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{"Monterey_Park": {"Vroman's Bookstore": "Vroman's Bookstore an independent bookstore. It is the oldest and largest independent bookstore in Southern California. Founded in 1894 by Adam Clark Vroman, the original Vroman\u2019s Book and Photographic Supply store was located at 60 E. Colorado St in Pasadena, California."}}
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{
"@metadata": {
"authors": [
"គីមស៊្រុន",
"វ័ណថារិទ្ធ"
]
},
"contribs-title": "ការផ្ទុកឡើងរបស់ខ្ញុំ",
"contribs-settings-button": "ការកំណត់នានា",
"contribs-upload-button": "ផ្ទុកឡើង",
"contribs-photo-library-button": "រូបថត",
"contribs-state-queued": "$1 ត្រូវបានដាក់ក្នុងជួររង់ចាំ",
"contribs-state-uploading": "$1 កំពុងត្រូវបានផ្ទុកឡើង",
"contribs-state-downloading": "កំពុងយកកូនរូបភាព $1",
"contribs-state-generating-thumb": "កំពុងបង្កើតកូនរូបភាព $1",
"contribs-untitled": "គ្មានចំណងជើង",
"contribs-refresh": "ទាញដើម្បីផ្ទុកជាថ្មី",
"contribs-refreshing": "កំពុងផ្ទុកជាថ្មី...",
"error-upload-failed": "ផ្ទុកឡើងមិនបានសម្រេច!",
"error-upload-cancelled": "បោះបង់ការផ្ទុកឡើង",
"error-dismiss": "លាក់",
"error-nologin-title": "អីយ៉ាស់!",
"error-nologin-text": "អ្នកត្រូវតែកត់ឈ្មោះចូលមុននឹងអ្នកអាចផ្ទុករូបថតឡើងបាន",
"error-bad-password-title": "បញ្ហាក្នុងការកត់ឈ្មោះចូល",
"error-bad-password": "ឈ្មោះអ្នកប្រើប្រាស់ ឬពាក្យសម្ងាត់មិនត្រឹមត្រូវ",
"reltime-minutes": "$1 នាទីមុន",
"reltime-hours": "$1 ម៉ោងមុន",
"reltime-days": "$1 ថ្ងៃមុន",
"settings-title": "ការកំណត់នានា",
"settings-username-placeholder": "ឈ្មោះអ្នកប្រើប្រាស់",
"settings-password-placeholder": "ពាក្យសម្ងាត់",
"settings-debug-label": "ម៉ូដឌីបាក់",
"settings-open-links-label": "បើកតំណភ្ជាប់ជាមួយ៖",
"settings-open-links-only-safari": "គ្មានកម្មវិធីរាវរកអ៊ីនធើណិតអាចប្រើបានផ្សេងទៀតត្រូវបានតម្លើងទេ។",
"settings-debug-detail": "កំពុងផ្ទុកឡើងទៅកាន់ $1",
"settings-usage-reports-label": "របាយការណ៍ស្ដីពីបម្រើបម្រាស់៖",
"settings-usage-reports-description": "ផ្ញើរបាយការណ៍ស្ដីពីបម្រើបម្រាស់ទៅវិគីមេឌាដើម្បីជួយកែលម្អកម្មវិធីយើងខ្ញុំ។",
"settings-links-label": "តំណភ្ជាប់៖",
"details-title": "ព័ត៌មានលម្អិត",
"details-nav-prompt": "សរសេរការពណ៌នារួចផ្ទុកឡើង",
"details-upload-button": "ផ្ទុកឡើង",
"details-title-label": "ចំណងជើង",
"details-title-placeholder": "ចំណងជើងដែលមានលក្ខណៈបរិយាយនិងពិសេស",
"details-description-label": "បរិយាយ",
"details-description-placeholder": "ត្រូវការជាចាំបាច់",
"details-description-loading": "កំពុងផ្ទុក…",
"details-description-none-found": "រកមិនឃើញការបរិយាយពន្យល់",
"details-license-label": "អាជ្ញាបណ្ណ",
"details-hide-keyboard": "លាក់ក្ដារចុច",
"details-category-label": "ចំណាត់ថ្នាក់ក្រុម",
"details-category-select": "ជ្រើសរើស...",
"details-category-loading": "កំពុងផ្ទុក…",
"details-category-none-found": "រកមិនឃើញចំណាត់ថ្នក់ក្រុម",
"web-open-in": "បើកនៅក្នុង $1",
"web-cancel": "បោះបង់",
"login-title": "គណនី",
"login-current-user-button": "បន្តប្រើប្រាស់ក្នុងនាមជា៖ $1",
"login-button": "កត់ឈ្មោះចូល",
"login-recover-password-button": "ស្ដារពាក្យសម្ងាត់",
"logout-button": "កត់ឈ្មោះចេញ",
"about-title": "អំពី",
"about-app-version-label": "កំណែ $1",
"about-commons-button": "វិគីមេឌា Commons",
"about-bugs-button": "កម្មវិធីតាមរកBug",
"about-privacy-button": "គោលការណ៍ភាពឯកជន",
"about-source-button": "ប្រភព",
"about-source-this-app-title": "កម្មវីធីនេះ (iOS)៖",
"about-source-this-app-contributors": "អ្នករួមចំណែក",
"about-license-button": "អាជ្ញាបណ្ណ",
"about-source-gradient-title": "Jeff LaMarche's Gradient UIButton:",
"welcome-no-images-message": "រកមិនឃើញអ្វីត្រូវផ្ទុកឡើង\nប្រើប៊ូតុងខាងក្រោមដើម្បី\nថតឬជ្រើសរើសរូបថត",
"welcome-take-or-choose-message": "ថតរូប ឬជ្រើសរើសពីអាល់ប៊ុម",
"welcome-checking-message": "ពិនិត្យមើលការផ្ទុកឡើងមុនៗ",
"license-title": "អាជ្ញាប័ណ្ណ",
"license-intro": "អ្នកយល់ព្រមដាក់អោយរបស់ដែលអ្នកផ្ទុកឡើងក្រោមអាជ្ញាប័ណ្ឌជ្រើសរើសខាងក្រោម៖",
"license-details-intro": "ព័ត៌មានបន្ថែម",
"license-details-label": "អានអំពីការដាក់អាជ្ញាបណ្ណ",
"license-details-url": "https://commons.m.wikimedia.org/wiki/Commons:Licensing",
"catdetail-title": "ចំណាត់ថ្នាក់ក្រុម",
"catdetail-intro": "ចំណាត់ថ្នាក់ក្រុមជួយអោយគេងាយស្រួលក្នុងការស្វែងរករូបភាពរបស់អ្នកនៅក្នុងWikimedia Commons។",
"catdetail-remove-label": "ដកចេញ \"$1\"",
"catdetail-info-intro": "ព័ត៌មានបន្ថែម",
"catdetail-info-label": "អានអំពីចំណាត់ថ្នាក់ក្រុម",
"catdetail-info-url": "https://commons.m.wikimedia.org/wiki/Commons:Categories",
"catadd-title": "បន្ថែមចំណាត់ថ្នាក់ក្រុម",
"catadd-search-placeholder": "ស្វែងរកចំណាត់ថ្នាក់ក្រុម",
"getting-started-what-is-commons-contribute-label": "បរិច្ចាគរូបភាពរបស់អ្នក។\nសូមជួយផ្ដល់ភាពរស់រវើកដល់អត្ថបទវិគីភីឌា!",
"getting-started-what-is-commons-images-label": "រូបភាពនៅលើវិគីភីឌាបានមកពីWikimedia Commons។",
"getting-started-what-photos-educate-label": "រូបភាពរបស់អ្នកជួយផ្ដល់ចំណេះដឹងដល់ប្រជាជនទូទាំងពិភពលោក។",
"getting-started-what-photos-avoid-label": "ជៀសវៀងផ្ទុកឡើងរបស់ដែលជាប់កម្មសិទ្ធិបញ្ញាដែលអ្នករកបានពីអ៊ីនធើណិត ក៏ដូចជារូបភាពខិតបណ្ឌ ក្របសៀវភៅជាដើម...",
"getting-started-got-it-label": "តើលោកអ្នកយល់ហើយ?",
"getting-started-yes-button": "បាទ/ចាស៎!",
"picture-of-day-label": "រូបភាពប្រចាំថ្ងៃរបស់ Wikimedia Commons",
"picture-of-day-author": "អ្នកនិពន្ធ៖",
"picture-of-day-tap-for-license": "ចុចមើលអាជ្ញាបណ្ឌ",
"picture-of-day-menu-title": "រូបភាពប្រចាំថ្ងៃ",
"picture-of-day-commons-button": "ទំព័រ Commons",
"picture-of-day-license-button": "អាជ្ញាបណ្ណ",
"picture-of-day-cancel-button": "បោះបង់",
"refresh-failed-alert": "ផ្ទុកឡើងវិញមិនបានសម្រេច!",
"gallery-cancel-button": "បោះបង់",
"gallery-album-title": "អាល់ប៊ុម"
}
|
{
"name": "datocms-plugin-tinymce-rich-text",
"homepage": "https://github.com/datocms/plugins/tree/master/rich-text-tinymce#readme",
"description": "Custom TinyMCE HTML editor",
"version": "0.1.9",
"author": "DatoCMS <support@datocms.com>",
"license": "ISC",
"keywords": [
"datocms",
"datocms-plugin",
"field-editor",
"text-field",
"wysiwyg",
"rich-text"
],
"files": [
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"build"
],
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"react-dom": "^17.0.2",
"react-scripts": "5.0.0",
"tinymce": "^5.10.2",
"typescript": "^4.5.4"
},
"scripts": {
"start": "cross-env BROWSER='none' PUBLIC_URL='/' react-scripts start",
"build": "cross-env PUBLIC_URL='.' react-scripts build",
"test": "react-scripts test",
"eject": "react-scripts eject",
"prepublishOnly": "npm run build"
},
"eslintConfig": {
"extends": [
"react-app"
]
},
"browserslist": {
"production": [
">0.2%",
"not dead",
"not op_mini all"
],
"development": [
"last 1 chrome version",
"last 1 firefox version",
"last 1 safari version"
]
},
"devDependencies": {
"@types/lodash": "^4.14.178",
"cross-env": "^7.0.3"
},
"datoCmsPlugin": {
"title": "TinyMCE Editor",
"previewImage": "docs/rich-editor-plugin-preview.png",
"coverImage": "docs/cover.png",
"entryPoint": "build/index.html"
}
}
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{"body": "Explore This Park Get Involved Planning Winter Use in Yellowstone National Park Winter Use Adaptive Management Program WUAMP: Wildlife Working Group Winter Use Adaptive Management Program: Wildlife Working Group Background and Area of Focus The Wildlife Working Group will make recommendations to the NPS on the design of an updated monitoring strategy that measures and evaluates the impacts of winter use on the park's wildlife species, including bison, trumpeter swans, elk, lynx, wolverines, gray wolves, and bald eagles to ensure winter wildlife ecology is not disrupted going forward. This group will also provide feedback to the NPS on current and future results of wildlife monitoring and research, and on adaptive management actions that may be implemented in the future as part of the new Adaptive Management Plan. Any and all participants can join this working group at any time by contacting the NPS lead for the Wildlife Working Group: John Treanor at e-mail us or 307.344.2505. Meeting Agendas Meeting #1: February 14, 2014 Meeting Agenda Meeting Notes Meeting #1: February 14, 2014 Meeting Notes Monitoring Strategy Wildlife Responses to Motorized Winter Use in Yellowstone National Park- 2015 Monitoring Plan (298 KB pdf) Other Supporting Materials", "url": "https://www.nps.gov/yell/learn/management/wild.htm", "title": "Yellowstone National Park: WUAMP: Wildlife Working Group"}
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"first_traded_price": 5054.0,
"highest_price": 5330.0,
"isin": "IRO3KZGZ0001",
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{
"id": 1,
"title": "É montagem vídeo que mostra furacão Dorian sobre as Bahamas",
"publishedAt": "09/04/2019 14:21:00",
"likes": 20000,
"image": "https://piaui.folha.uol.com.br/lupa/wp-content/uploads/2019/09/furacao-dorian-bahamas-destaque.jpg",
"link": "https://piaui.folha.uol.com.br/lupa/2019/09/04/verificamos-video-furacao-dorian-bahamas/",
"content": "A informação, analisada pela Lupa, é falsa. O vídeo é na verdade uma animação por computador, criada pelo artista digital norte-americano Brent Shavnore. Ele compartilhou a obra em sua conta no Instagram no dia 19 de maio deste ano. Mais de 164 mil pessoas visualizaram o vídeo. Shavnore criou o “furacão” sobre uma foto da cidade de Miami, nos Estados Unidos. Em sua página pessoal, imagens que mostram tempestades e raios sobre cidades ou paisagens são comuns."
},
{
"id": 2,
"title": "Polícia Federal não realizou operação contra a Rede Globo em 30 de agosto",
"publishedAt": "09/04/2019 19:02:00",
"likes": 5000000,
"image": "https://piaui.folha.uol.com.br/lupa/wp-content/uploads/2019/09/printglobopftopo.png",
"link": "https://piaui.folha.uol.com.br/lupa/2019/09/03/verificamos-globo-pf/",
"content": "A informação analisada pela Lupa é falsa. A Lupa contatou a Divisão de Comunicação Social da Polícia Federal (PF), que disse não ter conhecimento de qualquer operação envolvendo a emissora. “Não chegou ao conhecimento desta DCS qualquer ocorrência nos moldes da descrita na consulta”, diz e-mail enviado pela PF."
},
{
"id": 3,
"title": "É falso que Drauzio Varella recomendou suplemento contra dores articulares",
"publishedAt": "09/02/2019 18:23:00",
"likes": 10,
"image": "https://piaui.folha.uol.com.br/lupa/wp-content/uploads/2019/09/drauzioreproducao.png",
"link": "https://piaui.folha.uol.com.br/lupa/2019/09/02/verificamos-drauzio-dores-articulares/",
"content": "O conteúdo analisado pela Lupa é falso. O doutor Drauzio Varella nunca deu entrevista ao programa Fantástico recomendando um suplemento alimentar para dores nas articulações. “Nunca fiz nem nunca farei propaganda de remédios, muito menos desses que prometem curas sem comprovação científica. Sempre que você vir meu nome associado a algum medicamento tenha certeza: é mentira, propaganda enganosa”, disse Varella, em nota enviada à Lupa."
}
]
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{"746070":{"success":true,"data":{"type":"game","name":"The Survivors","steam_appid":746070,"required_age":"18","is_free":true,"controller_support":"full","detailed_description":"Welcome to the 19th century. The Survivors is an Arctic Western Battle Royale where you and 99 other gold hunters fight for a claim in the gold rush. Scavenge for equipment in the rough, unforgiving Silverspring County. The environment sets the pace; only storm eyes are safe from the raging storm, and bandits are out for your gold. Do you have what it takes, or will you be blinded by your greed?<h2 class=\"bb_tag\">How do I sign up for the Closed Alpha?<\/h2><a href=\"https:\/\/steamcommunity.com\/linkfilter\/?url=https:\/\/docs.google.com\/forms\/d\/e\/1FAIpQLSe3iLKQLqBuso91EvUXrrWpYhq3Q8jWBKN0A98zas4xRocklw\/viewform\" target=\"_blank\" rel=\"noopener\" >Fill in this form!<\/a> You will be added to a list, from which we select testers in batches.<h2 class=\"bb_tag\">Features<\/h2><img src=\"https:\/\/%CDN_HOST_MEDIA_SSL%\/steam\/apps\/746070\/extras\/Title_OneHundredPlayers.png?t=1528469825\" crossorigin=\"anonymous\"><br>Play on a huge map with up to 99 other players to stake your claim for the gold hidden in Silverspring County. Using four different weapons you'll be ready for any situation. With the scaling game systems, you can enjoy the match whether you're playing with 10 or 99 other players!<br><br><br><img src=\"https:\/\/%CDN_HOST_MEDIA_SSL%\/steam\/apps\/746070\/extras\/Title_TeamPlay.png?t=1528469825\" crossorigin=\"anonymous\"><br>Silverspring County contains a wide variety of environments. The boreal forest in the southwest provides cover and hides piles of loot, whereas in the northwest the bandits rule the land, and the remnants of frontier towns dot the landscape in the east.<br><br><br><img src=\"https:\/\/%CDN_HOST_MEDIA_SSL%\/steam\/apps\/746070\/extras\/Title_StormEyes.png?t=1528469825\" crossorigin=\"anonymous\"><br>Over the course of the match, a snowstorm will develop with several storm eyes, which are your only havens from the impending rage of the storm. Beware, as you will not be the only one seeking safety. After the storm settles down, run freely across the map as you seek shelter within the next storm eye. Eventually, only one shrinking storm eye will remain as the storm engulfs all of Silverspring County.<br><br><br><img src=\"https:\/\/%CDN_HOST_MEDIA_SSL%\/steam\/apps\/746070\/extras\/Title_BanditCamp.png?t=1528469825\" crossorigin=\"anonymous\"><br>Bandits have claimed small settlements across the map. It is up to you to decide if it is more worthwhile to fight for the loot or to take a detour around the ruthless raiders.<br><br><br><img src=\"https:\/\/%CDN_HOST_MEDIA_SSL%\/steam\/apps\/746070\/extras\/Title_GunUpgradeSystem.png?t=1528469825\" crossorigin=\"anonymous\"><br>As you loot, you may be fortunate enough to stumble upon boxes of scrap. You can use these to easily craft weapon attachments, giving you an edge on the battlefield.<h2 class=\"bb_tag\">What is Closed Alpha?<\/h2>Closed Alpha is a state very early in game development. Expect art and gameplay to be in an unfinished state and subject to change. Your feedback will shape the direction of this game in the times to come.<h2 class=\"bb_tag\">How does Closed Alpha work?<\/h2>From the release date, June 6th 2018, the servers will open during certain time slots during which you can play the game. You can then keep us informed with problems you find or suggestions you may on the forums here or on our Discord.<br><br><br><strong>We are a team of dedicated students at NHTV Breda University in the Netherlands and are working on this Battle Royale title as a proof of concept. The Closed Alpha will help us take the game to the next level, giving us the opportunity to continue development in the future.<\/strong>","about_the_game":"Welcome to the 19th century. The Survivors is an Arctic Western Battle Royale where you and 99 other gold hunters fight for a claim in the gold rush. Scavenge for equipment in the rough, unforgiving Silverspring County. The environment sets the pace; only storm eyes are safe from the raging storm, and bandits are out for your gold. Do you have what it takes, or will you be blinded by your greed?<h2 class=\"bb_tag\">How do I sign up for the Closed Alpha?<\/h2><a href=\"https:\/\/steamcommunity.com\/linkfilter\/?url=https:\/\/docs.google.com\/forms\/d\/e\/1FAIpQLSe3iLKQLqBuso91EvUXrrWpYhq3Q8jWBKN0A98zas4xRocklw\/viewform\" target=\"_blank\" rel=\"noopener\" >Fill in this form!<\/a> You will be added to a list, from which we select testers in batches.<h2 class=\"bb_tag\">Features<\/h2><img src=\"https:\/\/%CDN_HOST_MEDIA_SSL%\/steam\/apps\/746070\/extras\/Title_OneHundredPlayers.png?t=1528469825\" crossorigin=\"anonymous\"><br>Play on a huge map with up to 99 other players to stake your claim for the gold hidden in Silverspring County. Using four different weapons you'll be ready for any situation. With the scaling game systems, you can enjoy the match whether you're playing with 10 or 99 other players!<br><br><br><img src=\"https:\/\/%CDN_HOST_MEDIA_SSL%\/steam\/apps\/746070\/extras\/Title_TeamPlay.png?t=1528469825\" crossorigin=\"anonymous\"><br>Silverspring County contains a wide variety of environments. The boreal forest in the southwest provides cover and hides piles of loot, whereas in the northwest the bandits rule the land, and the remnants of frontier towns dot the landscape in the east.<br><br><br><img src=\"https:\/\/%CDN_HOST_MEDIA_SSL%\/steam\/apps\/746070\/extras\/Title_StormEyes.png?t=1528469825\" crossorigin=\"anonymous\"><br>Over the course of the match, a snowstorm will develop with several storm eyes, which are your only havens from the impending rage of the storm. Beware, as you will not be the only one seeking safety. After the storm settles down, run freely across the map as you seek shelter within the next storm eye. Eventually, only one shrinking storm eye will remain as the storm engulfs all of Silverspring County.<br><br><br><img src=\"https:\/\/%CDN_HOST_MEDIA_SSL%\/steam\/apps\/746070\/extras\/Title_BanditCamp.png?t=1528469825\" crossorigin=\"anonymous\"><br>Bandits have claimed small settlements across the map. It is up to you to decide if it is more worthwhile to fight for the loot or to take a detour around the ruthless raiders.<br><br><br><img src=\"https:\/\/%CDN_HOST_MEDIA_SSL%\/steam\/apps\/746070\/extras\/Title_GunUpgradeSystem.png?t=1528469825\" crossorigin=\"anonymous\"><br>As you loot, you may be fortunate enough to stumble upon boxes of scrap. You can use these to easily craft weapon attachments, giving you an edge on the battlefield.<h2 class=\"bb_tag\">What is Closed Alpha?<\/h2>Closed Alpha is a state very early in game development. Expect art and gameplay to be in an unfinished state and subject to change. Your feedback will shape the direction of this game in the times to come.<h2 class=\"bb_tag\">How does Closed Alpha work?<\/h2>From the release date, June 6th 2018, the servers will open during certain time slots during which you can play the game. You can then keep us informed with problems you find or suggestions you may on the forums here or on our Discord.<br><br><br><strong>We are a team of dedicated students at NHTV Breda University in the Netherlands and are working on this Battle Royale title as a proof of concept. The Closed Alpha will help us take the game to the next level, giving us the opportunity to continue development in the future.<\/strong>","short_description":"The Survivors is the Arctic-Western Battle Royale where 100 gold hunters fight for their claim in the Yukon Gold Rush. Storm eyes set the pace for the players, and bandits protect valuable loot. Players can use scrap to upgrade the 19th century weaponry. 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{
"name": "MATCH",
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{
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{"name":"广东省","city":[{"name":"广州市","area":[]},{"name":"韶关市","area":[]},{"name":"深圳市","area":[]},{"name":"珠海市","area":[]},{"name":"汕头市","area":[]},{"name":"佛山市","area":[]},{"name":"江门市","area":[]},{"name":"湛江市","area":[]},{"name":"茂名市","area":[]},{"name":"肇庆市","area":[]},{"name":"惠州市","area":[]},{"name":"梅州市","area":[]},{"name":"汕尾市","area":[]},{"name":"河源市","area":[]},{"name":"阳江市","area":[]},{"name":"清远市","area":[]},{"name":"东莞市","area":[]},{"name":"中山市","area":[]},{"name":"潮州市","area":[]},{"name":"揭阳市","area":[]},{"name":"云浮市","area":[]}]}
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{
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{
"id": "59426",
"title": "Ch.011 : The Hyakki Empire Conspiracy",
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"number": "011"
},
{
"id": "59420",
"title": "Ch.010 : Twister Of Terror",
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"number": "010"
},
{
"id": "59413",
"title": "Ch.009 : Operation - Infiltrate the Dinosaur Empire",
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},
{
"id": "59408",
"title": "Ch.008 : Chiryu Clan",
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},
{
"id": "59396",
"title": "Ch.007 : The Battle to Destroy ",
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{
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{
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{
"id": "59361",
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{
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{
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{
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"Few women competed in sports until the late nineteenth and early twentieth centuries, as social changes in Europe and North America favored increased female participation in society as equals with men. Although women were permitted to participate in many sports, relatively few showed interest, and there was often disapproval of those who did. The modern Olympics had female competitors from 1900 onward, though women at first participated in considerably fewer events than men. Concern over the physical strength and stamina of women led to the discouragement of female participation in more physically intensive sports, and in some cases led to less physically demanding female versions of male sports. Thus netball was developed out of basketball and softball out of baseball."
