{"id":29892,"date":"2023-10-14T06:04:00","date_gmt":"2023-10-14T09:04:00","guid":{"rendered":"https:\/\/mindthegraph.com\/blog\/academic-report-format-copy\/"},"modified":"2023-10-10T18:12:07","modified_gmt":"2023-10-10T21:12:07","slug":"ordinal-data-examples","status":"publish","type":"post","link":"https:\/\/mindthegraph.com\/blog\/lv\/ordinal-data-examples\/","title":{"rendered":"Ordin\u0101lo datu izp\u0113te: Piem\u0113ri un pielietojums"},"content":{"rendered":"

P\u0113tniec\u012bbas un datu anal\u012bzes jom\u0101 da\u017e\u0101du datu veidu izpratne ir b\u016btiska, lai izdar\u012btu j\u0113gpilnus secin\u0101jumus un pie\u0146emtu pamatotus l\u0113mumus. Viens no \u0161\u0101diem tipiem ir k\u0101rtas dati, kuriem ir b\u016btiska noz\u012bme da\u017e\u0101d\u0101s discipl\u012bn\u0101s, s\u0101kot no soci\u0101laj\u0101m zin\u0101tn\u0113m un beidzot ar tirgus izp\u0113ti. Izpratne par to, kas ir ordin\u0101lie dati un k\u0101 tie at\u0161\u0137iras no citiem datu veidiem, ir b\u016btiska p\u0113tniekiem, kuru m\u0113r\u0137is ir ieg\u016bt j\u0113gpilnus secin\u0101jumus no datu kop\u0101m. \u0160aj\u0101 rakst\u0101 tiks sniegts visaptvero\u0161s skaidrojums par to, kas ir k\u0101rtas dati un k\u0101da ir to noz\u012bme p\u0113tniec\u012bb\u0101.<\/p>\n\n\n\n

Kas ir k\u0101rtas dati?<\/strong><\/h2>\n\n\n\n

K\u0101rt\u0113jie dati ir kategorisko datu veids, kur\u0101 kategorij\u0101m ir noteikta dabiska sec\u012bba vai sec\u012bba. Tas noz\u012bm\u0113, ka kategorijas ir sak\u0101rtotas t\u0101, ka t\u0101s var sarindot vai sak\u0101rtot, pamatojoties uz to relat\u012bvo v\u0113rt\u012bbu vai noz\u012bmi. Piem\u0113ram, aptaujas jaut\u0101jum\u0101, kur\u0101 respondentiem l\u016bdz nov\u0113rt\u0113t vi\u0146u piekri\u0161anas l\u012bmeni skal\u0101 no 1 l\u012bdz 5, tiek v\u0101kti ordin\u0101li dati, jo atbild\u0113m ir dabiska sec\u012bba no \"piln\u012bgi nepiekr\u012btu\" (1) l\u012bdz \"piln\u012bgi piekr\u012btu\" (5). Ordin\u0101lo datu piem\u0113rus var analiz\u0113t, izmantojot statistikas metodes, piem\u0113ram, chi-kvadr\u0101ts testu, bet ir nepiecie\u0161ama zin\u0101ma piesardz\u012bba, jo att\u0101lumi starp kategorij\u0101m var neb\u016bt vien\u0101di.<\/p>\n\n\n\n

K\u0101rt\u0113jie dati ir \u013coti svar\u012bgi zin\u0101tniskajos p\u0113t\u012bjumos, jo tie \u013cauj klasific\u0113t un sal\u012bdzin\u0101t datus ar dabisku k\u0101rt\u012bbu vai rangu, kas var sniegt v\u0113rt\u012bgu ieskatu par datu mode\u013ciem, attiec\u012bb\u0101m un tendenc\u0113m. \u0160\u0101da veida datus bie\u017ei izmanto soci\u0101lo zin\u0101t\u0146u p\u0113t\u012bjumos, piem\u0113ram, aptauj\u0101s un anket\u0101s, kur respondentiem l\u016bdz nov\u0113rt\u0113t savu viedokli vai pieredzi p\u0113c skalas.<\/p>\n\n\n\n

Att\u0113ls: https:\/\/www.voxco.com\/wp-content\/uploads\/2021\/03\/Cover-scale-1536\u00d7864.jpg<\/p>\n\n\n\n

Ordin\u0101lo datu raksturlielumi<\/strong><\/h2>\n\n\n\n

K\u0101rt\u0113jie dati ir kategorisko datu veids, kas atspogu\u013co noteiktu k\u0101rt\u012bbu vai rangu starp kategorij\u0101m. T\u0101l\u0101k ir uzskait\u012btas da\u017eas galven\u0101s ordin\u0101lo datu \u012bpa\u0161\u012bbas:<\/p>\n\n\n\n

Pas\u016bt\u012bjums: <\/strong>Kategorij\u0101m ordin\u0101lajos datos ir noteikta sec\u012bba vai rangs, un \u0161\u012b sec\u012bba atspogu\u013co piekri\u0161anas, nepiekri\u0161anas vai priek\u0161roku l\u012bmeni. Piem\u0113ram, aptauj\u0101, kur\u0101 jaut\u0101ts par sa\u0146emt\u0101 pakalpojuma kvalit\u0101ti, atbil\u017eu varianti var\u0113tu b\u016bt \"teicami\", \"labi\", \"pietiekami\" vai \"slikti\", un tiem b\u016btu skaidra sec\u012bba.<\/p>\n\n\n\n

