[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-160321-en":3,"doc-seo-160321-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},160321,8796096645457,"Arica Lee","https://ap-avatar.wpscdn.com/avatar/800003749518d68ffe3?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345340919836971",8,"Research & Report","Website Worksheets - R - Principal Components Analysis - Robustness Checks with Survey Research 2","Guide 2 in a 3-part Q-Step R “How to” series explains how to use principal components analysis (PCA) as a robustness check in survey research. It introduces PCA’s purpose for reducing correlated questionnaire items into fewer uncorrelated components, explains when to choose correlation versus covariance matrices, and walks through an RStudio example using a 20-item values questionnaire. The guide covers preparing the data, running princomp(), interpreting explained variance and loadings(), and visualizing results with scree plots and biplots.","Q-Step R ‘How to’ Guides:  \nRobustness Checks with Survey Research 2: Principal Components Analysis  \nCreator: Dr James Weinberg  \nIn most social science disciplines, quantitative researchers will work with survey research to develop, build or test theories. In order for such survey research to be as rigorous as possible, it is important that we conduct robustness checks on our results in order to assess the validity of our theoretical claims. Therefore, this guide (the second in a series of 3) focuses on principal components analysis with questionnaire items.  \nIn this guide, you will be given a simple contextual description of principal components analysis and when/why it should be used, as well as an example worked through in Rstudio. This guide assumes a basic competency in R from the start -for example users should already be comfortable with assigning and calling objects.  \nThe example used in this guide is based on a dataset of people’s Basic Human Values. Basic values are a personality characteristic that can be measured by psychometric surveys. In this instance, a 20 item questionnaire was administered to 107 people, with two items each tapping one of the ten basic values in the theory. These ten values can be clustered further into 4 higher order values. This guide will use principal components analysis to assess the reliability of the questionnaire items used to test this theory.  \n0 2  \nPrincipal Components Analysis What is it and when to use it?  \nPrincipal component analysis (PCA) is a method of data reduction or compression that is used to turn a conceivably large dataset of (potentially) correlated variables (or questionnaire items) into a smaller number of uncorrelated variables known as principal components.  \nPCA is performed on a square symmetric matrix. This can be a SSCP matrix (pure sums of squares and cross products), Covariance matrix (scaled sums of squares and cross products), or Correlation matrix (sums of squares and cross products from standardized data) . You should only use a correlation matrix if the variances differ substantially across indiviudal items or they are measured in different units.  \nPCA will reduce the dataset to a series of principal components. The first principal component accounts for as much variabality in the data as possible, and each successive component accounts for as much of the remaining variability as possible. You should use PCA to determine the fewest possible dimensions you can statistically use in further analysis of your data.  \nExample:  \nStart by setting your working directory and reading the data file containing your questionnaire responses. You can conduct PCA using the “psych” package.  \nIt is likely that your survey contains a lot more items than you need for this analysis (i. e. socio-demographic data or another item battery) . For example, in my dataset I have 165 variables but for the purpose of the current test, I am only interested in the 20 questions related to respondents’ basic values. Therefore, you need to isolate these data as a new matrix in your global environment (top right panel in Rstudio) .  \nI am going to organise my survey battery for basic values into a new matrix (x) using the cbind function. Once you have created this, you can use the summary() function to check the descriptive statistics for each item. You can also use the cor() function check the inter-item correlations before you conduct PCA. Your correlations should be higher between items that you think tap the same latent (unobservable) constructs. If you have any missing values in your dataset, remember to remove these beforehand or use the na. omit() function to tell R that they should be ignored.  \n0 3  \nOnce you’ve re-organised your data, you are ready to conduct PCA. You can do this using the ‘princomp’ function in R. Remember to omit any missing values in your dataset. Here I have used ‘scores = TRUE’ to tell R that the score on each principal component should be calculated","cbCaiuzUAvQ3eFyL","https://ap.wps.com/l/cbCaiuzUAvQ3eFyL","pdf",519886,1,7,"English","en",105,"# Robustness Checks with Survey Research 2: Principal Components Analysis\n## What is it and when to use it?\n## Example in RStudio\n## Visualising PCA","[{\"question\":\"What does principal components analysis do in survey research?\",\"answer\":\"Principal components analysis reduces a large set of potentially correlated variables, such as questionnaire items, into fewer uncorrelated principal components.\"},{\"question\":\"When should a researcher use a correlation matrix versus covariance matrix for PCA?\",\"answer\":\"Use a correlation matrix when item variances differ substantially across individuals or items are measured in different units; otherwise covariance or SSCP matrices are appropriate.\"},{\"question\":\"How are PCA results typically interpreted in this guide?\",\"answer\":\"The guide interprets explained variance by components and uses loadings() to examine correlations between variables and component factors, then visualizes outcomes with a screeplot or biplot.\"}]","Website Worksheets - R - Principal Components Analysis - Robustness Checks with Survey Research 2 | PDF",1788053937,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"website-worksheets-r-principal-components-analysis-robustness-checks-with-survey-research-2","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/website-worksheets-r-principal-components-analysis-robustness-checks-with-survey-research-2/160321/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-30",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does principal components analysis do in survey research?","Question",{"text":75,"@type":76},"Principal components analysis reduces a large set of potentially correlated variables, such as questionnaire items, into fewer uncorrelated principal components.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"When should a researcher use a correlation matrix versus covariance matrix for PCA?",{"text":80,"@type":76},"Use a correlation matrix when item variances differ substantially across individuals or items are measured in different units; otherwise covariance or SSCP matrices are appropriate.",{"name":82,"@type":73,"acceptedAnswer":83},"How are PCA results typically interpreted in this guide?",{"text":84,"@type":76},"The guide interprets explained variance by components and uses loadings() to examine correlations between variables and component factors, then visualizes outcomes with a screeplot or biplot.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]