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[
"この機械は、数種類の金属素材を小さな部品へ分割していくよう設計されています。その動作は素早く、あらゆるタイプの可燃性の燃料を受け入れます。",
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}
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{
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{
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},
{
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},
{
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{"name":"RAMP Holdings","permalink":"everyzing","crunchbase_url":"http://www.crunchbase.com/company/everyzing","homepage_url":"http://www.ramp.com/","blog_url":"","blog_feed_url":"","twitter_username":"","category_code":"search","number_of_employees":30,"founded_year":null,"founded_month":null,"founded_day":null,"deadpooled_year":null,"deadpooled_month":null,"deadpooled_day":null,"deadpooled_url":null,"tag_list":"video-seo, seo, search, universal-search, speech-to-text, video","alias_list":"","email_address":"","phone_number":"857-202-3500","description":"Search & SEO platform","created_at":"Mon Sep 03 14:06:16 UTC 2007","updated_at":"Fri Nov 16 13:20:36 UTC 2012","overview":"<p>RAMP (formerly EveryZing) is a universal search and SEO provider to major media companies including FOX, NBCU and CBS. 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{"hiddenService":"cryptsen7fo43rr6.onion","webDetected":true,"sshDetected":false,"ricochetDetected":false,"ircDetected":false,"ftpDetected":false,"smtpDetected":false,"bitcoinDetected":false,"mongodbDetected":false,"vncDetected":false,"xmppDetected":false,"serverPoweredBy":"","serverVersion":"Apache/2.2.22 (@RELEASE@)","foundApacheModStatus":false,"relatedOnionServices":null,"relatedOnionDomains":null,"linkedSites":["ru.wikipedia.org","en.wikipedia.org"],"internalPages":null,"ipAddresses":null,"openDirectories":null,"exifImages":null,"interestingFiles":null,"pageReferencedDirectories":null,"pgpKeys":null,"hashes":["58f09762228be2022cd728cadc417ed951efdc20"],"snapshot":"\u003chtml\u003e\n\u003chead\u003e\n\t\u003cmeta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\"\u003e\n\t\u003ctitle\u003eОбратная связь | Feedback\u003c/title\u003e\n\u003c/head\u003e\n\u003cbody bgcolor=\"#eadfef\"\u003e\n\n\u003cstyle type=\"text/css\"\u003e\na.submit {\n color: #fff;\n text-decoration: none;\n user-select: none;\n background: #5f0d89;\n padding: .3em 1em;\n margin-bottom:10px;\n margin-top: 10px;\n outline: none;\n border-radius: 5px 5px 5px 5px;\n display:inline-block;\n font-size: larger;\n} \na.submit:hover { background: #924ab7; }\na.submit:active { background: #5b2676; }\n\u003c/style\u003e\n\n\u003cscript type=\"text/javascript\"\u003e\n\nfunction getXmlHttp(){\n var xmlhttp;\n try {\n xmlhttp = new ActiveXObject(\"Msxml2.XMLHTTP\");\n } catch (e) {\n try {\n xmlhttp = new ActiveXObject(\"Microsoft.XMLHTTP\");\n } catch (E) {\n xmlhttp = false;\n }\n }\n if (!xmlhttp \u0026\u0026 typeof XMLHttpRequest!='undefined') {\n xmlhttp = new XMLHttpRequest();\n }\n return xmlhttp;\n}\n\nfunction getCookie(name) {\n var cookie = \" \" + document.cookie;\n var search = \" \" + name + \"=\";\n var setStr = null;\n var offset = 0;\n var end = 0;\n if (cookie.length \u003e 0) {\n offset = cookie.indexOf(search);\n if (offset != -1) {\n offset += search.length;\n end = cookie.indexOf(\";\", offset);\n if (end == -1) {\n end = cookie.length;\n }\n setStr = unescape(cookie.substring(offset, end));\n }\n }\n return(setStr);\n}\n\nfunction onSpanSelect(bIsCode)\n{\n\tif (document.getElementById('locked').value == '1')\n\t\treturn;\n\n\tif (bIsCode)\n\t{\n\t\tdocument.getElementById('have_code_yes').checked=true;\n\t}\n\telse\n\t{\n\t\tdocument.getElementById('have_code_no').checked=true;\n\t}\n\tdocument.getElementById('code').disabled = !(document.getElementById('have_code_yes').checked);\n}\n\nfunction disableUi(bDisable)\n{\n\tdocument.getElementById('email').disabled = bDisable;\n\tdocument.getElementById('have_code_yes').disabled = bDisable;\n\tdocument.getElementById('have_code_no').disabled = bDisable;\n\tdocument.getElementById('code').disabled = bDisable || !(document.getElementById('have_code_yes').checked);\n\tdocument.getElementById('text').disabled = bDisable;\n\t\n\tif (bDisable)\n\t\tdocument.getElementById('a_submit').innerHTML = 'Пожалуйста, подождите / Please wait';\n\telse\n\t\tdocument.getElementById('a_submit').innerHTML = 'Отправить / Send';\n}\n\nfunction onSubmit()\n{\n\tif (document.getElementById('locked').value == '1')\n\t\treturn;\n\n\tif (getCookie('sent') != null || document.getElementById('was_sent').value == '1')\n\t{\n\t\talert('Вы уже отправляли сообщение. 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{"id": "35884331", "header": "ĐBQH NGUYỄN THANH XUÂN CHẤT VẤN BỘ GIAO THÔNG VẬN TẢI VỀ VIỆC XEM XÉT TUYẾN CẦN THƠ – CÔN ĐẢO VÀO DANH MỤC TUYẾN VẬN TẢI TÀU THỦY", "datetime": "2020-08-01T09:01:33.000+07:00", "summary": "Trước nhu cầu phát triển du lịch và kết nối từ đất liền ra đảo với hành trình du lịch lịch sử văn hóa Cần Thơ – Côn Đảo ngày càng lớn. Tại phiên chất vấn của kỳ họp thứ 8, Quốc hội khóa XIV, Đại biểu Nguyễn Thanh Xuân, Đoàn đại biểu Quốc hội thành phố Cần Thơ, đã chất vấn Bộ trưởng Bộ Giao thông vận tải Nguyễn Văn Thể về việc bổ sung tuyến giao thông Cần Thơ – Côn Đảo vào danh mục tuyến vận tải thủy.", "content": "Đây là chuyến tàu tàu Superdong Sóc Trăng - Côn Đảo với hơn 600 khách/ngày được đưa vào hoạt động vào năm 2017. Ở những chuyến tàu này, du khách chỉ mất 2,5 giờ để ra Côn Đảo với mức giá chỉ 300.000 đồng; trong khi đi máy bay phải mất khoảng 2 triệu đồng mà thời gian tiêu tốn vẫn tương tự.Theo thống kê, chỉ trong nửa đầu năm 2018, Côn Đảo đã đón hơn 153.000 lượt khách, với tổng doanh thu đạt 730 tỉ đồng. Trong đó, chỉ riêng tháng 6 đã có đến hơn 41.000 lượt khách với doanh thu 187 tỉ đồng. So với những năm trước, đà tăng trưởng của du lịch đang phát triển mạnh với nguồn thu cao, tăng 41% so với cùng kỳ, đem lại nguồn thu lớn.Trước nhu cầu du lịch từ Cần Thơ tới Côn Đảo, năm 2018, Ủy ban nhân dân Tp.Cần Thơ đề nghị Ủy ban nhân dân tỉnh Bà Rịa - Vũng Tàu (BR-VT), Bộ Giao thông Vận tải (GTVT) xem xét, thống nhất chủ trương cho mở tuyến tàu cao tốc từ Tp.Cần Thơ đi Côn Đảo. Theo đề xuất của Tp.Cần Thơ, tuyến sẽ đi dọc theo sông Hậu đến cảng Trần Đề khoảng 90 km, sau đó đi từ cảng Trần Đề - Sóc Trăng đến cảng Bến Đầm - Côn Đảo khoảng 105 km. Dự kiến, thời gian hành trình từ Tp.Cần Thơ đi cảng Bến Đầm - Côn Đảo vào khoảng 3 giờ đến 3 giờ 30 phút tùy thời tiết.Căn cứ vào Thông tư 15 về tuyến đường thủy nội địa quốc gia thì tuyến từ Cần Thơ - Côn Đảo không thuộc danh mục cấp phép tuyến từ bờ ra đảo. Thêm vào đó, trước thực trạng lượng khách đến với Côn Đảo tăng cao đột biến, tăng 40% so với cùng kỳ năm trước, Ủy ban nhân dân huyện Côn Đảo, Bà Rịa-Vũng Tàu đã có văn bản hỏa tốc gửi Ủy ban nhân dân tỉnh Bà Rịa-Vũng Tàu, Sở Giao thông vận tải về khả năng tiếp nhận các loại tàu khách tại cảng Bến Đầm.Theo đó, căn cứ vào tình hình hoạt động, cảng Bến Đầm không có khả năng tiếp nhận cho cập cảng thêm số lượng tàu khách đến đón, trả khách vì những khó khăn như cảng đang trong thời gian vừa sửa chữa vừa khai thác ba bến tàu. Các bến này đang tiếp nhận tổng hợp bao gồm tàu hàng, tàu khách, ghe cá…, các loại tàu chuyên dụng khác trong điều kiện vừa thực hiện chức năng cảng biển vừa là bến thủy nội địa. Theo huyện Côn Đảo, hai tàu của Superdong và một tàu Côn Đảo Express 36 cập bến mỗi ngày với khoảng 900 khách cùng với các tàu dầu, tàu hàng, tàu chuyên dụng… khiến cảng Bến Đầm đang quá tải.Mặt khác, theo nhu cầu, dự báo các dịch vụ điện, nước tại cảng Bến Đầm không đủ đáp ứng. Điều kiện về an toàn cảng tàu khách chưa đảm bảo cũng như phát sinh nước thải, chất thải sinh hoạt tăng đột biến từ các tàu khách qua cảng là nguy cơ đe dọa gây ô nhiễm môi trường cao.Sau khi có ý kiến của Đại biểu Quốc hội tới Bộ Giao thông vận tải, Bộ Giao thông vận tải đã ban hành Thông tư số 36/2019/TT- BGTVT ngày 12/9/2019 về sửa đổi, bổ sung các thông tư quy định về quản lý tuyến vận tải thủy từ bờ ra đảo trong vùng biển Việt Nam, trong đó có bổ sung tuyến Cần Thơ – Côn Đảo vào Danh mục các tuyến vận tải thủy tờ bờ ra đảo trong vùng biển Việt Nam. Tháng 12/2019, Công ty Cổ phần Tàu cao tốc Phú Quốc (Phú Quốc Express) chính thức đưa tàu cao tốc tuyến Cần Thơ - Trần Đề - Côn Đảo vào khai thác. Tàu khởi hành từ bến Ninh Kiều (TP Cần Thơ) lúc 7h sáng hàng ngày, rời bến tại cảng Bến Đầm (Côn Đảo) vào lúc 13h30 hàng ngày. Thời gian cho mỗi lượt khoảng 3 giờ 30’. Vận hành tuyến hải trình Cần Thơ - Trần Đề - Côn Đảo là 1 trong 3 tàu thuộc “series” Côn đảo Express 36, với sức chứa đến 600 khách. Tốc độ tối đa của tàu đạt đến 35 hải lý/h (60km/h), tàu có thể chịu được sóng cấp 8 đảm bảo an toàn và thoải mái cho du khách.Bà Ngũ Thị Diệu Bình, Phó Tổng Giám đốc thương mại Công ty Cổ phần Tàu cao tốc Phú Quốc cho biết: Khi tàu cao tốc Phú Quốc Express tuyến Cần Thơ - Trần Đề - Côn Đảo đi vào khai thác, lượng khách tới với Côn Đảo ngày càng nhiều hơn, thuận lợi hơn. Phương tiện ở Côn Đảo được mở mang và từ cảng Trần Đề ra cũng khá thuận tiện hơn khi xuất phát từ Bà Rịa - Vũng Tàu.Cùng với tàu cao tốc Phú Quốc (Phú Quốc Express), tàu cao tốc Trưng Nhị Express tuyến Cần Thơ - Côn Đảo chính thức hoạt động thêm một lựa chọn nữa cho du khách. Với hành trình Cần Thơ - Côn Đảo có tần suất chạy hàng ngày ít nhất 1 chuyến/ngày ở cả hai bến là Bến tàu Trung tâm TP Cần Thơ (Bến Ninh Kiều) và Bến tàu du lịch Côn Đảo, tập trung chủ yếu là vận chuyển hành khách và hàng hóa. Tuyến đi dọc theo sông Hậu đến cảng Trần Đề khoảng 90 km, sau đó đi từ cảng Trần Đề – Sóc Trăng đến cảng Bến Đầm – Côn Đảo khoảng 105 km với thời gian chạy là 03 giờ 15 phút. Dự kiến trong giai đoạn tiếp theo sẽ tiếp tục đầu tư và đưa vào khai thác các tuyến du lịch đường thủy nội địa tiềm năng khác, kết nối Cần Thơ với các trung tâm du lịch như: Cần Thơ - đảo Phú Quốc; Cần Thơ - Tp. Hồ Chí Minh; Cần Thơ- Châu Đốc; Cần Thơ- đảo Phú Quý (Bình Thuận); Cần Thơ- đảo Lý Sơn (Quảng Ngãi); Cần Thơ- PhnomPenh (Campuchia)...Ông Quách Văn Hòa, Tổng Giám đốc Công ty Cổ phần Du lịch Cần Thơ nhấn mạnh: Lần đầu tiên chuyến tàu kết nối 3 vùng Cần Thơ - Trần Đề - Côn Đảo trên một tuyến hành trình bằng tàu cao tốc hai thân lớn nhất Việt Nam góp phần giúp các tỉnh miền Tây nói riêng, khu vực Đồng bằng sông Cửu Long nói chung xích lại gần hơn với vùng đảo thiêng liêng của Tổ quốc, đồng thời nâng cao tỉ lệ lượt khách du lịch đến Côn Đảo bằng đường thủy ở khu vực này.Được biết, vào những ngày cao điểm, tại cầu cảng số 1, Bến tàu khách Du kịch Cần Thơ lượt khách đạt khoảng 800-900 khách, ngày thường dao động từ 400-500 khách. Thời gian qua, các tuyến du lịch ra Côn Đảo rất thu hút khách, đặc biệt là các tuyến bắt đầu từ các tỉnh Đồng bằng sông Cửu Long. Phương tiện để ra Côn Đảo từ Đồng bằng sông Cửu Long có 2 tuyến là bằng tàu cao tốc từ cảng Trần Đề (Sóc Trăng) ra Côn Đảo; hoặc đi bằng máy bay từ Cần Thơ ra Côn Đảo. Ngoài ra, từ Tp.Hồ Chí Minh cũng có các chuyến bay ra Côn Đảo và từ Vũng Tàu mới có tàu cao tốc đi Côn Đảo.Tuy nhiên, từ Cần Thơ đi Côn Đảo mỗi ngày chỉ có hai chuyến bay bằng loại máy bay nhỏ do Vasco khai thác nên số lượng khách chuyên chở được rất ít. Do đó, vào dịp cuối tuần, hoặc các dịp lễ, tết, hè... luôn xảy ra tình trạng “cháy vé”. Còn nếu đi bằng tàu cao tốc từ Trần Đề ra Côn Đảo, lại gặp một vấn đề khó khăn là khách phải di chuyển bằng ô tô từ các nơi về Trần Đề, là một địa phương có cơ sở hạ tầng, giao thông còn nhiều khó khăn, thiếu trầm trọng khách sạn, nhà nghỉ, dịch vụ du lịch... phục vụ du khách.Thống kê cho thấy năm 2019, Cần Thơ đón khoảng 8 triệu lượt khách, trong đó có 3 triệu lượt khách lưu trú. Lượng khách từ miền Bắc về Cần thơ và từ Cần Thơ đi Côn Đảo rất lớn. Dự kiến trong 2020, cần thơ sẽ đón lượng khách du lịch cao hơn so với năm trước, việc khai thác tàu cao tốc Trưng Trắc với sức chở 600 khách, thời gian từ Cần Thơ đến Côn Đảo là 3,5 giờ ngày càng thêm sự lựa chọn cho du khách.Như vậy, có thể thấy việc đưa tuyến tàu cao tốc từ Cần Thơ - Trần Đề - Côn Đảo vào Danh mục các tuyến vận tải thủy tờ bờ ra đảo trong vùng biển Việt Nam của Bộ Giao thông vận tải trước đề nghị của thành phố và Đoàn Đại biểu Quốc hội thành phố Cần Thơ đã kịp thời và đáp ứng nhu cầu của người dân trong vùng và cả nước, góp phần giảm chi phí cho người dân và du khách, việc lưu thông ra Côn Đảo trở nên thuận lợi rất nhiều.Cần Thơ nói riêng và vùng Đồng bằng sông Cửu Long nói chung vốn nổi tiếng là miền đất hứa du lich phía Nam của đất nước. Với lợi thế sinh vật phong phú, nguồn thủy hải sản, lúa, trái cây dồi dào, đã từ lâu nơi đây trở thành điểm đến không thể thiếu được của du khách trong và ngoài nước khi tới Việt Nam. Đặc biệt, những năm gần đây, nhu cầu du lịch từ Cần Thơ cũng như các tỉnh Đồng Bằng sông Cửu Long ra Côn Đảo rất lớn. Tuy nhiên, việc du lịch ra Côn Đảo bằng đường hàng không, hoặc xuất phát từ Bà Rịa Vũng Tàu bằng tàu thủy như hiện tại làm giảm số lượng du khách từ khu vực này tới với Côn Đảo.Trước nhu cầu phát triển du lịch và kết nối từ đất liền ra đảo ngày càng lớn tại khu vực Đồng bằng sông Cửu Long, Tại kỳ họp thứ 8 Quốc hội khóa XIV, đại biểu Nguyễn Thanh Xuân, Phó Trưởng Đoàn Đại biểu Quốc hội thành phố Cần Thơ, đã đặt câu hỏi với tư lệnh ngành Giao thông vận tải. Nội dung câu hỏi nêu rõ: “Đề nghị xem xét bổ sung tuyến Cần Thơ – Côn Đảo vào danh mục các tuyến vận tải thủy từ bờ ra đảo trong vùng biển Việt Nam tạo điều kiện thúc đẩy phát triển du lịch của thành phố và vùng Đồng bằng sông Cửu Long”.Sau câu hỏi của Đại biểu Nguyễn Thanh Xuân về những giải pháp của Bộ trong việc bổ sung tuyến Cần Thơ – Côn Đảo vào danh mục các tuyến vận tải thủy từ bờ ra đảo trong vùng biển Việt Nam tạo điều kiện thúc đẩy phát triển du lịch trên địa bàn. Thay mặt Chính phủ, Bộ trưởng Bộ Giao thông vận tải Nguyễn Văn Thể khẳng định: Căn cứ vào quy định của pháp luật, thực tế hoạt động vận tải, Bộ Giao thông vận tải đã quyết định bổ sung tuyến Cần Thơ – Côn Đảo vào Danh mục các tuyến vận tải thủy tờ bờ ra đảo trong vùng biển Việt Nam tại Thông tư số 36/2019/TT- BGTVT ngày 12/9/2019 về sửa đổi, bổ sung các thông tư quy định về quản lý tuyến vận tải thủy từ bờ ra đảo trong vùng biển Việt Nam.Qua phần trả lời của Bộ trưởng Bộ Giao thông vận tải có thể thấy Chính phủ, Quốc hội và ngành giao thông đã có những nỗ lực để thực hiện sớm bổ sung tuyến giao thông thủy Cần Thơ – Côn Đảo đi vào khai thác đáp ứng nhu cầu kết nối từ bờ ra đảo của người dân đồng bằng. Chính phủ, Bộ đã nhìn nhận được thực trạng và có nội dung trả lời cụ thể, kịp thời tới Đại biểu trong thời gian sớm nhất tại Thông tư 36 của Bộ Giao thông vận tải.Vậy giải pháp cụ thể và sự vào cuộc của Bộ Giao thông vận tải, Chính phủ, Quốc hội sẽ được thực hiện như thế nào? Đại biểu kỳ vọng gì vào những giải pháp thực tế của ngành Giao thông vận tải đưa ra trong thời gian tới. Phóng viên Cổng Thông tin điện tử Quốc hội đã có cuộc trao đổi với Đại biểu Nguyễn Thanh Xuân, Phó Trưởng Đoàn đại biểu Quốc hội thành phố Cần Thơ về vấn đề này.: Hiện nay chỉ có một tuyến duy nhất đó là hàng không Việt Nam bay từ sân bay Cần Thơ ra Côn Đảo một ngày chỉ có một chuyến. Đi loại máy bay ATR72 khoảng 70 chỗ ngồi. Như vậy thì ngoài hàng không ra thì đường thủy lại không có. Nếu có đường thủy nữa thì sẽ giải quyết được nhu cầu này cho khách được thuận lợi hơn. Và giải quyết được một lưu lượng khách ra thăm và viếng ở ngoài Côn Đảo sẽ thuận lợi hơn. Xét thấy nhu cầu về du lịch kết nối giữa Cần Thơ với Côn Đảo tại Cần Thơ và Đồng bằng sông Cửu Long cũng như khách du lịch các nơi có một nhu cầu rất lớn mà hiện nay chưa có tuyến tàu vận tải thủy từ Cần Thơ ra Côn Đảo. Từ đó, tôi có chất vấn với Bộ trưởng Bộ Giao thông vận tải về vấn đề này.Bộ Giao thông vận tải cũng có trả lời và đặt vấn đề bổ sung vào danh mục giao thông vận tải thủy. Trong danh mục đó, sau đó cũng được ghi nhận của Bộ là cho bổ sung thêm một tuyến Cần Thơ – Côn Đảo. Thì như vậy, ngoài tuyến hàng không từ Cần Thơ ra Côn Đảo, còn có tuyến đường thủy, khách đi đường bộ từ Cần Thơ tới cảng Trần Đề của Sóc Trăng là khoảng trên 90 km, sau đó từ cảng Trần Đề tới Côn Đảo. Ngoài vận chuyển khách du lịch còn có vận chuyển về hàng hóa, rồi lương thực, rau củ quả của đồng bằng ra đảo. Nếu từ Cần Thơ mà đi bằng đường thủy thì thuận lợi hơn. Nếu đi từ cảng Trần Đề - Sóc Trăng thì phải qua một chặng đường là đường bộ nhiều công đoạn cũng không được thuận lợi lắm. Nhưng trước mắt bổ sung như vậy là đáp ứng và giải quyết rất lớn nhu cầu giao thương, du lịch của người dân đồng bằng.Tôi cho rằng Bộ giao thông vận tải cũng đã khảo sát và có xem xét là một nhu cầu thực sự và tạo điều kiện thuận lợi không chỉ khách du lịch mà cho vận chuyển được hàng hóa từ Cần Thơ ra. Vận chuyển như vậy cũng bớt đi công đoạn rất nhiều. Ví dụ như đi bằng phương tiện hàng không, từ sân bay Cần Thơ ra Côn Đảo, chi phí của hàng không nhiều khi cũng cao hơn hoặc du khách, người dân có nhu cầu không muốn đi đường hàng không mà muốn đi bằng đường thủy. Điều đó giúp họ có thể dễ dàng tham quan luôn tuyến sông Hậu và một số địa phương xung quanh của vùng Đồng bằng sông Cửu Long. Từ đó, họ sẽ có trải nghiệm trên sông và biển. Bên cạnh đó thì có thể kết hợp để đưa hàng hóa ra ngoài Côn Đảo thuận lợi, chi phí có thể giảm hơn và thuận lợi hơn.Tôi nghĩ rằng tuyến này sẽ kích cầu cho phát triển du lịch và kích cầu cho việc lưu thông hàng hóa trong vùng Đồng bằng sông Cửu Long, trong nội địa đất liền bằng đường thủy ra ngoài Côn Đảo. Như vậy sẽ được thuận lợi và phong phú hơn. Bởi vì đi phương tiện cao tốc thì không chỉ phục vụ trở khách mà còn kết hợp chở hàng hóa. Qua dịch bệnh Covid 19, chúng ta đang phát động kích cầu du lịch trong nước và trong nội địa. Người Việt Nam đi du lịch trong đất nước của chính mình. Trong đó có việc ra Côn Đảo sẽ là một điểm đến thú vị và mang tính giáo dục cao.Tôi cho rằng nếu được thì sẽ phát triển tuyến du lịch bằng đường thủy từ Cần Thơ ra Côn Đảo để giải quyết được nhu cầu hiện nay, nhất là trong cao điểm phòng chống dịch. Côn Đảo là một trong những vùng đất thiêng, nơi mà thể hiện giáo dục truyền thống lịch sử, truyền thống văn hóa dân tộc. Nơi chúng ta tìm hiểu để hiểu biết hơn trong việc bảo vệ biển đảo của tổ quốc trong tình hình hiện nay. Đây cũng là vấn đề tạo điều kiện cho các thế hệ được đến Côn Đảo một cách thuận lợi, qua đó hiểu hơn về ý nghĩa lịch sử của vùng đất này. Tời giờ này Bộ Giao thông đã đưa vào danh mục bổ sung khai thác tuyến giao thông vận tải thủy Cần Thơ – Côn Đảo và hiện nay đang ở trong giai đoạn các nhà đầu tư, các tổ chức cá nhân nghiên cứu để khai thác tuyến này. Tất nhiên chủ trương có đưa vào danh mục là trong một thời gian có một sự chuẩn bị khai thác. Tôi được biết các nhà đầu tư đang chuẩn bị các phương tiện để khai thác tuyến này thông qua các loại tàu cao tốc hiện nay. Vậy thì về phía địa phương là Cần Thơ đang kêu gọi các nhà đầu tư trong tư thế sẵn sàng. Bộ Giao thông cùng với Cần Thơ, Bà Rịa Vũng Tàu sẽ có gặp nhau để kết nối thực hiện.Theo quy hoạch, năm 2030 Côn Đảo sẽ đón 300.000 lượt khách, trong đó 40% lượng khách quốc tế. Tuy nhiên, tới tháng 6/2020 lượng du khách đã vượt quy hoạch được phê duyệt. Với những giá trị lịch sử, tâm linh đặc biệt cùng với phong cảnh thiên nhiên được ưu đãi, hành trình tới Côn Đảo ngày càng hấp dẫn du khách trong và ngoài nước. Cùng với vận tải đường hàng không, tuyến Cần Thơ - Côn Đảo xuất phát từ Cần Thơ khai thác vào danh mục tuyến vận tải thủy sẽ góp phần tích cực mang lại giá trị văn hóa – du lịch, thúc đẩy phát triển kinh tế xã hội của vùng.Thủ phủ miền tây Cần Thơ có nhiều lợi thế, trong đó giao thông thủy được xem là tiềm năng “trời phú” của miền Tây mà không nơi nào có được. Việc khai thác, phát triển du lịch bằng tuyến đường thủy nội địa là phù hợp với chủ trương thu hút đầu tư của địa phương là điều mà lãnh đạo thành phố, đại biểu Quốc hội thành phố Cần Thơ hết sức mong muốn. Dự án được triển khai, khai thác vào danh mục tuyến vận tải thủy từ đất liền ra đảo sẽ góp phần vào sự phát triển kinh tế- xã hội của thành phố Cần Thơ nói riêng và vùng Đồng Bằng Sông Cửu Long nói chung cũng như du lịch cả nước./. ", "topic": "Nhà đất", "tag": ["Côn Đảo", "Cần Thơ", "Nguyễn Thanh Xuân", "Cảng Bến Đầm", "Danh mục", "Vận tải", "Bộ giao thông vận tải", "Cảng Trần Đề", "Bộ trưởng bộ giao thông vận tải", "Nguyễn Văn Thể", "Chất vấn", "Thành phố Cần Thơ", "Tàu cao tốc", "Quốc Hội Khóa XIV", "Đại biểu quốc hội", "Đảo", "Côn Đảo Express 36", "Tàu khách", "Phú Quốc Express", "Du lịch"], "link": "http://quochoi.vn/hoatdongdbqh/pages/tin-hoat-dong-dai-bieu.aspx?itemid=47386"}
