{"id":812,"date":"2024-04-10T17:20:16","date_gmt":"2024-04-10T10:20:16","guid":{"rendered":"https:\/\/cattleyapublicationservices.com\/?p=812"},"modified":"2024-04-10T21:30:46","modified_gmt":"2024-04-10T14:30:46","slug":"panduan-lengkap-dan-tutorial-uji-goodness-of-fit-dalam-sem-pls","status":"publish","type":"post","link":"https:\/\/cattleyapublicationservices.com\/?p=812","title":{"rendered":"Panduan Lengkap dan Tutorial Uji Goodness of Fit dalam SEM PLS"},"content":{"rendered":"\n<p>Uji goodness-of-fit merupakan langkah penting dalam analisis SEM PLS untuk memastikan bahwa model yang dibangun fit dengan data. Uji ini membantu peneliti untuk menentukan apakah model tersebut mampu menjelaskan hubungan antar variabel dengan baik.<\/p>\n\n\n\n<p><strong><a href=\"https:\/\/cattleyapublicationservices.com\/?p=800\" target=\"_blank\" rel=\"noreferrer noopener\">Cara Mudah Pahami Structural Equation Modeling (SEM)<\/a><\/strong><\/p>\n\n\n\n<p><strong><a href=\"https:\/\/cattleyapublicationservices.com\/?p=804\" target=\"_blank\" rel=\"noreferrer noopener\">Perbedaan SEM PLS dengan SEM Konvensional<\/a><\/strong><\/p>\n\n\n\n<p><strong><a href=\"https:\/\/cattleyapublicationservices.com\/?p=806\" target=\"_blank\" rel=\"noreferrer noopener\">Cara Menentukan Variabel dan Indikator dalam SEM PLS<\/a><\/strong><\/p>\n\n\n\n<p><strong><a href=\"https:\/\/cattleyapublicationservices.com\/?p=808\" target=\"_blank\" rel=\"noreferrer noopener\">Cara Menentukan Model Pengukuran dan Model Struktural dalam SEM PLS<\/a><\/strong><\/p>\n\n\n\n<p><strong>Kriteria Uji Goodness-of-Fit<\/strong><\/p>\n\n\n\n<p>Terdapat beberapa kriteria yang digunakan untuk menguji goodness-of-fit dalam SEM PLS:<\/p>\n\n\n\n<p><strong>1. R-squared (R\u00b2)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Menunjukkan proporsi varians variabel dependen yang dijelaskan oleh variabel independen.<\/li>\n\n\n\n<li>Nilai R\u00b2 yang tinggi (umumnya di atas 0.67) menunjukkan bahwa model fit dengan data.<\/li>\n\n\n\n<li>Nilai R\u00b2 yang rendah menunjukkan bahwa model perlu diperbaiki.<\/li>\n<\/ul>\n\n\n\n<p><strong>2. Q-squared (Q\u00b2)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Menunjukkan goodness-of-fit model dibandingkan dengan model baseline.<\/li>\n\n\n\n<li>Nilai Q\u00b2 yang positif menunjukkan bahwa model fit dengan data.<\/li>\n\n\n\n<li>Nilai Q\u00b2 yang negatif menunjukkan bahwa model tidak fit dengan data.<\/li>\n<\/ul>\n\n\n\n<p><strong>3. Standard Root Mean Square Residual (SRMR)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Menunjukkan standar deviasi root mean square residual.<\/li>\n\n\n\n<li>Nilai SRMR yang kecil (umumnya di bawah 0.08) menunjukkan bahwa model fit dengan data.<\/li>\n\n\n\n<li>Nilai SRMR yang tinggi menunjukkan bahwa model perlu diperbaiki.<\/li>\n<\/ul>\n\n\n\n<p><strong>4. Geweke&#8217;s Epsilon<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Digunakan untuk menguji konvergensi parameter model.<\/li>\n\n\n\n<li>Nilai Geweke&#8217;s Epsilon yang kecil menunjukkan bahwa parameter model telah konvergen.<\/li>\n<\/ul>\n\n\n\n<p><strong>5. T-value dan P-value<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Digunakan untuk menguji signifikansi parameter model.<\/li>\n\n\n\n<li>Nilai t-value yang tinggi dan nilai p-value yang kecil menunjukkan bahwa parameter model signifikan.<\/li>\n<\/ul>\n\n\n\n<p><strong>Tutorial Uji Goodness-of-Fit dengan SmartPLS<\/strong><\/p>\n\n\n\n<p>Berikut adalah tutorial uji goodness-of-fit dalam SEM PLS menggunakan software SmartPLS:<\/p>\n\n\n\n<p><strong>1. Buka file project SmartPLS yang berisi model SEM PLS yang ingin diuji.<\/strong><\/p>\n\n\n\n<p><strong>2. Klik pada tab &#8220;Output&#8221;.<\/strong><\/p>\n\n\n\n<p><strong>3. Pada bagian &#8220;Goodness-of-Fit&#8221;, perhatikan nilai R\u00b2, Q\u00b2, dan SRMR.<\/strong><\/p>\n\n\n\n<p><strong>4. Pada bagian &#8220;Parameter Estimates&#8221;, perhatikan nilai t-value dan p-value untuk setiap parameter model.<\/strong><\/p>\n\n\n\n<p><strong>5. Pada bagian &#8220;Convergence&#8221;, perhatikan nilai Geweke&#8217;s Epsilon.<\/strong><\/p>\n\n\n\n<p><strong>Interpretasi Hasil<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Jika nilai R\u00b2, Q\u00b2, dan SRMR menunjukkan bahwa model fit dengan data, dan nilai t-value dan p-value menunjukkan bahwa parameter model signifikan, maka model tersebut dapat diterima.<\/li>\n\n\n\n<li>Jika nilai R\u00b2, Q\u00b2, dan SRMR menunjukkan bahwa model tidak fit dengan data, dan nilai t-value dan p-value menunjukkan bahwa parameter model tidak signifikan, maka model tersebut perlu diperbaiki.<\/li>\n<\/ul>\n\n\n\n<p><strong>Referensi:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Hair,&nbsp;J. F., Hult, G. T. M., Ringle, C. M., &amp; Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.<\/li>\n\n\n\n<li>Chin, W. W. (1998). The partial least squares approach to&nbsp;structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research&nbsp;(pp. 295-336). Lawrence Erlbaum Associates.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Uji goodness-of-fit merupakan langkah penting dalam analisis SEM PLS untuk memastikan bahwa model yang dibangun fit dengan data.<\/p>\n","protected":false},"author":1,"featured_media":827,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[25],"tags":[],"class_list":["post-812","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analisis-data-sem-pls"],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/cattleyapublicationservices.com\/wp-content\/uploads\/2024\/04\/2fee449d-8a24-41f7-9992-aaacef9ad9e4.jpg","jetpack_sharing_enabled":true,"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=\/wp\/v2\/posts\/812","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=812"}],"version-history":[{"count":1,"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=\/wp\/v2\/posts\/812\/revisions"}],"predecessor-version":[{"id":813,"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=\/wp\/v2\/posts\/812\/revisions\/813"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=\/wp\/v2\/media\/827"}],"wp:attachment":[{"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=812"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=812"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cattleyapublicationservices.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=812"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}