<\/p>\n\n\n\n
IBM SPSS Statistics 29, which is now available for the Swarthmore College community, includes new customer-requested linear ordinary least squares (OLS) regression and parametric accelerated failure time (AFT) model statistical procedures, improved open-source extension integration, UI enhancements, new data visualization features, and other enhancements that are designed to improve everyday usability. Most importantly for Swarthmore folks is that there is a patch for compatibility with MacOS 14 (Sonoma)!<\/strong><\/p>\n\n\n\n The linear elastic net extension<\/strong> procedure estimates regularized linear regression models for a dependent variable on one or more independent variables. <\/p>\n\n\n\n The linear lasso extension<\/strong> estimates L1 loss regularized linear regression models for a dependent variable on one or more independent variables, and includes optional modes to display trace plots and to select the alpha hyperparameter value based on cross validation. <\/p>\n\n\n\n The linear ridge extension<\/strong> procedure estimates L2 or squared loss regularized linear regression models for a dependent variable on one or more independent variables, and includes optional modes to display trace plots and to select the alpha hyperparameter value based on cross validation. <\/p>\n\n\n\n The new procedure invokes the parametric survival models procedure with non-recurrent life time data. Parametric survival models assume that survival time follow a known distribution and this analysis fits accelerated failure time models with their model effects proportional with respect to survival time.<\/p>\n\n\n\n Pseudo-R-squared measures and the intra-class correlation coefficient are now included in linear mixed models and generalized linear mixed models output when appropriate. The coefficient of determination R2 is a commonly reported statistic, because it represents the proportion of variance explained by a linear model. The intra-class correlation coefficient (ICC) is a related statistic that quantifies the proportion of variance explained by a random grouping factor in multilevel and hierarchical data.<\/p>\n\n\n\n The Graphboard Template Chooser includes a new violin plot<\/strong>, which is a hybrid of the box and kernel density plots. Violin plots show peaks in the data and are used to visualize the distribution of numerical data. Unlike a box plot that can only show summary statistics, violin plots depict summary statistics and the density of each variable.<\/p>\n\n\n\n Two new workbook toolbar items have been added: Show\/Hide all syntax windows<\/strong> and Clear all output<\/strong>. There is also a new button on the status bar to switch between classic (output and syntax) and workbook modes. <\/p>\n\n\n\n The search feature now provides options for entering terms directly in a toolbar field and for viewing results in a drop-down pane. <\/p>\n\n\n\n Unselected cases are no longer hidden in the data editor when a subset of cases is selected and the unselected cases are not discarded. This represents a return to the behavior of Statistics 27.0.1 and earlier versions. <\/p>\n\n\n\n Furthermore, Python 3.10.4 and R 4.2.0 are also installed with IBM SPSS Statistics 29.<\/p>\n\n\n\n As always, please reach out to ITS<\/a> for any support questions regarding SPSS and check our KnowledgeBase<\/a> (https:\/\/kb.swarthmore.edu) for important details about installing and using the software tools available to you as a member of the Swarthmore College community.<\/p>\n","protected":false},"excerpt":{"rendered":" IBM SPSS Statistics 29, which is now available for the Swarthmore College community, includes new customer-requested linear ordinary least squares (OLS) regression and parametric accelerated failure time (AFT) model statistical procedures, improved open-source extension integration, UI enhancements, new data visualization … Continue reading New SPSS version adds features, MacOS Sonoma compatibility<\/span><\/a><\/p>\n","protected":false},"author":55,"featured_media":4979,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[2,450,97],"tags":[90],"class_list":{"0":"post-8839","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-academic-technology","8":"category-data-science","9":"category-software","10":"tag-featured","12":"fallback-thumbnail"},"jetpack_featured_media_url":"https:\/\/blogs.swarthmore.edu\/its\/wp-content\/uploads\/2018\/04\/2000px-SPSS_logo.svg_.png","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/ph2nPL-2iz","_links":{"self":[{"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/posts\/8839","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/users\/55"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/comments?post=8839"}],"version-history":[{"count":1,"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/posts\/8839\/revisions"}],"predecessor-version":[{"id":8840,"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/posts\/8839\/revisions\/8840"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/media\/4979"}],"wp:attachment":[{"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/media?parent=8839"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/categories?post=8839"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.swarthmore.edu\/its\/wp-json\/wp\/v2\/tags?post=8839"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Several Linear OLS alternatives added<\/h3>\n\n\n\n
Parametric AFT models <\/h4>\n\n\n\n
Pseudo-R-squared measures <\/h3>\n\n\n\n
Plotting Enhancements<\/h3>\n\n\n\n
Usability improvements<\/h2>\n\n\n\n
Workbook mode enhancements<\/h3>\n\n\n\n
Search enhancements <\/h3>\n\n\n\n
Removal of ability to hide unselected cases<\/h3>\n\n\n\n
Python and R upgrades <\/h3>\n\n\n\n