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[{"detail": [{"content": "Course Descriptions - Applied Statistics - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Applied Statistics Master of Science Program Overview Careers Degree Requirements Sample Schedules Course Descriptions 2015-2016 Courses FAQ Admissions Financial Aid People Events Contact Us Center for the Promotion of Research Involving Innovative Statistical Methodology (PRIISM) Course Descriptions Links to syllabi for courses offered 2014-15 are given here . Links to syllabi for the prior year are given here . APSTA-GE.2003: Intermediate Quantitative Methods: The General Linear Model This course follows a first year statistics class (pre-requisite for matriculation in the program) by examining more deeply multiple regression/correlation as a general and flexible system for analyzing data in the behavioral, social, and health sciences. In addition to covering more advanced topics related to traditional multiple regression/correlation, the course examines ANOVA, ANCOVA, and Path Analysis as special cases of this general linear model. A statistical software package is used to give students hands-on experience with topics covered. STAT-GB 2301: Regression and Multivariate Data Analysis (through Stern) This is a data-driven, applied statistics course focusing on the analysis of data using regression models. It emphasizes applications to the analysis of business and other data and makes extensive use of computer statistical packages. Topics include simple and multiple linear regression, residual analysis and other regression diagnostics, multicollinearity and model selection, autoregression, heteroscedasticity, regression models using categorical predictors, and logistic regression. All topics are illustrated on real data sets obtained from financial markets, market research studies, and other scientific inquiries. The goal of the class is that students begin to develop the skills to be able to collect, organize, analyze, and interpret regression data. APSTA-GE.2004: Advanced Modeling I: Topics in Multivariate Analysis This course extends the material covered in Intermediate Statistics by examining some of the more advanced topics in multivariate data analysis for the behavioral, social, and health sciences that are prerequisite to continuing the study of quantitative methods at NYU. The topics to be covered are logistic regression, multivariate analysis of variance, repeated measures analysis of variance, and an introduction to hierarchical linear modeling. A software package is used to give students hands-on experience with topics covered. In so doing, the course provides foundational skills and knowledge critical to those graduate students whose research relies on the analysis of quantitative data. APSTA-GE.2011: Advanced Topics in Quantitative Methods: Classification and Clustering Classification and clustering are important statistical techniques commonly applied in many social and behavioral science research problems. Both seek to understand social phenomena through the identification of naturally occurring homogeneous groupings within a population. Classification techniques are used to sort new observations into pre-existing or known groupings, while clustering techniques sort the population under study into groupings based on their observed characteristics. Both help to reveal hidden structure that may be used in further analyses. This course will compare and contrast these techniques, including many of their variations, with an emphasis on applications. APSTA-GE.2012: Advanced Topics in Quantitative Methods: Causal Inference: Statistical Methods for Program Evaluation and Policy Research The course provides students with a basic knowledge of both how to perform analyses and critique the use of some more advanced statistical methods useful in answering policy questions. While randomized experiments will be discussed, the primary focus will be the challenge of answering causal questions using data that do not meet such standards. Several approaches for observational data including propensity score methods, instrumental variables, difference in differences, fixed effects models and regression discontinuity designs will be discussed. APSTA-GE.2013: Advanced Topics in Quantitative Methods: Missing Data This course provides students with a basic knowledge of the potential implications of missing data on their data analyses as well as potential solutions. We will begin by discussing different types of mechanisms that can generate missing data. This will lay the groundwork for discussions of what types of missing data scenarios can be accommodated by each missing data method discussed subsequently. Simple missing data fixes will be described next as well as the problems they can create in terms of bias and loss of efficiency. We next explore some slightly more complicated fixes and the assumptions required for valid inference for each. The course will end with a focus on multiple imputation including discussions of the general framework, different models and algorithms and the basic theory. APSTA-GE.2015: Advanced Topics in Quantitative Methods: Applied Spatial Statistics Spatial data arise when information is collected on units that reside in different locations. Common examples include geology, criminology and epidemiology, where the goal may be to identify patterning or clusters (‘hot spots’) in the outcomes across the terrain being examined. In the social sciences, a similar set of questions and techniques are required, for example in studies of homelessness, poverty, environmental justice, and education. However, spatial data present a novel set of exploratory and modeling challenges, given the unique way in which outcomes are related (correlated) with each other through proximity. This course is an overview of the methods needed to analyze data for which it is suspected that the spatial component plays an important role. APSTA-GE.2016: Advanced Topics in Quantitative Methods: Factor Scoring and Practical Issues in Scaling This course reviews and expands on the topics of measurement and reliability. The general topic is factor scoring, with a focus on how to implement, evaluate, and interpret different methods of scoring in the multidimensional factor model. Students will also learn how to conduct exploratory factor analysis using rigorous methods for assessing goodness of fit and dimensionality. The course is especially well suited to students who have collected test/questionnaire data and require a method for getting scores from those data, scores which can then be used in further analyses. APSTA-GE.2040: Multilevel Models: Growth Curves This is a course on models for multilevel growth curve data. These data arise in longitudinal designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. In this module, we will develop and fit a set of models for longitudinal. The course assignments will use state of the art statistical software to explore, fit and interpret the models. APSTA-GE.2041: Practicum in Multi-Level Models This is practicum course on models for multilevel growth curve data. This course is a natural sequel to Multi-Level Modeling: Growth Curves. Building on the theory and examples developed in that course, students will participate in a guided, larger research project that employs multi-level growth curve models. Students will meet in groups with the instructor in a lab setting to fit, evaluate and describe these models. The final project for the course will consist of a “results and discussion” section, journal article quality write-up. APSTA-GE.2042: Multilevel Models: Nested Data Models This is a course on models for multilevel nested data. These data arise in nested designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. The course assignments will use state of the art statistical software to explore, fit and interpret the models. APSTA-GE.2094: Factor Analysis and Structural Equation Modeling Course provides students with the software skills and theoretical knowledge required to apply structural equation modeling (SEM). First, we review of multiple regression and basic concepts from matrix algebra. Next, path analysis and factor analysis are developed, leading to more advanced topics, including how to translate theory into models, strategies for dealing with poor fitting specifications, categorical data, and issues in multigroup analysis. The latter component focuses on data applications of SEM, including a final project. Example material will be drawn from education and psychology. APSTA-GE.2110: Applied Statistics: Using Large Databases in Education Research This course is designed to serve as a bridge between more theoretical coursework in applied statistics (introductory course in statistics and linear models) and practical work with real, large-scale data. Although the focus is mainly on datasets relevant to education and educational policy research, the skills taught in the course are broadly transferrable across subject areas in social, behavioral, and health sciences. At the conclusion of this course students will be prepared to produce descriptive statistics about a population using data collected under complex survey design and to estimate simple cross-sectional and longitudinal regression models of the sort frequently employed in real applied data analysis. RESCH-GE.2134: Experimental and Quasi-Experimental Design This course covers issues related to the design of research studies. Topics include observational, quasi-experimental, and experimental studies. The course focuses on practical aspects of design, linking these to appropriate analyses. RESCH-GE.2139: Survey Research I This course provides a broad overview of the many aspects of survey research methodology including sampling, instrument design, the psychology of survey response, field testing, survey operations, nonresponse bias analysis and correction, and primary and secondary analysis of survey data. The course is designed primarily for those who intend to use surveys in their own research – whether designing original surveys or performing secondary analysis on survey data collected by others. Whenever possible, we will use examples and data from real surveys employed by academic researchers, professional survey firms, and Federal statistical agencies. SOC-GA 2306 Event History Analysis (through FAS/Sociology) This course surveys methods for analyzing event history data, with a focus on continuous-time models and estimation techniques. Topics include the exploratory analysis of event history data, nonparametric methods, right censoring, maximum likelihood estimation, alternative specifications for a time dependent baseline hazard rate, observed and unobserved heterogeneity, time-varying covariates, proportional and nonproportional models, multiple transition and competing risk models, left truncation and left censoring, and analogs of recursive and nonrecursive models. Major emphasis is placed on the logic, practical use, and estimation of models. APSTA-GE.2401: Statistical Consulting Research Seminar This course is designed to assist graduate students in the quantitative methods specific to the design and analysis of their theses. In this seminar format, under the guidance of one or more statistical faculty members, students will have the opportunity to present and defend their scholarly work-in-progress. They will also be required to critique and provide constructive suggestions for their fellow students. The focus of critiques will be on the research methodology and other statistical issues. Students will additionally benefit from being able to observe how the participating faculty diagnose and solve statistical issues that arise in others’ presented work and to benefit from this advice in their own work. In essence this course provides training in statistical consulting along with detailed feedback on one’s dissertation research. APSTA-GE.2997: Advanced Methodology in Health and Policy Research: Generalized Linear Models This course is a second year course in advanced statistical techniques that covers useful quantitative tools in health and policy research. Assuming a strong foundation in regression and the general linear model, this course focuses on data analysis that utilizes models for categorical, discrete or limited outcomes that are commonly seen in health and policy studies. Examples are drawn from broad areas of health and policy research including determinants of self-reported health status or factors influencing number of clinic visits. In this course students will also learn the principles of likelihood-based inference, which will assist them in some of the more advanced statistics courses. APSTA-GE.2998: Advanced Methodology in Health and Policy Research: Survival Analysis This course is a second year course in advanced statistical techniques that introduces basic concepts and methods for analyzing survival time data obtained by tracking individuals until occurrence of an event or their loss to follow-up (e.g., time to death or degree). Assuming a strong foundation in regression and the inferential statistics, this course will cover nonparametric, semiparametric and parametric survival models that are commonly seen in health and policy studies. Students will also learn discrete time event history models, which are useful tools in longitudinal data analysis. Examples in this course are drawn from broad areas in health and policy research including, for example: assessing risk factors for time to death among heart transplant patients and assessing risk factors for juvenile delinquency. Social Research Foundation RESCH-GE.2132: Principles of Empirical Research This course introduces advanced study of social science research methods. The course examines: 1), the relationship between theory and research; 2), methodological issues, such as objectivity, the logic of argument, reliability and validity; and 3), exemplars of various methodological techniques, including survey, interview, experimental, archival and ethnographic research. The course is designed to train social scientists to both recognize and be able to conduct rigorous, theoretically informed research. Additional Courses APSTA-GE.2300. Internship Course In the internship, students will gain experience working with “real world” data, working with an approved faculty member, local firm or organization. Students will receive practical training focused on the kinds of issues that researchers face in collecting and analyzing data. This course will not only enhance the tools and techniques students develop, but will also possibly lead to employment opportunities after graduation.   This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}]},
{"detail": [{"content": "Course Descriptions - Applied Statistics - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Applied Statistics Master of Science Program Overview Careers Degree Requirements Sample Schedules Course Descriptions 2015-2016 Courses FAQ Admissions Financial Aid People Events Contact Us Center for the Promotion of Research Involving Innovative Statistical Methodology (PRIISM) Course Descriptions Links to syllabi for courses offered 2014-15 are given here . Links to syllabi for the prior year are given here . APSTA-GE.2003: Intermediate Quantitative Methods: The General Linear Model This course follows a first year statistics class (pre-requisite for matriculation in the program) by examining more deeply multiple regression/correlation as a general and flexible system for analyzing data in the behavioral, social, and health sciences. In addition to covering more advanced topics related to traditional multiple regression/correlation, the course examines ANOVA, ANCOVA, and Path Analysis as special cases of this general linear model. A statistical software package is used to give students hands-on experience with topics covered. STAT-GB 2301: Regression and Multivariate Data Analysis (through Stern) This is a data-driven, applied statistics course focusing on the analysis of data using regression models. It emphasizes applications to the analysis of business and other data and makes extensive use of computer statistical packages. Topics include simple and multiple linear regression, residual analysis and other regression diagnostics, multicollinearity and model selection, autoregression, heteroscedasticity, regression models using categorical predictors, and logistic regression. All topics are illustrated on real data sets obtained from financial markets, market research studies, and other scientific inquiries. The goal of the class is that students begin to develop the skills to be able to collect, organize, analyze, and interpret regression data. APSTA-GE.2004: Advanced Modeling I: Topics in Multivariate Analysis This course extends the material covered in Intermediate Statistics by examining some of the more advanced topics in multivariate data analysis for the behavioral, social, and health sciences that are prerequisite to continuing the study of quantitative methods at NYU. The topics to be covered are logistic regression, multivariate analysis of variance, repeated measures analysis of variance, and an introduction to hierarchical linear modeling. A software package is used to give students hands-on experience with topics covered. In so doing, the course provides foundational skills and knowledge critical to those graduate students whose research relies on the analysis of quantitative data. APSTA-GE.2011: Advanced Topics in Quantitative Methods: Classification and Clustering Classification and clustering are important statistical techniques commonly applied in many social and behavioral science research problems. Both seek to understand social phenomena through the identification of naturally occurring homogeneous groupings within a population. Classification techniques are used to sort new observations into pre-existing or known groupings, while clustering techniques sort the population under study into groupings based on their observed characteristics. Both help to reveal hidden structure that may be used in further analyses. This course will compare and contrast these techniques, including many of their variations, with an emphasis on applications. APSTA-GE.2012: Advanced Topics in Quantitative Methods: Causal Inference: Statistical Methods for Program Evaluation and Policy Research The course provides students with a basic knowledge of both how to perform analyses and critique the use of some more advanced statistical methods useful in answering policy questions. While randomized experiments will be discussed, the primary focus will be the challenge of answering causal questions using data that do not meet such standards. Several