[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-269481-105":59,"doc-detail-269481-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","conducting-a-hierarchical-multiple-regression-in-jamovi","Conducting a Hierarchical Multiple Regression in jamovi","","Guidance for running a hierarchical multiple regression in jamovi using a Star Wars fandom survey example. The workflow covers defining variables (ordinal Likert items as factors and continuous predictors as covariates), configuring Linear Regression in the Analyses menu, building a two-block hierarchical model, and selecting model fit outputs such as adjusted R², overall F, and the F-change test. It also explains coefficient reporting options, how to toggle Model 1 vs Model 2 results, and how to obtain partial correlations for semi-partial effects via a separate Partial Correlation analysis.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/conducting-a-hierarchical-multiple-regression-in-jamovi/269481/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/conducting-a-hierarchical-multiple-regression-in-jamovi/269481.png","ImageObject",300,407,{"name":92,"@type":93},"McQueen","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How should jamovi classify the survey variables for hierarchical regression?","Question",{"text":113,"@type":114},"Treat continuous predictors as covariates and ordinal Likert items as factors. In the example, age is continuous and the fandom ratings and opinion ratings are ordinal Likert variables.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How do you set up a two-block hierarchical model in jamovi?",{"text":118,"@type":114},"In Linear Regression, use Model Builder to add Block 1 with the first set of predictors, then create Block 2 with the additional predictors. The output then supports comparing Block 1 vs Block 2 and testing added explained variance.",{"name":120,"@type":111,"acceptedAnswer":121},"Which outputs indicate overall fit and the added variance from Block 2?",{"text":122,"@type":114},"Select adjusted R² and the Overall Model F test to get F tests for Block 1 and Block 2, and include the Fchange test to assess whether Block 2 explains significant additional variance beyond Block 1.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},269481,1789452327,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":41,"preview_page_count":4},5909890329169,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Conducting a Hierarchical Multiple Regression in jamovi  \nStar Wars is a much beloved franchise. Some characters and plots however are more beloved than others by the fan base. One particularly divisive character is JarJar Binks, who first appeared in the first of the three prequels to the original trilogy, The Phantom Menace in 1999. A researcher decided to explore what factors predict a love for this character and so conducted a survey to explore this. To participate in the survey participants had to have seen JarJar Binks in at least one form. This could either be in the live action movies The Phantom Menace, Attack of the Clones or Revenge of the Sith (also known as Episodes 1, 2 and 3 respectively) or in the animated series The Clone Wars. The survey included questions such as “How old were you when you first saw JarJar Binks?” and “How would you rate the extent of your Star Wars fandom” which the researcher assumed would strongly relate to attitudes towards JarJar. However, she was particularly interested in whether, once first accounting for variance in JarJar Binks attitudes from these factors, additional variance could be accounted for via a shared agreement with other somewhat maligned beliefs about Star Wars characters and plotlines. To this end, the survey also asked participants to rate their level of agreement that the Kylo Ren and Rey romance makes sense, and that Boba Fett is over-rated. Responses from 100 participants were obtained.  \nStep 1 – Taking a look at the data.  \nOur three Star Wars opinion variables (JarJar Binks is awesome, The Kylo Ren and Rey romance makes sense, and Boba Fett is over-rated) have been specified as ordinal variables in Measure type. The anchor points of the Likert scale (1 = strongly disagree, 5 = strongly agree) have also been entered  \nThe age a participant was when they first saw JarJar Binks in a Star Wars movie or The Clone Wars is specified as a continuous variable  \nAnd finally, the rating of the extent of Star Wars fandom is specified as an ordinal variable with the anchor points of the Likert scale (1 = Could not care less about the Star Wars, 5 = Adoration of the franchise) also entered  \nConducting a Hierarchical Multiple Regression in jamovi  \nStep 2 – Navigating to the Linear Regression menu.  \nOn the Analyses tab select the Regression menu, then select Linear Regression.  \nStep 3 – Selecting analysis options  \nThe first thing we need to do is tell jamovi what variable is our criterion/dependent variable and which are our predictors. It is important to note that jamovi refers to continuous predictors as covariates and categorical predictors as factors. In our case all our predictors are continuous so we will need to move them across to the covariates box.  \nFrom the drop down lists we’ll ask for options from Model Builder, Model Fit and Model Coefficients.  \nConducting a Hierarchical Multiple Regression in jamovi  \nUnder Model Builder we will be setting up our multiple regression as a hierarchical model by telling jamovi how many Blocks or steps we want in our model, as well as which of our predictors to put in which of those Blocks.  \nInitially you will see under the heading Blocks on the right hand side a heading for Block 1 and a space to move variables to. Here we want to drag the variables we want in our first Block which are the age when first saw JarJar and Star Wars fandom rating.  \nTo create additional Blocks we click on“Add New Block”. Doing this gives us Block 2, which we can now add the two Star Wars opinion ratings about Kylo and Rey and Boba Fett. We’ll stop here as we only need two Blocks but you can see you could continue adding additional Blocks if you wanted to.  \nUnder Model Fit, in addition to the R and R2 already set as a default, we’ll ask for adjusted R2 and the Overall Model F test. This will give us three F tests, one for the Block 1 model, one for the Block 2 model (which includes both Block 1 and 2 predictors), and an Fchange test that assess","cbCaid3xMbakZzxW","https://ap.wps.com/l/cbCaid3xMbakZzxW","pdf",1532905,12,"English","# Step 1 – Taking a look at the data\n## Variable types and Likert anchors\n# Step 2 – Navigating to the Linear Regression menu\n# Step 3 – Selecting analysis options\n## Model Builder: blocks and predictors\n## Model Fit: adjusted R² and F tests\n## Model Coefficients: estimates, betas, and confidence intervals\n## Obtaining results for Model 1 and Model 2\n# Additional steps for r²a(b,c) via Partial Correlation\n## Running Partial Correlation one analysis at a time","[{\"question\":\"How should jamovi classify the survey variables for hierarchical regression?\",\"answer\":\"Treat continuous predictors as covariates and ordinal Likert items as factors. In the example, age is continuous and the fandom ratings and opinion ratings are ordinal Likert variables.\"},{\"question\":\"How do you set up a two-block hierarchical model in jamovi?\",\"answer\":\"In Linear Regression, use Model Builder to add Block 1 with the first set of predictors, then create Block 2 with the additional predictors. The output then supports comparing Block 1 vs Block 2 and testing added explained variance.\"},{\"question\":\"Which outputs indicate overall fit and the added variance from Block 2?\",\"answer\":\"Select adjusted R² and the Overall Model F test to get F tests for Block 1 and Block 2, and include the Fchange test to assess whether Block 2 explains significant additional variance beyond Block 1.\"}]","Conducting a Hierarchical Multiple Regression in jamovi | PDF"]