that there is a disconnect between the livestock production industry and the general public related to animal welfare, specifically in terms of (1) convictions, (2) values, (3) norms, (4) knowledge, and (5) interests. # What methods are used? In addition to establishing selection criteria, the authors utilized SPSS for running segmentation analyses. Specifically, the authors used hierarchical clustering using Ward's Method, and conducted K-means cluster analyses to obtain segments. They also conducted bivariate analyses including cross tabulations with Chi^2-statistics, Independent Samples T-test and One-Way ANOVA comparison of means. # What are the findings? The findings of this study were based on six clusters of respondents that were categorized based on their perceptions of the following determinants: (1) structural determinants of animal welfare, (2) consumption frequency of meat and meat substitutes, (3) knowledge about animal welfare, and (4) attitudes toward information about animal welfare. Results were largely in line with the authors' hypothesis; there was a significant link between respondents' interpretations, and thus their perceptions, on farm animal welfare and their level of involvement with animal welfare (e.g., those involved with agriculture and livestock production). Additionally, the authors also evaluated the efficacy of marketing opportunities based on clusters' perceptions and willingness to pay for ethically-sourced products. # What's my take on this? I was very intrigued by the multi-pronged approach to this research question, as well as the identified determinants used as a foundation for the segmentation of survey results. I also appreciated that the ways in which the authors clustered respondents provided a path for further research, such as marketing opportunities. # My research questions and potential data sources: This study provides a framework that I think will help refine my research question, specifically in terms of established literature on the topic of farm animal welfare (e.g., the established determinants). In terms of data sources, because this study referenced data from the authors' own survey, I would seek out data from public surveys through resources such as Pew Research Center, the Journal of the American Veterinary Medical Association, and Johns Hopkins Bloomberg School of Public Health. # Remaining questions: I would like to understand the best analyses to run in terms of closed-ended survey questions. Are the bivariate analyses listed above considered best practice? ```{r} #| label: setup #| warning: false library(tidyverse) knitr::opts_chunk$set(echo = TRUE) ``` ## Instructions This document provides yaml header inforamtion you will need to replicate each week to submit your homework or other blog posts. Please observe the following conventions: - Save your own copy of this template as a blog post in the `posts` folder, naming it `FirstLast_hwX.qmd` - Edit the yaml header to change your author name - use the same name each week - include a *description* that is reader friendly - update the *category* list to indicate the type of submission, the data used, the main packages or techniques, your name, or any thing else to make your document easy to find - edit as a normal qmd/rmd file ```{r} x <- c(2,3,4,5) mean(x) ``` ## Rendering your post When you click the **Render** button a document will be generated that includes both content and the output of embedded code. :::{.callout-warning} Be sure that you have moved your `*.qmd` file into the `posts` folder **BEFORE** you render it, so that all files are stored in the correct location. ::: :::{.callout-important} Only render a single file - don't try to render the whole website! ::: :::{.callout-note} ## Pilot Student Blogs We are piloting a workflow including individual student websites with direted and limited pull requests back to course blogs. Please let us know if you would like to participate. ::: ## Reading in data files The easiest data source to use - at least initially - is to choose something easily accessible, either from our `_data` folder provided, or from an online source that is publicly available. :::{.callout-tip} ## Using Other Data If you would like to use a source that you have access to and it is small enough and you don't mind making it public, you can copy it into the `_data` file and include in your *commit* and *pull request*. ::: :::{.callout-tip} ## Using Private Data If you would like to use a proprietary source of data, that should be possible using the same process outlined above. There may initially be a few issues. We hope to have this feature working smoothly soon! :::