approaches for observational data including propensity score methods, instrumental variables, difference in differences, fixed effects models and regression discontinuity designs will be discussed. APSTA-GE.2013: Advanced Topics in Quantitative Methods: Missing Data This course provides students with a basic knowledge of the potential implications of missing data on their data analyses as well as potential solutions. We will begin by discussing different types of mechanisms that can generate missing data. This will lay the groundwork for discussions of what types of missing data scenarios can be accommodated by each missing data method discussed subsequently. Simple missing data fixes will be described next as well as the problems they can create in terms of bias and loss of efficiency. We next explore some slightly more complicated fixes and the assumptions required for valid inference for each. The course will end with a focus on multiple imputation including discussions of the general framework, different models and algorithms and the basic theory. APSTA-GE.2015: Advanced Topics in Quantitative Methods: Applied Spatial Statistics Spatial data arise when information is collected on units that reside in different locations. Common examples include geology, criminology and epidemiology, where the goal may be to identify patterning or clusters (‘hot spots’) in the outcomes across the terrain being examined. In the social sciences, a similar set of questions and techniques are required, for example in studies of homelessness, poverty, environmental justice, and education. However, spatial data present a novel set of exploratory and modeling challenges, given the unique way in which outcomes are related (correlated) with each other through proximity. This course is an overview of the methods needed to analyze data for which it is suspected that the spatial component plays an important role. APSTA-GE.2016: Advanced Topics in Quantitative Methods: Factor Scoring and Practical Issues in Scaling This course reviews and expands on the topics of measurement and reliability. The general topic is factor scoring, with a focus on how to implement, evaluate, and interpret different methods of scoring in the multidimensional factor model. Students will also learn how to conduct exploratory factor analysis using rigorous methods for assessing goodness of fit and dimensionality. The course is especially well suited to students who have collected test/questionnaire data and require a method for getting scores from those data, scores which can then be used in further analyses. APSTA-GE.2040: Multilevel Models: Growth Curves This is a course on models for multilevel growth curve data. These data arise in longitudinal designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. In this module, we will develop and fit a set of models for longitudinal. The course assignments will use state of the art statistical software to explore, fit and interpret the models. APSTA-GE.2041: Practicum in Multi-Level Models This is practicum course on models for multilevel growth curve data. This course is a natural sequel to Multi-Level Modeling: Growth Curves. Building on the theory and examples developed in that course, students will participate in a guided, larger research project that employs multi-level growth curve models. Students will meet in groups with the instructor in a lab setting to fit, evaluate and describe these models. The final project for the course will consist of a “results and discussion” section, journal article quality write-up. APSTA-GE.2042: Multilevel Models: Nested Data Models This is a course on models for multilevel nested data. These data arise in nested designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. The course assignments will use state of the art statistical software to explore, fit and interpret the models. APSTA-GE.2094: Factor Analysis and Structural Equation Modeling Course provides students with the software skills and theoretical knowledge required to apply structural equation modeling (SEM). First, we review of multiple regression and basic concepts from matrix algebra. Next, path analysis and factor analysis are developed, leading to more advanced topics, including how to translate theory into models, strategies for dealing with poor fitting specifications, categorical data, and issues in multigroup analysis. The latter component focuses on data applications of SEM, including a final project. Example material will be drawn from education and psychology. APSTA-GE.2110: Applied Statistics: Using Large Databases in Education Research This course is designed to serve as a bridge between more theoretical coursework in applied statistics (introductory course in statistics and linear models) and practical work with real, large-scale data. Although the focus is mainly on datasets relevant to education and educational policy research, the skills taught in the course are broadly transferrable across subject areas in social, behavioral, and health sciences. At the conclusion of this course students will be prepared to produce descriptive statistics about a population using data collected under complex survey design and to estimate simple cross-sectional and longitudinal regression models of the sort frequently employed in real applied data analysis. RESCH-GE.2134: Experimental and Quasi-Experimental Design This course covers issues related to the design of research studies. Topics include observational, quasi-experimental, and experimental studies. The course focuses on practical aspects of design, linking these to appropriate analyses. RESCH-GE.2139: Survey Research I This course provides a broad overview of the many aspects of survey research methodology including sampling, instrument design, the psychology of survey response, field testing, survey operations, nonresponse bias analysis and correction, and primary and secondary analysis of survey data. The course is designed primarily for those who intend to use surveys in their own research – whether designing original surveys or performing secondary analysis on survey data collected by others. Whenever possible, we will use examples and data from real surveys employed by academic researchers, professional survey firms, and Federal statistical agencies. SOC-GA 2306 Event History Analysis (through FAS/Sociology) This course surveys methods for analyzing event history data, with a focus on continuous-time models and estimation techniques. Topics include the exploratory analysis of event history data, nonparametric methods, right censoring, maximum likelihood estimation, alternative specifications for a time dependent baseline hazard rate, observed and unobserved heterogeneity, time-varying covariates, proportional and nonproportional models, multiple transition and competing risk models, left truncation and left censoring, and analogs of recursive and nonrecursive models. Major emphasis is placed on the logic, practical use, and estimation of models. APSTA-GE.2401: Statistical Consulting Research Seminar This course is designed to assist graduate students in the quantitative methods specific to the design and analysis of their theses. In this seminar format, under the guidance of one or more statistical faculty members, students will have the opportunity to present and defend their scholarly work-in-progress. They will also be required to critique and provide constructive suggestions for their fellow students. The focus of critiques will be on the research methodology and other statistical issues. Students will additionally benefit from being able to observe how the participating faculty diagnose and solve statistical issues that arise in others’ presented work and to benefit from this advice in their own work. In essence this course provides training in statistical consulting along with detailed feedback on one’s dissertation research. APSTA-GE.2997: Advanced Methodology in Health and Policy Research: Generalized Linear Models This course is a second year course in advanced statistical techniques that covers useful quantitative tools in health and policy research. Assuming a strong foundation in regression and the general linear model, this course focuses on data analysis that utilizes models for categorical, discrete or limited outcomes that are commonly seen in health and policy studies. Examples are drawn from broad areas of health and policy research including determinants of self-reported health status or factors influencing number of clinic visits. In this course students will also learn the principles of likelihood-based inference, which will assist them in some of the more advanced statistics courses. APSTA-GE.2998: Advanced Methodology in Health and Policy Research: Survival Analysis This course is a second year course in advanced statistical techniques that introduces basic concepts and methods for analyzing survival time data obtained by tracking individuals until occurrence of an event or their loss to follow-up (e.g., time to death or degree). Assuming a strong foundation in regression and the inferential statistics, this course will cover nonparametric, semiparametric and parametric survival models that are commonly seen in health and policy studies. Students will also learn discrete time event history models, which are useful tools in longitudinal data analysis. Examples in this course are drawn from broad areas in health and policy research including, for example: assessing risk factors for time to death among heart transplant patients and assessing risk factors for juvenile delinquency. Social Research Foundation RESCH-GE.2132: Principles of Empirical Research This course introduces advanced study of social science research methods. The course examines: 1), the relationship between theory and research; 2), methodological issues, such as objectivity, the logic of argument, reliability and validity; and 3), exemplars of various methodological techniques, including survey, interview, experimental, archival and ethnographic research. The course is designed to train social scientists to both recognize and be able to conduct rigorous, theoretically informed research. Additional Courses APSTA-GE.2300. Internship Course In the internship, students will gain experience working with “real world” data, working with an approved faculty member, local firm or organization. Students will receive practical training focused on the kinds of issues that researchers face in collecting and analyzing data. This course will not only enhance the tools and techniques students develop, but will also possibly lead to employment opportunities after graduation.   This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}, {"content": "Interdepartmental Research Studies/Applied Statistics Courses 2015-2016 - Interdepartmental Research Studies - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Interdepartmental Research Studies Information News Course Descriptions Related Courses FAQ Interdepartmental Research Studies/Applied Statistics Courses 2015-2016 Fall 2015 | January Intersession 2016 | Spring 2016 | Summer 2016 | CLASS ARCHIVE Please click on the course titles below for sample syllabi. The syllabi are copyrighted material and are intended for informational use only. The instructor listed on the syllabus may differ from those currently planning to teach the course. Fall 2015 Course # Call # Course Title Days / Times Pts Instructor APSTA-UE 1085-001 Basic Statistics I (undergraduate) Registration for one lab section is required MW 3:30-4:45PM Lab: R 3:30-4:45PM; F 11:00AM-12:15PM 4 R. Hassad APSTA -GE 2001-001 3274 Statistics for the Behavioral and Social Sciences I W 2:00-4:45PM 3 S. Weinberg APSTA -GE 2001-002 3275 Statistics for the Behavioral and Social Sciences I M 4:45-7:35PM 3 S. Corcoran APSTA -GE 2003-001 req'd lab-002 3276, 3277 Intermediate Quantitative Methods: The General Linear Model T 3:30-6:10PM Lab R 3:30-4:45PM 3 S. Weinberg; APSTA -GE 2012 3278 Adv Topics in Quant Meths: Causal Inference: Statistical Methods for Program Evaluation and Policy Research F 9:30AM - 12:30PM 3 J. Hill APSTA -GE 2015 17387 Topics in Advanced Quantitative Methods: Applied Spatial Statistics T 3.30 PM - 6.45 PM 2 (09/08/2015 - 10/27/2015) T. Mijanovich APSTA -GE 2016 4627 Adv Topics in Quant Meths: Factor Scoring and Practical Issues in Scaling M 2.00 PM - 4.45 PM 2 (09/14/2015 - 10/19/2015) P. Halpin APSTA -GE 2040 6162 Multilevel Models: growth curves Not offered 2015-16 See APSTA-GE 2042 Winter 2016 APSTA -GE 2041 6163 Practicum in Multi-Level Models Growth Curves *to be taken concurrently with APSTA -GE.2040 See Winter 2016 APSTA -GE 2085 3281 Basic Statistics I (masters) M 6:20-9:00PM 3 D. Chan APSTA -GE 2086 3282 Basic Statistics II T 6:20-9:00PM 3 D. Chan RESCH-GE 2132 3283 Principles of Empirical Research T 2:00-3:40PM 3 C. Voulgarides RESCH-GE 2138 4462 Writing Empirical Research T 4:55-6:35PM 3 Norman RESCH-GE 2139 4129 Survey Research Methods R 4:55-7:35PM 3 D. Harel RESCH-GE 2140 3284/3285 Approaches to Qualitative Inquiry T 2:00-4:55PM / R 4:55-6:35PM 3 Norman / Larson RESCH-GE 2143 3286 Participatory Action Research R 6:45-8:25PM 3 Middleton APSTA -GE 2300 4836-4841 Independent Study 1-6 M. Scott; J.Hill, S. Weinberg; Y. Lu; P. Halpin; D. Harel APSTA-GE 2401 17388 Statistical Consulting Research Seminar W 12.30 PM - 2.30 PM 1-3 J. Hill & Y. Lu APSTA -GE 2995-001 3294, 4464 Biostatistics I (required lab) T 4:55-6:35PM T 6:45-8:00PM 3 Y. Lu January Intersession 2016 (students register for this course as part of Spring 2016) Course # Course Title Days / Times Pts Instructor APSTA-GE 2042 Multilevel Nested Data Models TBD 2 M. Scott APSTA-GE 2041 Practicum in Multi-Level Models TBD 1 M. Scott Spring 2016 Course # Course Title Days / Times Pts Instructor APSTA-UE 0010 Statistical Mysteries and How to Solve Them TR 2:00-3:15pm Lab T 3:30-4:30pm 4 J. Hill / D. Harel APSTA-UE 1085-001 Basic Statistics I (undergraduate) Registration for one lab section is required MW 3:30-4:45PM Lab: R TBD; F 11:00AM-12:15PM; F 12:30-1:45PM 4 R. Hassad APSTA -GE 2002 Statistics for the Behavioral and Social Sciences II W 2:00-4:30PM 3 S. Weinberg APSTA -GE 2004 req'd lab Advanced Modeling I: Topics in Multivariate Analysis T 3:30-6:10PM Lab T 6:20-7:30PM (first 7 weeks of term) 2 S. Weinberg APSTA-GE 2013 Advanced Topics in Quantiative Methods: Missing Data F 9:30-12:15 (SECOND 7 weeks of term) 2 J. Hill APSTA-GE 2017 Advanced Topics in Quantitative Methods: Educational Data Science Practicum 12:45-3:25pm (SECOND 7 weeks of term) 2 Y. Bergner APSTA -GE 2042 Advanced Topics in Quantiative Methods:Nested-Data Models Offered Winter 2016 2 M. Scott APSTA -GE 2085 Basic Statistics I (masters) M 6:20-9:00PM 3 D. Chan APSTA -GE 2094 Factor Analysis and Structural Equation Modeling R 2:00-4:45PM 3 P. Halpin APSTA -GE 2110 Applied Statistics: Using Large Databases in Education Research W 4:55-7:35PM 4 S. Corcoran / K. Ziol-Guest APSTA-GE 2122 Applied Statistical Modeling and Inference Lec M 930-1130am Lab T 710-8pm 3 D. Harel / Y. Lu APSTA-GE 2300 Independent Study TBA 1-6 Staff APSTA -GE 2996-001 Biostatistics II T 4:55-6:35PM Lab: T 6:45-8:25PM 3 T. Mijanovich APSTA -GE 2997 Generalized Linear and Multilevel Growth Curve Models Lect F 9:30AM - 11:00PM Lab 11:10-12:30pm (meets FIRST 7 weeks of semester) 2 Y. Lu Summer 2016 Course # Course Title Days / Times Pts Instructor APSTA-GE 2300 IND Study TBA 1-6 S. Weinberg This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}]},
{"detail": [{"content": "Course Descriptions - Applied Statistics - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Applied Statistics Master of Science Program Overview Careers Degree Requirements Sample Schedules Course Descriptions 2015-2016 Courses FAQ Admissions Financial Aid People Events Contact Us Center for the Promotion of Research Involving Innovative Statistical Methodology (PRIISM) Course Descriptions Links to syllabi for courses offered 2014-15 are given here . Links to syllabi for the prior year are given here . APSTA-GE.2003: Intermediate Quantitative Methods: The General Linear Model This course follows a first year statistics class (pre-requisite for matriculation in the program) by examining more deeply multiple regression/correlation as a general and flexible system for analyzing data in the behavioral, social, and health sciences. In addition to covering more advanced topics related to traditional multiple regression/correlation, the course examines ANOVA, ANCOVA, and Path Analysis as special cases of this general linear model. A statistical software package is used to give students hands-on experience with topics covered. STAT-GB 2301: Regression and Multivariate Data Analysis (through Stern) This is a data-driven, applied statistics course focusing on the analysis of data using regression models. It emphasizes applications to the analysis of business and other data and makes extensive use of computer statistical packages. Topics include simple and multiple linear regression, residual analysis and other regression diagnostics, multicollinearity and model selection, autoregression, heteroscedasticity, regression models using categorical predictors, and logistic regression. All topics are illustrated on real data sets obtained from financial markets, market research studies, and other scientific inquiries. The goal of the class is that students begin to develop the skills to be able to collect, organize, analyze, and interpret regression data. APSTA-GE.2004: Advanced Modeling I: Topics in Multivariate Analysis This course extends the material covered in Intermediate Statistics by examining some of the more advanced topics in multivariate data analysis for the behavioral, social, and health sciences that are prerequisite to continuing the study of quantitative methods at NYU. The topics to be covered are logistic regression, multivariate analysis of variance, repeated measures analysis of variance, and an introduction to hierarchical linear modeling. A software package is used to give students hands-on experience with topics covered. In so doing, the course provides foundational skills and knowledge critical to those graduate students whose research relies on the analysis of quantitative data. APSTA-GE.2011: Advanced Topics in Quantitative Methods: Classification and Clustering Classification and clustering are important statistical techniques commonly applied in many social and behavioral science research problems. Both seek to understand social phenomena through the identification of naturally occurring homogeneous groupings within a population. Classification techniques are used to sort new observations into pre-existing or known groupings, while clustering techniques sort the population under study into groupings based on their observed characteristics. Both help to reveal hidden structure that may be used in further analyses. This course will compare and contrast these techniques, including many of their variations, with an emphasis on applications. APSTA-GE.2012: Advanced Topics in Quantitative Methods: Causal Inference: Statistical Methods for Program Evaluation and Policy Research The course provides students with a basic knowledge of both how to perform analyses and critique the use of some more advanced statistical methods useful in answering policy questions. While randomized experiments will be discussed, the primary focus will be the challenge of answering causal questions using data that do not meet such standards. Several approaches for observational data including propensity score methods, instrumental variables, difference in differences, fixed effects models and regression discontinuity designs will be discussed. APSTA-GE.2013: Advanced Topics in Quantitative Methods: Missing Data This course provides students with a basic knowledge of the potential implications of missing data on their data analyses as well as potential solutions. We will begin by discussing different types of mechanisms that can generate missing data. This will lay the groundwork for discussions of what types of missing data scenarios can be accommodated by each missing data method discussed subsequently. Simple missing data fixes will be described next as well as the problems they can create in terms of bias and loss of efficiency. We next explore some slightly more complicated fixes and the assumptions required for valid inference for each. The course will end with a focus on multiple imputation including discussions of the general framework, different models and algorithms and the basic theory. APSTA-GE.2015: Advanced Topics in Quantitative Methods: Applied Spatial Statistics Spatial data arise when information is collected on units that reside in different locations. Common examples include geology, criminology and epidemiology, where the goal may be to identify patterning or clusters (‘hot spots’) in the outcomes across the terrain being examined. In the social sciences, a similar set of questions and techniques are required, for example in studies of homelessness, poverty, environmental justice, and education. However, spatial data present a novel set of exploratory and modeling challenges, given the unique way in which outcomes are related (correlated) with each other through proximity. This course is an overview of the methods needed to analyze data for which it is suspected that the spatial component plays an important role. APSTA-GE.2016: Advanced Topics in Quantitative Methods: Factor Scoring and Practical Issues in Scaling This course reviews and expands on the topics of measurement and reliability. The general topic is factor scoring, with a focus on how to implement, evaluate, and interpret different methods of scoring in the multidimensional factor model. Students will also learn how to conduct exploratory factor analysis using rigorous methods for assessing goodness of fit and dimensionality. The course is especially well suited to students who have collected test/questionnaire data and require a method for getting scores from those data, scores which can then be used in further analyses. APSTA-GE.2040: Multilevel Models: Growth Curves This is a course on models for multilevel growth curve data. These data arise in longitudinal designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. In this module, we will develop and fit a set of models for longitudinal. The course assignments will use state of the art statistical software to explore, fit and interpret the models. APSTA-GE.2041: Practicum in Multi-Level Models This is practicum course on models for multilevel growth curve data. This course is a natural sequel to Multi-Level Modeling: Growth Curves. Building on the theory and examples developed in that course, students will participate in a guided, larger research project that employs multi-level growth curve models. Students will meet in groups with the instructor in a lab setting to fit, evaluate and describe these models. The final project for the course will consist of a “results and discussion” section, journal article quality write-up. APSTA-GE.2042: Multilevel Models: Nested Data Models This is a course on models for multilevel nested data. These data arise in nested designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. The course assignments will use state of the art statistical software to explore, fit and interpret the models. APSTA-GE.2094: Factor Analysis and Structural Equation Modeling Course provides students with the software skills and theoretical knowledge required to apply structural equation modeling (SEM). First, we review of multiple regression and basic concepts from matrix algebra. Next, path analysis and factor analysis are developed, leading to more advanced topics, including how to translate theory into models, strategies for dealing with poor fitting specifications, categorical data, and issues in multigroup analysis. The latter component focuses on data applications of SEM, including a final project. Example material will be drawn from education and psychology. APSTA-GE.2110: Applied Statistics: Using Large Databases in Education Research This course is designed to serve as a bridge between more theoretical coursework in applied statistics (introductory course in statistics and linear models) and practical work with real, large-scale data. Although the focus is mainly on datasets relevant to education and educational policy research, the skills taught in the course are broadly transferrable across subject areas in social, behavioral, and health sciences. At the conclusion of this course students will be prepared to produce descriptive statistics about a population using data collected under complex survey design and to estimate simple cross-sectional and longitudinal regression models of the sort frequently employed in real applied data analysis. RESCH-GE.2134: Experimental and Quasi-Experimental Design This course covers issues related to the design of research studies. Topics include observational, quasi-experimental, and experimental studies. The course focuses on practical aspects of design, linking these to appropriate analyses. RESCH-GE.2139: Survey Research I This course provides a broad overview of the many aspects of survey research methodology including sampling, instrument design, the psychology of survey response, field testing, survey operations, nonresponse bias analysis and correction, and primary and secondary analysis of survey data. The course is designed primarily for those who intend to use surveys in their own research – whether designing original surveys or performing secondary analysis on survey data collected by others. Whenever possible, we will use examples and data from real surveys employed by academic researchers, professional survey firms, and Federal statistical agencies. SOC-GA 2306 Event History Analysis (through FAS/Sociology) This course surveys methods for analyzing event history data, with a focus on continuous-time models and estimation techniques. Topics include the exploratory analysis of event history data, nonparametric methods, right censoring, maximum likelihood estimation, alternative specifications for a time dependent baseline hazard rate, observed and unobserved heterogeneity, time-varying covariates, proportional and nonproportional models, multiple transition and competing risk models, left truncation and left censoring, and analogs of recursive and nonrecursive models. Major emphasis is placed on the logic, practical use, and estimation of models. APSTA-GE.2401: Statistical Consulting Research Seminar This course is designed to assist graduate students in the quantitative methods specific to the design and analysis of their theses. In this seminar format, under the guidance of one or more statistical faculty members, students will have the opportunity to present and defend their scholarly work-in-progress. They will also be required to critique and provide constructive suggestions for their fellow students. The focus of critiques will be on the research methodology and other statistical issues. Students will additionally benefit from being able to observe how the participating faculty diagnose and solve statistical issues that arise in others’ presented work and to benefit from this advice in their own work. In essence this course provides training in statistical consulting along with detailed feedback on one’s dissertation research. APSTA-GE.2997: Advanced Methodology in Health and Policy Research: Generalized Linear Models This course is a second year course in advanced statistical techniques that covers useful quantitative tools in health and policy research. Assuming a strong foundation in regression and the general linear model, this course focuses on data analysis that utilizes models for categorical, discrete or limited outcomes that are commonly seen in health and policy studies. Examples are drawn from broad areas of health and policy research including determinants of self-reported health status or factors influencing number of clinic visits. In this course students will also learn the principles of likelihood-based inference, which will assist them in some of the more advanced statistics courses. APSTA-GE.2998: Advanced Methodology in Health and Policy Research: Survival Analysis This course is a second year course in advanced statistical techniques that introduces basic concepts and methods for analyzing survival time data obtained by tracking individuals until occurrence of an event or their loss to follow-up (e.g., time to death or degree). Assuming a strong foundation in regression and the inferential statistics, this course will cover nonparametric, semiparametric and parametric survival models that are commonly seen in health and policy studies. Students will also learn discrete time event history models, which are useful tools in longitudinal data analysis. Examples in this course are drawn from broad areas in health and policy research including, for example: assessing risk factors for time to death among heart transplant patients and assessing risk factors for juvenile delinquency. Social Research Foundation RESCH-GE.2132: Principles of Empirical Research This course introduces advanced study of social science research methods. The course examines: 1), the relationship between theory and research; 2), methodological issues, such as objectivity, the logic of argument, reliability and validity; and 3), exemplars of various methodological techniques, including survey, interview, experimental, archival and ethnographic research. The course is designed to train social scientists to both recognize and be able to conduct rigorous, theoretically informed research. Additional Courses APSTA-GE.2300. Internship Course In the internship, students will gain experience working with “real world” data, working with an approved faculty member, local firm or organization. Students will receive practical training focused on the kinds of issues that researchers face in collecting and analyzing data. This course will not only enhance the tools and techniques students develop, but will also possibly lead to employment opportunities after graduation.   This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}, {"content": "Interdepartmental Research Studies/Applied Statistics Courses 2015-2016 - Interdepartmental Research Studies - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Interdepartmental Research Studies Information News Course Descriptions Related Courses FAQ Interdepartmental Research Studies/Applied Statistics Courses 2015-2016 Fall 2015 | January Intersession 2016 | Spring 2016 | Summer 2016 | CLASS ARCHIVE Please click on the course titles below for sample syllabi. The syllabi are copyrighted material and are intended for informational use only. The instructor listed on the syllabus may differ from those currently planning to teach the course. Fall 2015 Course # Call # Course Title Days / Times Pts Instructor APSTA-UE 1085-001 Basic Statistics I (undergraduate) Registration for one lab section is required MW 3:30-4:45PM Lab: R 3:30-4:45PM; F 11:00AM-12:15PM 4 R. Hassad APSTA -GE 2001-001 3274 Statistics for the Behavioral and Social Sciences I W 2:00-4:45PM 3 S. Weinberg APSTA -GE 2001-002 3275 Statistics for the Behavioral and Social Sciences I M 4:45-7:35PM 3 S. Corcoran APSTA -GE 2003-001 req'd lab-002 3276, 3277 Intermediate Quantitative Methods: The General Linear Model T 3:30-6:10PM Lab R 3:30-4:45PM 3 S. Weinberg; APSTA -GE 2012 3278 Adv Topics in Quant Meths: Causal Inference: Statistical Methods for Program Evaluation and Policy Research F 9:30AM - 12:30PM 3 J. Hill APSTA -GE 2015 17387 Topics in Advanced Quantitative Methods: Applied Spatial Statistics T 3.30 PM - 6.45 PM 2 (09/08/2015 - 10/27/2015) T. Mijanovich APSTA -GE 2016 4627 Adv Topics in Quant Meths: Factor Scoring and Practical Issues in Scaling M 2.00 PM - 4.45 PM 2 (09/14/2015 - 10/19/2015) P. Halpin APSTA -GE 2040 6162 Multilevel Models: growth curves Not offered 2015-16 See APSTA-GE 2042 Winter 2016 APSTA -GE 2041 6163 Practicum in Multi-Level Models Growth Curves *to be taken concurrently with APSTA -GE.2040 See Winter 2016 APSTA -GE 2085 3281 Basic Statistics I (masters) M 6:20-9:00PM 3 D. Chan APSTA -GE 2086 3282 Basic Statistics II T 6:20-9:00PM 3 D. Chan RESCH-GE 2132 3283 Principles of Empirical Research T 2:00-3:40PM 3 C. Voulgarides RESCH-GE 2138 4462 Writing Empirical Research T 4:55-6:35PM 3 Norman RESCH-GE 2139 4129 Survey Research Methods R 4:55-7:35PM 3 D. Harel RESCH-GE 2140 3284/3285 Approaches to Qualitative Inquiry T 2:00-4:55PM / R 4:55-6:35PM 3 Norman / Larson RESCH-GE 2143 3286 Participatory Action Research R 6:45-8:25PM 3 Middleton APSTA -GE 2300 4836-4841 Independent Study 1-6 M. Scott; J.Hill, S. Weinberg; Y. Lu; P. Halpin; D. Harel APSTA-GE 2401 17388 Statistical Consulting Research Seminar W 12.30 PM - 2.30 PM 1-3 J. Hill & Y. Lu APSTA -GE 2995-001 3294, 4464 Biostatistics I (required lab) T 4:55-6:35PM T 6:45-8:00PM 3 Y. Lu January Intersession 2016 (students register for this course as part of Spring 2016) Course # Course Title Days / Times Pts Instructor APSTA-GE 2042 Multilevel Nested Data Models TBD 2 M. Scott APSTA-GE 2041 Practicum in Multi-Level Models TBD 1 M. Scott Spring 2016 Course # Course Title Days / Times Pts Instructor APSTA-UE 0010 Statistical Mysteries and How to Solve Them TR 2:00-3:15pm Lab T 3:30-4:30pm 4 J. Hill / D. Harel APSTA-UE 1085-001 Basic Statistics I (undergraduate) Registration for one lab section is required MW 3:30-4:45PM Lab: R TBD; F 11:00AM-12:15PM; F 12:30-1:45PM 4 R. Hassad APSTA -GE 2002 Statistics for the Behavioral and Social Sciences II W 2:00-4:30PM 3 S. Weinberg APSTA -GE 2004 req'd lab Advanced Modeling I: Topics in Multivariate Analysis T 3:30-6:10PM Lab T 6:20-7:30PM (first 7 weeks of term) 2 S. Weinberg APSTA-GE 2013 Advanced Topics in Quantiative Methods: Missing Data F 9:30-12:15 (SECOND 7 weeks of term) 2 J. Hill APSTA-GE 2017 Advanced Topics in Quantitative Methods: Educational Data Science Practicum 12:45-3:25pm (SECOND 7 weeks of term) 2 Y. Bergner APSTA -GE 2042 Advanced Topics in Quantiative Methods:Nested-Data Models Offered Winter 2016 2 M. Scott APSTA -GE 2085 Basic Statistics I (masters) M 6:20-9:00PM 3 D. Chan APSTA -GE 2094 Factor Analysis and Structural Equation Modeling R 2:00-4:45PM 3 P. Halpin APSTA -GE 2110 Applied Statistics: Using Large Databases in Education Research W 4:55-7:35PM 4 S. Corcoran / K. Ziol-Guest APSTA-GE 2122 Applied Statistical Modeling and Inference Lec M 930-1130am Lab T 710-8pm 3 D. Harel / Y. Lu APSTA-GE 2300 Independent Study TBA 1-6 Staff APSTA -GE 2996-001 Biostatistics II T 4:55-6:35PM Lab: T 6:45-8:25PM 3 T. Mijanovich APSTA -GE 2997 Generalized Linear and Multilevel Growth Curve Models Lect F 9:30AM - 11:00PM Lab 11:10-12:30pm (meets FIRST 7 weeks of semester) 2 Y. Lu Summer 2016 Course # Course Title Days / Times Pts Instructor APSTA-GE 2300 IND Study TBA 1-6 S. Weinberg This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}, {"content": "Degree Requirements - Applied Statistics - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Applied Statistics Master of Science Program Overview Careers Degree Requirements Sample Schedules Course Descriptions 2015-2016 Courses FAQ Admissions Financial Aid People Events Contact Us Center for the Promotion of Research Involving Innovative Statistical Methodology (PRIISM) Degree Requirements Total Points Required: 34 minimum; 34-47 variable This is a variable-credit program in which you can take a minimum of 34 and a maximum of 47 credits. If you are entering the program with prior statistical training, the accelerated, lower credit option may be ideal for you. The program consists of theoretical foundations, statistical inference and generalized linear models, causal inference, survey research methods, multilevel modeling, applied statistics electives, and unrestricted electives. A statistical consulting research seminar and internship provide practical learning experiences. See brief course descriptions . See links to syllabi for courses offered in 2013-14 . Get links to syllabi for previous years . Required Courses: 31-33 points Foundations in Social Research: Points RESCH-GE.2132: Empirical Research Methods + 3 + Students may choose to replace this requirement with a methods course being offered in their specific area of studies. Inference and Regression: APSTA-GE.2003: Intermediate Quantitative Methods* or APSTA-GE.2003 can be replaced by STAT-GB 2301: Regression and Multivariate Data Analysis 3 APSTA-GE.2004: Advanced Modeling I: Topics in Multivariate Analysis* 2 APSTA-GE.2012: Causal Inference 3 APSTA-GE 2122: Applied Statistical Modeling and Inference 3 APSTA-GE.2997: Adv. Methods in Health and Policy Res.: Generalized Linear Models 2 APSTA-GE.2998: Adv. Methods in Health and Policy Res.: Survival Analysis or SOC-GA 2306: Event History Analysis 2-4 * indicates that the student with equivalent prior coursework may place out of this course. Theoretical and Practical Issues in Survey Methodology: RESCH-GE.2139: Survey Research I 3 APSTA-GE.2110: Applied Statistics: Using Large Databases in Education Research 4 Multilevel Models: APSTA-GE.2040/2041: Multilevel Models (course + practicum) 3 APSTA-GE.2042: Multilevel Models: Nested Data 2 Missing Data: APSTA-GE.2013: Advanced Topics in Quantitative Methods: Missing Data 2 Statistical Consulting Research Seminar and Internship: APSTA-GE.2300 Internship 2-4 APSTA-GE.2401: Statistical Consulting Research Seminar 3 * indicates that the student with equivalent prior coursework may place out of this course. Applied Statistics Electives: 4-8 points Students are required to take two electives with significant applied statistical content (e.g., measurement models, computational statistics. Courses may be taken within or outside the program. WIthin-Program Applied Statistics Electives (may count in required course points for those placing out of the above required course, except those *) APSTA-GE.2011: Adv. Topics in Quantitative Methods: Classification and Clustering 2 APSTA-GE.2015: Adv. Topics in Quantitative Methods: Applied Spatial Statistics 2 APSTA-GE.2016: Adv. Topics in Quant. Meths: Factor Scoring and Practical Issues in Scaling 2 APSTA-GE.2017: Adv. Topics in Quant. Meths: Educational Dat Science Practicum 2 APSTA-GE.2094: Factor Analysis and Structural Equation Modeling 3 Outside-Program Applied Statistics Electives Steinhardt: APSY-GE.2141: Measurement: Modern Test Theory APSY-GE.2142: Measurement and Evaluation: Psychometric Theory PUHE-GE.2306: Epidemiology Courant Institute of Mathematical Sciences, Faculty of Arts and Science, or NYU Poly: MA-GY.7763: Topics in Statistics: Data Mining and Machine Learning MATH-GA 2840: Advanced Topics in Applied Mathematics-Dynamic Computational Statistical models for Socio-economic and Geo-political Systems CSCI-GA-256: Machine Learning and Pattern Recognition For more courses, visit the Data Science curriculum website . Please note some of the courses offered through Data Science may have substantial pre-requisites in mathematics and computer science; further, open seats in classes offered in that program are limited. Department of Politics, Faculty of Arts and Science: POL-GA 3200: Topics in Network Analysis Department of Psychology, Faculty of Arts and Science: PSYCH-GA 2247: Structural Equation Methods PSYCH-GA 2248: Methods for the Analysis of Change Department of Sociology, Faculty of Arts and Science: SOC-GA 2314: Longitudinal Statistics SOC-GA 2306: Event History Analysis Department of Economics, Stern School of Business: ECON-GB 3351: Econometrics I ECON-GB 9912: Econometric Analysis of Panel Data Program of Statistics, Stern School of Business: STAT-GB 2301: Regression and Multivariate Data Analysis STAT-GB 2302: Forecasting Time Series Data STAT-GB 2308: Applied Stochastic Processes For Financial Models STAT-GB 4310.01: Statistics for Social Data Robert F. Wagner Graduate School of Public Service: PADM-GP.2875: Estimating Impacts in Policy Research PADM-GP.2171: Program Analysis and Evaluation Unrestricted Electives: 6-8 points The two unrestricted electives may be taken from departments across the entire university according to your own interests. We recommend EDPLY-GE 2030 Education and Social Policy. Unrestricted Electives Points Elective 1 3-4 Elective 2 3-4 This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}]}][{"detail": [{"content": "Interdepartmental Research Studies/Applied Statistics Courses 2015-2016 - Interdepartmental Research Studies - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Interdepartmental Research Studies Information News Course Descriptions Related Courses FAQ Interdepartmental Research Studies/Applied Statistics Courses 2015-2016 Fall 2015 | January Intersession 2016 | Spring 2016 | Summer 2016 | CLASS ARCHIVE Please click on the course titles below for sample syllabi. The syllabi are copyrighted material and are intended for informational use only. The instructor listed on the syllabus may differ from those currently planning to teach the course. Fall 2015 Course # Call # Course Title Days / Times Pts Instructor APSTA-UE 1085-001 Basic Statistics I (undergraduate) Registration for one lab section is required MW 3:30-4:45PM Lab: R 3:30-4:45PM; F 11:00AM-12:15PM 4 R. Hassad APSTA -GE 2001-001 3274 Statistics for the Behavioral and Social Sciences I W 2:00-4:45PM 3 S. Weinberg APSTA -GE 2001-002 3275 Statistics for the Behavioral and Social Sciences I M 4:45-7:35PM 3 S. Corcoran APSTA -GE 2003-001 req'd lab-002 3276, 3277 Intermediate Quantitative Methods: The General Linear Model T 3:30-6:10PM Lab R 3:30-4:45PM 3 S. Weinberg; APSTA -GE 2012 3278 Adv Topics in Quant Meths: Causal Inference: Statistical Methods for Program Evaluation and Policy Research F 9:30AM - 12:30PM 3 J. Hill APSTA -GE 2015 17387 Topics in Advanced Quantitative Methods: Applied Spatial Statistics T 3.30 PM - 6.45 PM 2 (09/08/2015 - 10/27/2015) T. Mijanovich APSTA -GE 2016 4627 Adv Topics in Quant Meths: Factor Scoring and Practical Issues in Scaling M 2.00 PM - 4.45 PM 2 (09/14/2015 - 10/19/2015) P. Halpin APSTA -GE 2040 6162 Multilevel Models: growth curves Not offered 2015-16 See APSTA-GE 2042 Winter 2016 APSTA -GE 2041 6163 Practicum in Multi-Level Models Growth Curves *to be taken concurrently with APSTA -GE.2040 See Winter 2016 APSTA -GE 2085 3281 Basic Statistics I (masters) M 6:20-9:00PM 3 D. Chan APSTA -GE 2086 3282 Basic Statistics II T 6:20-9:00PM 3 D. Chan RESCH-GE 2132 3283 Principles of Empirical Research T 2:00-3:40PM 3 C. Voulgarides RESCH-GE 2138 4462 Writing Empirical Research T 4:55-6:35PM 3 Norman RESCH-GE 2139 4129 Survey Research Methods R 4:55-7:35PM 3 D. Harel RESCH-GE 2140 3284/3285 Approaches to Qualitative Inquiry T 2:00-4:55PM / R 4:55-6:35PM 3 Norman / Larson RESCH-GE 2143 3286 Participatory Action Research R 6:45-8:25PM 3 Middleton APSTA -GE 2300 4836-4841 Independent Study 1-6 M. Scott; J.Hill, S. Weinberg; Y. Lu; P. Halpin; D. Harel APSTA-GE 2401 17388 Statistical Consulting Research Seminar W 12.30 PM - 2.30 PM 1-3 J. Hill & Y. Lu APSTA -GE 2995-001 3294, 4464 Biostatistics I (required lab) T 4:55-6:35PM T 6:45-8:00PM 3 Y. Lu January Intersession 2016 (students register for this course as part of Spring 2016) Course # Course Title Days / Times Pts Instructor APSTA-GE 2042 Multilevel Nested Data Models TBD 2 M. Scott APSTA-GE 2041 Practicum in Multi-Level Models TBD 1 M. Scott Spring 2016 Course # Course Title Days / Times Pts Instructor APSTA-UE 0010 Statistical Mysteries and How to Solve Them TR 2:00-3:15pm Lab T 3:30-4:30pm 4 J. Hill / D. Harel APSTA-UE 1085-001 Basic Statistics I (undergraduate) Registration for one lab section is required MW 3:30-4:45PM Lab: R TBD; F 11:00AM-12:15PM; F 12:30-1:45PM 4 R. Hassad APSTA -GE 2002 Statistics for the Behavioral and Social Sciences II W 2:00-4:30PM 3 S. Weinberg APSTA -GE 2004 req'd lab Advanced Modeling I: Topics in Multivariate Analysis T 3:30-6:10PM Lab T 6:20-7:30PM (first 7 weeks of term) 2 S. Weinberg APSTA-GE 2013 Advanced Topics in Quantiative Methods: Missing Data F 9:30-12:15 (SECOND 7 weeks of term) 2 J. Hill APSTA-GE 2017 Advanced Topics in Quantitative Methods: Educational Data Science Practicum 12:45-3:25pm (SECOND 7 weeks of term) 2 Y. Bergner APSTA -GE 2042 Advanced Topics in Quantiative Methods:Nested-Data Models Offered Winter 2016 2 M. Scott APSTA -GE 2085 Basic Statistics I (masters) M 6:20-9:00PM 3 D. Chan APSTA -GE 2094 Factor Analysis and Structural Equation Modeling R 2:00-4:45PM 3 P. Halpin APSTA -GE 2110 Applied Statistics: Using Large Databases in Education Research W 4:55-7:35PM 4 S. Corcoran / K. Ziol-Guest APSTA-GE 2122 Applied Statistical Modeling and Inference Lec M 930-1130am Lab T 710-8pm 3 D. Harel / Y. Lu APSTA-GE 2300 Independent Study TBA 1-6 Staff APSTA -GE 2996-001 Biostatistics II T 4:55-6:35PM Lab: T 6:45-8:25PM 3 T. Mijanovich APSTA -GE 2997 Generalized Linear and Multilevel Growth Curve Models Lect F 9:30AM - 11:00PM Lab 11:10-12:30pm (meets FIRST 7 weeks of semester) 2 Y. Lu Summer 2016 Course # Course Title Days / Times Pts Instructor APSTA-GE 2300 IND Study TBA 1-6 S. Weinberg This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}]},
{"detail": [{"content": "Interdepartmental Research Studies/Applied Statistics Courses 2015-2016 - Interdepartmental Research Studies - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Interdepartmental Research Studies Information News Course Descriptions Related Courses FAQ Interdepartmental Research Studies/Applied Statistics Courses 2015-2016 Fall 2015 | January Intersession 2016 | Spring 2016 | Summer 2016 | CLASS ARCHIVE Please click on the course titles below for sample syllabi. The syllabi are copyrighted material and are intended for informational use only. The instructor listed on the syllabus may differ from those currently planning to teach the course. Fall 2015 Course # Call # Course Title Days / Times Pts Instructor APSTA-UE 1085-001 Basic Statistics I (undergraduate) Registration for one lab section is required MW 3:30-4:45PM Lab: R 3:30-4:45PM; F 11:00AM-12:15PM 4 R. Hassad APSTA -GE 2001-001 3274 Statistics for the Behavioral and Social Sciences I W 2:00-4:45PM 3 S. Weinberg APSTA -GE 2001-002 3275 Statistics for the Behavioral and Social Sciences I M 4:45-7:35PM 3 S. Corcoran APSTA -GE 2003-001 req'd lab-002 3276, 3277 Intermediate Quantitative Methods: The General Linear Model T 3:30-6:10PM Lab R 3:30-4:45PM 3 S. Weinberg; APSTA -GE 2012 3278 Adv Topics in Quant Meths: Causal Inference: Statistical Methods for Program Evaluation and Policy Research F 9:30AM - 12:30PM 3 J. Hill APSTA -GE 2015 17387 Topics in Advanced Quantitative Methods: Applied Spatial Statistics T 3.30 PM - 6.45 PM 2 (09/08/2015 - 10/27/2015) T. Mijanovich APSTA -GE 2016 4627 Adv Topics in Quant Meths: Factor Scoring and Practical Issues in Scaling M 2.00 PM - 4.45 PM 2 (09/14/2015 - 10/19/2015) P. Halpin APSTA -GE 2040 6162 Multilevel Models: growth curves Not offered 2015-16 See APSTA-GE 2042 Winter 2016 APSTA -GE 2041 6163 Practicum in Multi-Level Models Growth Curves *to be taken concurrently with APSTA -GE.2040 See Winter 2016 APSTA -GE 2085 3281 Basic Statistics I (masters) M 6:20-9:00PM 3 D. Chan APSTA -GE 2086 3282 Basic Statistics II T 6:20-9:00PM 3 D. Chan RESCH-GE 2132 3283 Principles of Empirical Research T 2:00-3:40PM 3 C. Voulgarides RESCH-GE 2138 4462 Writing Empirical Research T 4:55-6:35PM 3 Norman RESCH-GE 2139 4129 Survey Research Methods R 4:55-7:35PM 3 D. Harel RESCH-GE 2140 3284/3285 Approaches to Qualitative Inquiry T 2:00-4:55PM / R 4:55-6:35PM 3 Norman / Larson RESCH-GE 2143 3286 Participatory Action Research R 6:45-8:25PM 3 Middleton APSTA -GE 2300 4836-4841 Independent Study 1-6 M. Scott; J.Hill, S. Weinberg; Y. Lu; P. Halpin; D. Harel APSTA-GE 2401 17388 Statistical Consulting Research Seminar W 12.30 PM - 2.30 PM 1-3 J. Hill & Y. Lu APSTA -GE 2995-001 3294, 4464 Biostatistics I (required lab) T 4:55-6:35PM T 6:45-8:00PM 3 Y. Lu January Intersession 2016 (students register for this course as part of Spring 2016) Course # Course Title Days / Times Pts Instructor APSTA-GE 2042 Multilevel Nested Data Models TBD 2 M. Scott APSTA-GE 2041 Practicum in Multi-Level Models TBD 1 M. Scott Spring 2016 Course # Course Title Days / Times Pts Instructor APSTA-UE 0010 Statistical Mysteries and How to Solve Them TR 2:00-3:15pm Lab T 3:30-4:30pm 4 J. Hill / D. Harel APSTA-UE 1085-001 Basic Statistics I (undergraduate) Registration for one lab section is required MW 3:30-4:45PM Lab: R TBD; F 11:00AM-12:15PM; F 12:30-1:45PM 4 R. Hassad APSTA -GE 2002 Statistics for the Behavioral and Social Sciences II W 2:00-4:30PM 3 S. Weinberg APSTA -GE 2004 req'd lab Advanced Modeling I: Topics in Multivariate Analysis T 3:30-6:10PM Lab T 6:20-7:30PM (first 7 weeks of term) 2 S. Weinberg APSTA-GE 2013 Advanced Topics in Quantiative Methods: Missing Data F 9:30-12:15 (SECOND 7 weeks of term) 2 J. Hill APSTA-GE 2017 Advanced Topics in Quantitative Methods: Educational Data Science Practicum 12:45-3:25pm (SECOND 7 weeks of term) 2 Y. Bergner APSTA -GE 2042 Advanced Topics in Quantiative Methods:Nested-Data Models Offered Winter 2016 2 M. Scott APSTA -GE 2085 Basic Statistics I (masters) M 6:20-9:00PM 3 D. Chan APSTA -GE 2094 Factor Analysis and Structural Equation Modeling R 2:00-4:45PM 3 P. Halpin APSTA -GE 2110 Applied Statistics: Using Large Databases in Education Research W 4:55-7:35PM 4 S. Corcoran / K. Ziol-Guest APSTA-GE 2122 Applied Statistical Modeling and Inference Lec M 930-1130am Lab T 710-8pm 3 D. Harel / Y. Lu APSTA-GE 2300 Independent Study TBA 1-6 Staff APSTA -GE 2996-001 Biostatistics II T 4:55-6:35PM Lab: T 6:45-8:25PM 3 T. Mijanovich APSTA -GE 2997 Generalized Linear and Multilevel Growth Curve Models Lect F 9:30AM - 11:00PM Lab 11:10-12:30pm (meets FIRST 7 weeks of semester) 2 Y. Lu Summer 2016 Course # Course Title Days / Times Pts Instructor APSTA-GE 2300 IND Study TBA 1-6 S. Weinberg This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}, {"content": "Course Descriptions - Applied Statistics - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Applied Statistics Master of Science Program Overview Careers Degree Requirements Sample Schedules Course Descriptions 2015-2016 Courses FAQ Admissions Financial Aid People Events Contact Us Center for the Promotion of Research Involving Innovative Statistical Methodology (PRIISM) Course Descriptions Links to syllabi for courses offered 2014-15 are given here . Links to syllabi for the prior year are given here . APSTA-GE.2003: Intermediate Quantitative Methods: The General Linear Model This course follows a first year statistics class (pre-requisite for matriculation in the program) by examining more deeply multiple regression/correlation as a general and flexible system for analyzing data in the behavioral, social, and health sciences. In addition to covering more advanced topics related to traditional multiple regression/correlation, the course examines ANOVA, ANCOVA, and Path Analysis as special cases of this general linear model. A statistical software package is used to give students hands-on experience with topics covered. STAT-GB 2301: Regression and Multivariate Data Analysis (through Stern) This is a data-driven, applied statistics course focusing on the analysis of data using regression models. It emphasizes applications to the analysis of business and other data and makes extensive use of computer statistical packages. Topics include simple and multiple linear regression, residual analysis and other regression diagnostics, multicollinearity and model selection, autoregression, heteroscedasticity, regression models using categorical predictors, and logistic regression. All topics are illustrated on real data sets obtained from financial markets, market research studies, and other scientific inquiries. The goal of the class is that students begin to develop the skills to be able to collect, organize, analyze, and interpret regression data. APSTA-GE.2004: Advanced Modeling I: Topics in Multivariate Analysis This course extends the material covered in Intermediate Statistics by examining some of the more advanced topics in multivariate data analysis for the behavioral, social, and health sciences that are prerequisite to continuing the study of quantitative methods at NYU. The topics to be covered are logistic regression, multivariate analysis of variance, repeated measures analysis of variance, and an introduction to hierarchical linear modeling. A software package is used to give students hands-on experience with topics covered. In so doing, the course provides foundational skills and knowledge critical to those graduate students whose research relies on the analysis of quantitative data. APSTA-GE.2011: Advanced Topics in Quantitative Methods: Classification and Clustering Classification and clustering are important statistical techniques commonly applied in many social and behavioral science research problems. Both seek to understand social phenomena through the identification of naturally occurring homogeneous groupings within a population. Classification techniques are used to sort new observations into pre-existing or known groupings, while clustering techniques sort the population under study into groupings based on their observed characteristics. Both help to reveal hidden structure that may be used in further analyses. This course will compare and contrast these techniques, including many of their variations, with an emphasis on applications. APSTA-GE.2012: Advanced Topics in Quantitative Methods: Causal Inference: Statistical Methods for Program Evaluation and Policy Research The course provides students with a basic knowledge of both how to perform analyses and critique the use of some more advanced statistical methods useful in answering policy questions. While randomized experiments will be discussed, the primary focus will be the challenge of answering causal questions using data that do not meet such standards. Several approaches for observational data including propensity score methods, instrumental variables, difference in differences, fixed effects models and regression discontinuity designs will be discussed. APSTA-GE.2013: Advanced Topics in Quantitative Methods: Missing Data This course provides students with a basic knowledge of the potential implications of missing data on their data analyses as well as potential solutions. We will begin by discussing different types of mechanisms that can generate missing data. This will lay the groundwork for discussions of what types of missing data scenarios can be accommodated by each missing data method discussed subsequently. Simple missing data fixes will be described next as well as the problems they can create in terms of bias and loss of efficiency. We next explore some slightly more complicated fixes and the assumptions required for valid inference for each. The course will end with a focus on multiple imputation including discussions of the general framework, different models and algorithms and the basic theory. APSTA-GE.2015: Advanced Topics in Quantitative Methods: Applied Spatial Statistics Spatial data arise when information is collected on units that reside in different locations. Common examples include geology, criminology and epidemiology, where the goal may be to identify patterning or clusters (‘hot spots’) in the outcomes across the terrain being examined. In the social sciences, a similar set of questions and techniques are required, for example in studies of homelessness, poverty, environmental justice, and education. However, spatial data present a novel set of exploratory and modeling challenges, given the unique way in which outcomes are related (correlated) with each other through proximity. This course is an overview of the methods needed to analyze data for which it is suspected that the spatial component plays an important role. APSTA-GE.2016: Advanced Topics in Quantitative Methods: Factor Scoring and Practical Issues in Scaling This course reviews and expands on the topics of measurement and reliability. The general topic is factor scoring, with a focus on how to implement, evaluate, and interpret different methods of scoring in the multidimensional factor model. Students will also learn how to conduct exploratory factor analysis using rigorous methods for assessing goodness of fit and dimensionality. The course is especially well suited to students who have collected test/questionnaire data and require a method for getting scores from those data, scores which can then be used in further analyses. APSTA-GE.2040: Multilevel Models: Growth Curves This is a course on models for multilevel growth curve data. These data arise in longitudinal designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. In this module, we will develop and fit a set of models for longitudinal. The course assignments will use state of the art statistical software to explore, fit and interpret the models. APSTA-GE.2041: Practicum in Multi-Level Models This is practicum course on models for multilevel growth curve data. This course is a natural sequel to Multi-Level Modeling: Growth Curves. Building on the theory and examples developed in that course, students will participate in a guided, larger research project that employs multi-level growth curve models. Students will meet in groups with the instructor in a lab setting to fit, evaluate and describe these models. The final project for the course will consist of a “results and discussion” section, journal article quality write-up. APSTA-GE.2042: Multilevel Models: Nested Data Models This is a course on models for multilevel nested data. These data arise in nested designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. The course assignments will use state of the art statistical software to explore, fit and interpret the models. APSTA-GE.2094: Factor Analysis and Structural Equation Modeling Course provides students with the software skills and theoretical knowledge required to apply structural equation modeling (SEM). First, we review of multiple regression and basic concepts from matrix algebra. Next, path analysis and factor analysis are developed, leading to more advanced topics, including how to translate theory into models, strategies for dealing with poor fitting specifications, categorical data, and issues in multigroup analysis. The latter component focuses on data applications of SEM, including a final project. Example material will be drawn from education and psychology. APSTA-GE.2110: Applied Statistics: Using Large Databases in Education Research This course is designed to serve as a bridge between more theoretical coursework in applied statistics (introductory course in statistics and linear models) and practical work with real, large-scale data. Although the focus is mainly on datasets relevant to education and educational policy research, the skills taught in the course are broadly transferrable across subject areas in social, behavioral, and health sciences. At the conclusion of this course students will be prepared to produce descriptive statistics about a population using data collected under complex survey design and to estimate simple cross-sectional and longitudinal regression models of the sort frequently employed in real applied data analysis. RESCH-GE.2134: Experimental and Quasi-Experimental Design This course covers issues related to the design of research studies. Topics include observational, quasi-experimental, and experimental studies. The course focuses on practical aspects of design, linking these to appropriate analyses. RESCH-GE.2139: Survey Research I This course provides a broad overview of the many aspects of survey research methodology including sampling, instrument design, the psychology of survey response, field testing, survey operations, nonresponse bias analysis and correction, and primary and secondary analysis of survey data. The course is designed primarily for those who intend to use surveys in their own research – whether designing original surveys or performing secondary analysis on survey data collected by others. Whenever possible, we will use examples and data from real surveys employed by academic researchers, professional survey firms, and Federal statistical agencies. SOC-GA 2306 Event History Analysis (through FAS/Sociology) This course surveys methods for analyzing event history data, with a focus on continuous-time models and estimation techniques. Topics include the exploratory analysis of event history data, nonparametric methods, right censoring, maximum likelihood estimation, alternative specifications for a time dependent baseline hazard rate, observed and unobserved heterogeneity, time-varying covariates, proportional and nonproportional models, multiple transition and competing risk models, left truncation and left censoring, and analogs of recursive and nonrecursive models. Major emphasis is placed on the logic, practical use, and estimation of models. APSTA-GE.2401: Statistical Consulting Research Seminar This course is designed to assist graduate students in the quantitative methods specific to the design and analysis of their theses. In this seminar format, under the guidance of one or more statistical faculty members, students will have the opportunity to present and defend their scholarly work-in-progress. They will also be required to critique and provide constructive suggestions for their fellow students. The focus of critiques will be on the research methodology and other statistical issues. Students will additionally benefit from being able to observe how the participating faculty diagnose and solve statistical issues that arise in others’ presented work and to benefit from this advice in their own work. In essence this course provides training in statistical consulting along with detailed feedback on one’s dissertation research. APSTA-GE.2997: Advanced Methodology in Health and Policy Research: Generalized Linear Models This course is a second year course in advanced statistical techniques that covers useful quantitative tools in health and policy research. Assuming a strong foundation in regression and the general linear model, this course focuses on data analysis that utilizes models for categorical, discrete or limited outcomes that are commonly seen in health and policy studies. Examples are drawn from broad areas of health and policy research including determinants of self-reported health status or factors influencing number of clinic visits. In this course students will also learn the principles of likelihood-based inference, which will assist them in some of the more advanced statistics courses. APSTA-GE.2998: Advanced Methodology in Health and Policy Research: Survival Analysis This course is a second year course in advanced statistical techniques that introduces basic concepts and methods for analyzing survival time data obtained by tracking individuals until occurrence of an event or their loss to follow-up (e.g., time to death or degree). Assuming a strong foundation in regression and the inferential statistics, this course will cover nonparametric, semiparametric and parametric survival models that are commonly seen in health and policy studies. Students will also learn discrete time event history models, which are useful tools in longitudinal data analysis. Examples in this course are drawn from broad areas in health and policy research including, for example: assessing risk factors for time to death among heart transplant patients and assessing risk factors for juvenile delinquency. Social Research Foundation RESCH-GE.2132: Principles of Empirical Research This course introduces advanced study of social science research methods. The course examines: 1), the relationship between theory and research; 2), methodological issues, such as objectivity, the logic of argument, reliability and validity; and 3), exemplars of various methodological techniques, including survey, interview, experimental, archival and ethnographic research. The course is designed to train social scientists to both recognize and be able to conduct rigorous, theoretically informed research. Additional Courses APSTA-GE.2300. Internship Course In the internship, students will gain experience working with “real world” data, working with an approved faculty member, local firm or organization. Students will receive practical training focused on the kinds of issues that researchers face in collecting and analyzing data. This course will not only enhance the tools and techniques students develop, but will also possibly lead to employment opportunities after graduation.   This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}]},
{"detail": [{"content": "Interdepartmental Research Studies/Applied Statistics Courses 2015-2016 - Interdepartmental Research Studies - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Interdepartmental Research Studies Information News Course Descriptions Related Courses FAQ Interdepartmental Research Studies/Applied Statistics Courses 2015-2016 Fall 2015 | January Intersession 2016 | Spring 2016 | Summer 2016 | CLASS ARCHIVE Please click on the course titles below for sample syllabi. The syllabi are copyrighted material and are intended for informational use only. The instructor listed on the syllabus may differ from those currently planning to teach the course. Fall 2015 Course # Call # Course Title Days / Times Pts Instructor APSTA-UE 1085-001 Basic Statistics I (undergraduate) Registration for one lab section is required MW 3:30-4:45PM Lab: R 3:30-4:45PM; F 11:00AM-12:15PM 4 R. Hassad APSTA -GE 2001-001 3274 Statistics for the Behavioral and Social Sciences I W 2:00-4:45PM 3 S. Weinberg APSTA -GE 2001-002 3275 Statistics for the Behavioral and Social Sciences I M 4:45-7:35PM 3 S. Corcoran APSTA -GE 2003-001 req'd lab-002 3276, 3277 Intermediate Quantitative Methods: The General Linear Model T 3:30-6:10PM Lab R 3:30-4:45PM 3 S. Weinberg; APSTA -GE 2012 3278 Adv Topics in Quant Meths: Causal Inference: Statistical Methods for Program Evaluation and Policy Research F 9:30AM - 12:30PM 3 J. Hill APSTA -GE 2015 17387 Topics in Advanced Quantitative Methods: Applied Spatial Statistics T 3.30 PM - 6.45 PM 2 (09/08/2015 - 10/27/2015) T. Mijanovich APSTA -GE 2016 4627 Adv Topics in Quant Meths: Factor Scoring and Practical Issues in Scaling M 2.00 PM - 4.45 PM 2 (09/14/2015 - 10/19/2015) P. Halpin APSTA -GE 2040 6162 Multilevel Models: growth curves Not offered 2015-16 See APSTA-GE 2042 Winter 2016 APSTA -GE 2041 6163 Practicum in Multi-Level Models Growth Curves *to be taken concurrently with APSTA -GE.2040 See Winter 2016 APSTA -GE 2085 3281 Basic Statistics I (masters) M 6:20-9:00PM 3 D. Chan APSTA -GE 2086 3282 Basic Statistics II T 6:20-9:00PM 3 D. Chan RESCH-GE 2132 3283 Principles of Empirical Research T 2:00-3:40PM 3 C. Voulgarides RESCH-GE 2138 4462 Writing Empirical Research T 4:55-6:35PM 3 Norman RESCH-GE 2139 4129 Survey Research Methods R 4:55-7:35PM 3 D. Harel RESCH-GE 2140 3284/3285 Approaches to Qualitative Inquiry T 2:00-4:55PM / R 4:55-6:35PM 3 Norman / Larson RESCH-GE 2143 3286 Participatory Action Research R 6:45-8:25PM 3 Middleton APSTA -GE 2300 4836-4841 Independent Study 1-6 M. Scott; J.Hill, S. Weinberg; Y. Lu; P. Halpin; D. Harel APSTA-GE 2401 17388 Statistical Consulting Research Seminar W 12.30 PM - 2.30 PM 1-3 J. Hill & Y. Lu APSTA -GE 2995-001 3294, 4464 Biostatistics I (required lab) T 4:55-6:35PM T 6:45-8:00PM 3 Y. Lu January Intersession 2016 (students register for this course as part of Spring 2016) Course # Course Title Days / Times Pts Instructor APSTA-GE 2042 Multilevel Nested Data Models TBD 2 M. Scott APSTA-GE 2041 Practicum in Multi-Level Models TBD 1 M. Scott Spring 2016 Course # Course Title Days / Times Pts Instructor APSTA-UE 0010 Statistical Mysteries and How to Solve Them TR 2:00-3:15pm Lab T 3:30-4:30pm 4 J. Hill / D. Harel APSTA-UE 1085-001 Basic Statistics I (undergraduate) Registration for one lab section is required MW 3:30-4:45PM Lab: R TBD; F 11:00AM-12:15PM; F 12:30-1:45PM 4 R. Hassad APSTA -GE 2002 Statistics for the Behavioral and Social Sciences II W 2:00-4:30PM 3 S. Weinberg APSTA -GE 2004 req'd lab Advanced Modeling I: Topics in Multivariate Analysis T 3:30-6:10PM Lab T 6:20-7:30PM (first 7 weeks of term) 2 S. Weinberg APSTA-GE 2013 Advanced Topics in Quantiative Methods: Missing Data F 9:30-12:15 (SECOND 7 weeks of term) 2 J. Hill APSTA-GE 2017 Advanced Topics in Quantitative Methods: Educational Data Science Practicum 12:45-3:25pm (SECOND 7 weeks of term) 2 Y. Bergner APSTA -GE 2042 Advanced Topics in Quantiative Methods:Nested-Data Models Offered Winter 2016 2 M. Scott APSTA -GE 2085 Basic Statistics I (masters) M 6:20-9:00PM 3 D. Chan APSTA -GE 2094 Factor Analysis and Structural Equation Modeling R 2:00-4:45PM 3 P. Halpin APSTA -GE 2110 Applied Statistics: Using Large Databases in Education Research W 4:55-7:35PM 4 S. Corcoran / K. Ziol-Guest APSTA-GE 2122 Applied Statistical Modeling and Inference Lec M 930-1130am Lab T 710-8pm 3 D. Harel / Y. Lu APSTA-GE 2300 Independent Study TBA 1-6 Staff APSTA -GE 2996-001 Biostatistics II T 4:55-6:35PM Lab: T 6:45-8:25PM 3 T. Mijanovich APSTA -GE 2997 Generalized Linear and Multilevel Growth Curve Models Lect F 9:30AM - 11:00PM Lab 11:10-12:30pm (meets FIRST 7 weeks of semester) 2 Y. Lu Summer 2016 Course # Course Title Days / Times Pts Instructor APSTA-GE 2300 IND Study TBA 1-6 S. Weinberg This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}, {"content": "Course Descriptions - Applied Statistics - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Applied Statistics Master of Science Program Overview Careers Degree Requirements Sample Schedules Course Descriptions 2015-2016 Courses FAQ Admissions Financial Aid People Events Contact Us Center for the Promotion of Research Involving Innovative Statistical Methodology (PRIISM) Course Descriptions Links to syllabi for courses offered 2014-15 are given here . Links to syllabi for the prior year are given here . APSTA-GE.2003: Intermediate Quantitative Methods: The General Linear Model This course follows a first year statistics class (pre-requisite for matriculation in the program) by examining more deeply multiple regression/correlation as a general and flexible system for analyzing data in the behavioral, social, and health sciences. In addition to covering more advanced topics related to traditional multiple regression/correlation, the course examines ANOVA, ANCOVA, and Path Analysis as special cases of this general linear model. A statistical software package is used to give students hands-on experience with topics covered. STAT-GB 2301: Regression and Multivariate Data Analysis (through Stern) This is a data-driven, applied statistics course focusing on the analysis of data using regression models. It emphasizes applications to the analysis of business and other data and makes extensive use of computer statistical packages. Topics include simple and multiple linear regression, residual analysis and other regression diagnostics, multicollinearity and model selection, autoregression, heteroscedasticity, regression models using categorical predictors, and logistic regression. All topics are illustrated on real data sets obtained from financial markets, market research studies, and other scientific inquiries. The goal of the class is that students begin to develop the skills to be able to collect, organize, analyze, and interpret regression data. APSTA-GE.2004: Advanced Modeling I: Topics in Multivariate Analysis This course extends the material covered in Intermediate Statistics by examining some of the more advanced topics in multivariate data analysis for the behavioral, social, and health sciences that are prerequisite to continuing the study of quantitative methods at NYU. The topics to be covered are logistic regression, multivariate analysis of variance, repeated measures analysis of variance, and an introduction to hierarchical linear modeling. A software package is used to give students hands-on experience with topics covered. In so doing, the course provides foundational skills and knowledge critical to those graduate students whose research relies on the analysis of quantitative data. APSTA-GE.2011: Advanced Topics in Quantitative Methods: Classification and Clustering Classification and clustering are important statistical techniques commonly applied in many social and behavioral science research problems. Both seek to understand social phenomena through the identification of naturally occurring homogeneous groupings within a population. Classification techniques are used to sort new observations into pre-existing or known groupings, while clustering techniques sort the population under study into groupings based on their observed characteristics. Both help to reveal hidden structure that may be used in further analyses. This course will compare and contrast these techniques, including many of their variations, with an emphasis on applications. APSTA-GE.2012: Advanced Topics in Quantitative Methods: Causal Inference: Statistical Methods for Program Evaluation and Policy Research The course provides students with a basic knowledge of both how to perform analyses and critique the use of some more advanced statistical methods useful in answering policy questions. While randomized experiments will be discussed, the primary focus will be the challenge of answering causal questions using data that do not meet such standards. Several approaches for observational data including propensity score methods, instrumental variables, difference in differences, fixed effects models and regression discontinuity designs will be discussed. APSTA-GE.2013: Advanced Topics in Quantitative Methods: Missing Data This course provides students with a basic knowledge of the potential implications of missing data on their data analyses as well as potential solutions. We will begin by discussing different types of mechanisms that can generate missing data. This will lay the groundwork for discussions of what types of missing data scenarios can be accommodated by each missing data method discussed subsequently. Simple missing data fixes will be described next as well as the problems they can create in terms of bias and loss of efficiency. We next explore some slightly more complicated fixes and the assumptions required for valid inference for each. The course will end with a focus on multiple imputation including discussions of the general framework, different models and algorithms and the basic theory. APSTA-GE.2015: Advanced Topics in Quantitative Methods: Applied Spatial Statistics Spatial data arise when information is collected on units that reside in different locations. Common examples include geology, criminology and epidemiology, where the goal may be to identify patterning or clusters (‘hot spots’) in the outcomes across the terrain being examined. In the social sciences, a similar set of questions and techniques are required, for example in studies of homelessness, poverty, environmental justice, and education. However, spatial data present a novel set of exploratory and modeling challenges, given the unique way in which outcomes are related (correlated) with each other through proximity. This course is an overview of the methods needed to analyze data for which it is suspected that the spatial component plays an important role. APSTA-GE.2016: Advanced Topics in Quantitative Methods: Factor Scoring and Practical Issues in Scaling This course reviews and expands on the topics of measurement and reliability. The general topic is factor scoring, with a focus on how to implement, evaluate, and interpret different methods of scoring in the multidimensional factor model. Students will also learn how to conduct exploratory factor analysis using rigorous methods for assessing goodness of fit and dimensionality. The course is especially well suited to students who have collected test/questionnaire data and require a method for getting scores from those data, scores which can then be used in further analyses. APSTA-GE.2040: Multilevel Models: Growth Curves This is a course on models for multilevel growth curve data. These data arise in longitudinal designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. In this module, we will develop and fit a set of models for longitudinal. The course assignments will use state of the art statistical software to explore, fit and interpret the models. APSTA-GE.2041: Practicum in Multi-Level Models This is practicum course on models for multilevel growth curve data. This course is a natural sequel to Multi-Level Modeling: Growth Curves. Building on the theory and examples developed in that course, students will participate in a guided, larger research project that employs multi-level growth curve models. Students will meet in groups with the instructor in a lab setting to fit, evaluate and describe these models. The final project for the course will consist of a “results and discussion” section, journal article quality write-up. APSTA-GE.2042: Multilevel Models: Nested Data Models This is a course on models for multilevel nested data. These data arise in nested designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the model itself. Moreover, these more sophisticated techniques allow the researcher to learn new and important characteristics of the social and behavioral processes under study. The course assignments will use state of the art statistical software to explore, fit and interpret the models. APSTA-GE.2094: Factor Analysis and Structural Equation Modeling Course provides students with the software skills and theoretical knowledge required to apply structural equation modeling (SEM). First, we review of multiple regression and basic concepts from matrix algebra. Next, path analysis and factor analysis are developed, leading to more advanced topics, including how to translate theory into models, strategies for dealing with poor fitting specifications, categorical data, and issues in multigroup analysis. The latter component focuses on data applications of SEM, including a final project. Example material will be drawn from education and psychology. APSTA-GE.2110: Applied Statistics: Using Large Databases in Education Research This course is designed to serve as a bridge between more theoretical coursework in applied statistics (introductory course in statistics and linear models) and practical work with real, large-scale data. Although the focus is mainly on datasets relevant to education and educational policy research, the skills taught in the course are broadly transferrable across subject areas in social, behavioral, and health sciences. At the conclusion of this course students will be prepared to produce descriptive statistics about a population using data collected under complex survey design and to estimate simple cross-sectional and longitudinal regression models of the sort frequently employed in real applied data analysis. RESCH-GE.2134: Experimental and Quasi-Experimental Design This course covers issues related to the design of research studies. Topics include observational, quasi-experimental, and experimental studies. The course focuses on practical aspects of design, linking these to appropriate analyses. RESCH-GE.2139: Survey Research I This course provides a broad overview of the many aspects of survey research methodology including sampling, instrument design, the psychology of survey response, field testing, survey operations, nonresponse bias analysis and correction, and primary and secondary analysis of survey data. The course is designed primarily for those who intend to use surveys in their own research – whether designing original surveys or performing secondary analysis on survey data collected by others. Whenever possible, we will use examples and data from real surveys employed by academic researchers, professional survey firms, and Federal statistical agencies. SOC-GA 2306 Event History Analysis (through FAS/Sociology) This course surveys methods for analyzing event history data, with a focus on continuous-time models and estimation techniques. Topics include the exploratory analysis of event history data, nonparametric methods, right censoring, maximum likelihood estimation, alternative specifications for a time dependent baseline hazard rate, observed and unobserved heterogeneity, time-varying covariates, proportional and nonproportional models, multiple transition and competing risk models, left truncation and left censoring, and analogs of recursive and nonrecursive models. Major emphasis is placed on the logic, practical use, and estimation of models. APSTA-GE.2401: Statistical Consulting Research Seminar This course is designed to assist graduate students in the quantitative methods specific to the design and analysis of their theses. In this seminar format, under the guidance of one or more statistical faculty members, students will have the opportunity to present and defend their scholarly work-in-progress. They will also be required to critique and provide constructive suggestions for their fellow students. The focus of critiques will be on the research methodology and other statistical issues. Students will additionally benefit from being able to observe how the participating faculty diagnose and solve statistical issues that arise in others’ presented work and to benefit from this advice in their own work. In essence this course provides training in statistical consulting along with detailed feedback on one’s dissertation research. APSTA-GE.2997: Advanced Methodology in Health and Policy Research: Generalized Linear Models This course is a second year course in advanced statistical techniques that covers useful quantitative tools in health and policy research. Assuming a strong foundation in regression and the general linear model, this course focuses on data analysis that utilizes models for categorical, discrete or limited outcomes that are commonly seen in health and policy studies. Examples are drawn from broad areas of health and policy research including determinants of self-reported health status or factors influencing number of clinic visits. In this course students will also learn the principles of likelihood-based inference, which will assist them in some of the more advanced statistics courses. APSTA-GE.2998: Advanced Methodology in Health and Policy Research: Survival Analysis This course is a second year course in advanced statistical techniques that introduces basic concepts and methods for analyzing survival time data obtained by tracking individuals until occurrence of an event or their loss to follow-up (e.g., time to death or degree). Assuming a strong foundation in regression and the inferential statistics, this course will cover nonparametric, semiparametric and parametric survival models that are commonly seen in health and policy studies. Students will also learn discrete time event history models, which are useful tools in longitudinal data analysis. Examples in this course are drawn from broad areas in health and policy research including, for example: assessing risk factors for time to death among heart transplant patients and assessing risk factors for juvenile delinquency. Social Research Foundation RESCH-GE.2132: Principles of Empirical Research This course introduces advanced study of social science research methods. The course examines: 1), the relationship between theory and research; 2), methodological issues, such as objectivity, the logic of argument, reliability and validity; and 3), exemplars of various methodological techniques, including survey, interview, experimental, archival and ethnographic research. The course is designed to train social scientists to both recognize and be able to conduct rigorous, theoretically informed research. Additional Courses APSTA-GE.2300. Internship Course In the internship, students will gain experience working with “real world” data, working with an approved faculty member, local firm or organization. Students will receive practical training focused on the kinds of issues that researchers face in collecting and analyzing data. This course will not only enhance the tools and techniques students develop, but will also possibly lead to employment opportunities after graduation.   This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}, {"content": "Degree Requirements - Applied Statistics - NYU Steinhardt Skip to content NYU Steinhardt School of Culture, Education, and Human Development apply now request info contact Department of Humanities and Social Sciences in the Professions About Programs Applied Statistics Education and Jewish Studies Education and Social Policy Education Studies History of Education Interdepartmental Research Studies International Education Philosophy of Education Sociology of Education Minor in Global and Urban Education Studies People Faculty Staff Students Alumni Admissions Research Center for the Promotion of Research Involving Innovative Statistical Methodology Commission on Gender, Race and Social Justice Institute for Education and Social Policy Multinational Institute of American Studies Faculty Research Interests FAQ News Events Applied Statistics Master of Science Program Overview Careers Degree Requirements Sample Schedules Course Descriptions 2015-2016 Courses FAQ Admissions Financial Aid People Events Contact Us Center for the Promotion of Research Involving Innovative Statistical Methodology (PRIISM) Degree Requirements Total Points Required: 34 minimum; 34-47 variable This is a variable-credit program in which you can take a minimum of 34 and a maximum of 47 credits. If you are entering the program with prior statistical training, the accelerated, lower credit option may be ideal for you. The program consists of theoretical foundations, statistical inference and generalized linear models, causal inference, survey research methods, multilevel modeling, applied statistics electives, and unrestricted electives. A statistical consulting research seminar and internship provide practical learning experiences. See brief course descriptions . See links to syllabi for courses offered in 2013-14 . Get links to syllabi for previous years . Required Courses: 31-33 points Foundations in Social Research: Points RESCH-GE.2132: Empirical Research Methods + 3 + Students may choose to replace this requirement with a methods course being offered in their specific area of studies. Inference and Regression: APSTA-GE.2003: Intermediate Quantitative Methods* or APSTA-GE.2003 can be replaced by STAT-GB 2301: Regression and Multivariate Data Analysis 3 APSTA-GE.2004: Advanced Modeling I: Topics in Multivariate Analysis* 2 APSTA-GE.2012: Causal Inference 3 APSTA-GE 2122: Applied Statistical Modeling and Inference 3 APSTA-GE.2997: Adv. Methods in Health and Policy Res.: Generalized Linear Models 2 APSTA-GE.2998: Adv. Methods in Health and Policy Res.: Survival Analysis or SOC-GA 2306: Event History Analysis 2-4 * indicates that the student with equivalent prior coursework may place out of this course. Theoretical and Practical Issues in Survey Methodology: RESCH-GE.2139: Survey Research I 3 APSTA-GE.2110: Applied Statistics: Using Large Databases in Education Research 4 Multilevel Models: APSTA-GE.2040/2041: Multilevel Models (course + practicum) 3 APSTA-GE.2042: Multilevel Models: Nested Data 2 Missing Data: APSTA-GE.2013: Advanced Topics in Quantitative Methods: Missing Data 2 Statistical Consulting Research Seminar and Internship: APSTA-GE.2300 Internship 2-4 APSTA-GE.2401: Statistical Consulting Research Seminar 3 * indicates that the student with equivalent prior coursework may place out of this course. Applied Statistics Electives: 4-8 points Students are required to take two electives with significant applied statistical content (e.g., measurement models, computational statistics. Courses may be taken within or outside the program. WIthin-Program Applied Statistics Electives (may count in required course points for those placing out of the above required course, except those *) APSTA-GE.2011: Adv. Topics in Quantitative Methods: Classification and Clustering 2 APSTA-GE.2015: Adv. Topics in Quantitative Methods: Applied Spatial Statistics 2 APSTA-GE.2016: Adv. Topics in Quant. Meths: Factor Scoring and Practical Issues in Scaling 2 APSTA-GE.2017: Adv. Topics in Quant. Meths: Educational Dat Science Practicum 2 APSTA-GE.2094: Factor Analysis and Structural Equation Modeling 3 Outside-Program Applied Statistics Electives Steinhardt: APSY-GE.2141: Measurement: Modern Test Theory APSY-GE.2142: Measurement and Evaluation: Psychometric Theory PUHE-GE.2306: Epidemiology Courant Institute of Mathematical Sciences, Faculty of Arts and Science, or NYU Poly: MA-GY.7763: Topics in Statistics: Data Mining and Machine Learning MATH-GA 2840: Advanced Topics in Applied Mathematics-Dynamic Computational Statistical models for Socio-economic and Geo-political Systems CSCI-GA-256: Machine Learning and Pattern Recognition For more courses, visit the Data Science curriculum website . Please note some of the courses offered through Data Science may have substantial pre-requisites in mathematics and computer science; further, open seats in classes offered in that program are limited. Department of Politics, Faculty of Arts and Science: POL-GA 3200: Topics in Network Analysis Department of Psychology, Faculty of Arts and Science: PSYCH-GA 2247: Structural Equation Methods PSYCH-GA 2248: Methods for the Analysis of Change Department of Sociology, Faculty of Arts and Science: SOC-GA 2314: Longitudinal Statistics SOC-GA 2306: Event History Analysis Department of Economics, Stern School of Business: ECON-GB 3351: Econometrics I ECON-GB 9912: Econometric Analysis of Panel Data Program of Statistics, Stern School of Business: STAT-GB 2301: Regression and Multivariate Data Analysis STAT-GB 2302: Forecasting Time Series Data STAT-GB 2308: Applied Stochastic Processes For Financial Models STAT-GB 4310.01: Statistics for Social Data Robert F. Wagner Graduate School of Public Service: PADM-GP.2875: Estimating Impacts in Policy Research PADM-GP.2171: Program Analysis and Evaluation Unrestricted Electives: 6-8 points The two unrestricted electives may be taken from departments across the entire university according to your own interests. We recommend EDPLY-GE 2030 Education and Social Policy. Unrestricted Electives Points Elective 1 3-4 Elective 2 3-4 This site, and all its contents, are Copyright © by New York University. All rights reserved. NYU Steinhardt - 246 Greene Street, Suite 300, New York, NY 10003"}]}]
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"Sen. John McCain, R-Ariz., who chairs the Senate Armed Services Committee, urged Trump on Thursday to “resolve the differences within his administration as soon as possible.”\n\nMcCain said in a statement that he supports Army Gen. John Nicholson, the top U.S. commander in Afghanistan.",
"Related: With Trump's Afghanistan strategy murky, experts divided on future\n\nMore: Pentagon to answer for wasting $28 million on questionable camouflage for Afghan soldiers\n\nMore: Pentagon to decide troop levels in Afghanistan\n\nRead or Share this story: https://usat.ly/2wpOXF2",
"Trump’s frustration is not surprising given the length of the conflict and the lack of progress, said Michael O’Hanlon, a military analyst at the Brookings Institution in Washington, D.C. “Any president is going to feel some reluctance to fully own this war,” he said.",
"The Pentagon has said several thousand additional troops are needed to turn the tide in a conflict it has described as stalemate: Afghanistan’s U.S.-backed army is struggling to defeat a Taliban insurgency and a growing number of Islamic State militants.",
"On top of that, several senior Trump advisers, including chief White House strategist Steve Bannon, are anti-globalists skeptical of the defense and foreign policy establishments in Washington and wary of costly nation-building campaigns.",
"The Trump administration has delayed a decision on a troop increase for Afghanistan sought by the Pentagon, as White House advisers debate changes in the overall strategy for the war, which will mark its 16th anniversary in October.",
"CLOSE The war in Afghanistan has passed the 16-year mark, and the United States and its NATO allies are still wrangling over how to meet the need for troops to counter the resurgent Taliban and break a stalemate in the fight.",
"Bannon helped get a White House airing for a proposal by Erik Prince, the former head of the private security firm Blackwater, who has advocated outsourcing some of the fighting in Afghanistan to private contractors.",
"(Photo: Jawad Jalali, European Pressphoto Agency)\n\nWASHINGTON — President Trump faces a stalemate in Afghanistan, not only on the battlefield, but also inside the White House over how to end America's longest war.",
"Yet some of Trump’s closest advisers are skeptical of the costly effort to prop up Afghanistan’s government, and the president himself has expressed frustration at the lack of progress in the war.",
"(June 29) AP\n\nA U.S. soldier inspects the scene of an attack allegedly carried out by the Islamic State, in Kabul, Afghanistan, on July 31, 2017.",
"NBC News reported that Trump, impatient over the course of the war, suggested firing the general during a meeting at the White House on July 19.",
"The latest casualties were two service members killed Wednesday when their convoy came under attack near Kandahar, in southern Afghanistan.",
"The additions, some coming from NATO allies, would allow a U.S.-led coalition to put more advisers in the field with Afghan combat forces.",
"Trump has given the Pentagon the authority to set troop levels for Afghanistan and the war against the Islamic State in Iraq and Syria.",
"The latest to go was Ezra Cohen-Watnick, who was shifted from his position in the National Security Council, the White House said.",
"McMaster, Trump's national security adviser, has removed several Bannon loyalists from the White House national security staff.",
"Defense Secretary Jim Mattis said he wants an Afghanistan strategy formulated before deploying more troops to the war.",
"“Those arguments wouldn’t have any traction if the mission weren’t stuck in neutral for so long,\" O'Hanlon said.",
"“Our commanders in chief, not our commanders in the field, are responsible for this failure,” he said.",
"The U.S. military now has about 8,400 troops on the ground serving as advisers and trainers.",
"McCain said the failure in Afghanistan is a result of a lack of strategy from Washington.",
"Nicholson said a “few thousand” additional troops would help turn the tide of war.",
"About 2,300 U.S. troops have been killed in the war.",
"They do not have a direct combat role.",
"Nicholson remains in the job.",
"Lt. Gen. H.R."
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{
"vorgangId": "35590",
"VORGANG": {
"WAHLPERIODE": "17",
"VORGANGSTYP": "Mündliche Frage",
"TITEL": "Angemessenheit der nach dem Atomgesetz geltenden Deckungsvorsorge für einen nuklearen Katastrophenfall; Pläne zur EU-weiten Verschärfung der Haftungsregelungen für Betreiber von Atomkraftwerken",
"AKTUELLER_STAND": "Beantwortet",
"SIGNATUR": "",
"GESTA_ORDNUNGSNUMMER": "",
"PLENUM": {
"PLPR_KLARTEXT": "Mündliche Frage/Schriftliche Antwort",
"PLPR_HERAUSGEBER": "BT",
"PLPR_NUMMER": "17/107",
"PLPR_SEITEN": "12275A - 12275B",
"PLPR_LINK": "http://dipbt.bundestag.de:80/dip21/btp/17/17107.pdf#P.12275"
},
"EU_DOK_NR": "",
"SCHLAGWORT": [
"Atomgesetz",
"Atomhaftung",
"Europäische Union",
"Kernenergiewirtschaft",
"Kernkraftwerk",
"Nuklearer Unfall"
],
"ABSTRAKT": "Originaltext der Frage(n): \r\n \r\nHält die Bundesregierung die nach dem Atomgesetz geltende Deckungsvorsorge für einen nuklearen Katastrophenfall auch nach den Erfahrungen von Fukushima für ausreichend, die darauf hinweisen, dass die Schäden sowohl die vorliegende Deckungsvorsorge als auch die ökonomische Tragfähigkeit eines Betreibers eines Atomkraftwerkes deutlich überschreiten können, und falls nein, in welcher Höhe hält die Bundesregierung eine Deckungsvorsorge für erforderlich, um sicherzustellen, dass ein anspruchsvoller Opferschutz im Falle eines nuklearen Katastrophenfalls in Deutschland gewährleistet ist, ohne dass der Staat selbst mit eigenen Mitteln oder in Form einer Verstaatlichung des Betreibers einspringen muss, damit dieses Ziel erreicht wird? \r\n \r\nWas konkret versteht die Bundesregierung unter einem „anspruchsvollen Opferschutz“ (siehe Antwort der Bundesregierung vom 4. Mai 2011 auf meine Schriftliche Frage mit der Arbeitsnummer 4/348 vom 28. April 2011) im Zusammenhang mit den Folgen eines nuklearen Katastrophenfalls, und sind der Bundesregierung Pläne der EU-Kommission infolge der Ereignisse in Fukushima bekannt, die Haftungsregelungen für die Betreiber von Atomkraftwerken EU-weit zu verschärfen?"
},
"VORGANGSABLAUF": {
"VORGANGSPOSITION": [
{
"ZUORDNUNG": "BT",
"URHEBER": "Mündliche Frage ",
"FUNDSTELLE": "06.05.2011 - BT-Drucksache 17/5733, Nr. 27, 28",
"FUNDSTELLE_LINK": "http://dipbt.bundestag.de:80/dip21/btd/17/057/1705733.pdf"
},
{
"ZUORDNUNG": "BT",
"URHEBER": "Mündliche Frage/Schriftliche Antwort",
"FUNDSTELLE": "11.05.2011 - BT-Plenarprotokoll 17/107, S. 12275A - 12275B",
"FUNDSTELLE_LINK": "http://dipbt.bundestag.de:80/dip21/btp/17/17107.pdf#P.12275",
"PERSOENLICHER_URHEBER": [
{
"VORNAME": "Hans-Josef",
"NACHNAME": "Fell",
"FUNKTION": "MdB",
"FRAKTION": "BÜNDNIS 90/DIE GRÜNEN",
"AKTIVITAETSART": "Frage",
"SEITE": "12275A"
},
{
"VORNAME": "Ursula",
"NACHNAME": "Heinen-Esser",
"FUNKTION": "Parl. Staatssekr.",
"RESSORT": "Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit",
"AKTIVITAETSART": "Antwort",
"SEITE": "12275A"
}
]
}
]
}
}
|
{"brief":"Dinhabah","long":"Meaning: \"Dinhabah\", an Edomitish town. Usage: Dinhaban. Source: of uncertain derivation; "}
|
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