Nav skaitliski:<\/strong> <\/em>Ordin\u0101lo datu kategorijas nav oblig\u0101ti j\u0101nor\u0101da ar skait\u013ciem, un kategorijas var b\u016bt v\u0101rdi vai simboli. Piem\u0113ram, restor\u0101nu v\u0113rt\u0113\u0161anas sist\u0113m\u0101 kvalit\u0101tes l\u012bme\u0146u apz\u012bm\u0113\u0161anai var izmantot zvaigzn\u012btes, nevis skaitlisk\u0101s v\u0113rt\u012bbas.<\/p>\n\n\n\n

Nevien\u0101di interv\u0101li:<\/strong> <\/em>Att\u0101lumi starp kategorij\u0101m ne vienm\u0113r ir vien\u0101di. Piem\u0113ram, starp\u012bba starp \"piln\u012bgi piekr\u012btu\" un \"piekr\u012btu\" Likerta skal\u0101 var neb\u016bt t\u0101da pati k\u0101 starp\u012bba starp \"nepiekr\u012btu\" un \"piln\u012bgi nepiekr\u012btu\".<\/p>\n\n\n\n

Ierobe\u017eots kategoriju skaits:<\/strong> Ordin\u0101lajiem datiem parasti ir ierobe\u017eots kategoriju skaits, ko bie\u017ei vien iepriek\u0161 nosaka p\u0113tnieks. Piem\u0113ram, aptauj\u0101 var izmantot Likerta skalu ar piec\u0101m atbil\u017eu iesp\u0113j\u0101m.<\/p>\n\n\n\n

Var uzskat\u012bt par skaitliskajiem datiem: <\/strong>Da\u017ereiz k\u0101rtas datus var uzskat\u012bt par skaitliskajiem datiem statistisk\u0101s anal\u012bzes vajadz\u012bb\u0101m, ta\u010du tas j\u0101dara piesardz\u012bgi. Noz\u012bm\u012bgu skaitlisko v\u0113rt\u012bbu pie\u0161\u0137ir\u0161ana ordin\u0101laj\u0101m kategorij\u0101m var atvieglot anal\u012bzi un interpret\u0101ciju, bet t\u0101 nedr\u012bkst main\u012bt datu b\u016bt\u012bbu.<\/p>\n\n\n\n

Ordin\u0101lo main\u012bgo veidi<\/strong><\/h2>\n\n\n\n

K\u0101rt\u0113jie main\u012bgie ir main\u012bgie, kurus var sak\u0101rtot vai sak\u0101rtot, pamatojoties uz to v\u0113rt\u012bb\u0101m vai atrib\u016btiem. Ir divu veidu k\u0101rtas main\u012bgie:<\/p>\n\n\n\n

Atbilsto\u0161\u0101 kategorija<\/h3>\n\n\n\n

Saska\u0146otu kategoriju ordin\u0101lajos main\u012bgajos ir noteikta main\u012bg\u0101 lieluma kategoriju dabisk\u0101 sec\u012bba. \u0160o k\u0101rt\u012bbu nosaka pats main\u012bgais, un kategorijas ir savstarp\u0113ji izsl\u0113dzo\u0161as. Piem\u0113ram, pirms un p\u0113c p\u0113t\u012bjuma projekt\u0101 vienai un tai pa\u0161ai dal\u012bbnieku grupai m\u0113ra vienu un to pa\u0161u k\u0101rtas main\u012bgo divos da\u017e\u0101dos laika posmos, piem\u0113ram, pirms un p\u0113c \u0101rst\u0113\u0161anas. Kategorijas m\u0113r\u012bjum\u0101 \"pirms\" ir saska\u0146otas vai p\u0101r\u012b savienotas ar kategorij\u0101m m\u0113r\u012bjum\u0101 \"p\u0113c\". <\/p>\n\n\n\n

Cits piem\u0113rs ir p\u0113t\u012bjums, kur\u0101 tiek sal\u012bdzin\u0101tas p\u0101ru v\u0113lmes k\u0101d\u0101 noteikt\u0101 aspekt\u0101, kur viena partnera v\u0113lmes tiek saska\u0146otas vai p\u0101r\u012b savienotas ar otra partnera v\u0113lm\u0113m. Saska\u0146ot\u0101s kategorijas bie\u017ei analiz\u0113, izmantojot neparametriskos statistiskos testus, piem\u0113ram, Vilkoksona parakst\u012bt\u0101 ranga testu vai Fr\u012bdmena testu, lai sal\u012bdzin\u0101tu at\u0161\u0137ir\u012bbas starp kategorij\u0101m katr\u0101 p\u0101r\u012b vai grup\u0101.<\/p>\n\n\n\n

Nesaska\u0146ota kategorija<\/h3>\n\n\n\n

Neatbilsto\u0161\u0101 kategorija ir cita veida k\u0101rtas main\u012bgais. At\u0161\u0137ir\u012bb\u0101 no saska\u0146otaj\u0101m kategorij\u0101m, nesaska\u0146otaj\u0101m kategorij\u0101m nav skaidras saist\u012bbas vai saiknes starp kategorij\u0101m. Piem\u0113ram, ja j\u016bs l\u016bdzat respondentus nov\u0113rt\u0113t savas v\u0113lmes attiec\u012bb\u0101 uz da\u017e\u0101diem m\u016bzikas \u017eanriem, var neb\u016bt skaidra sak\u0101rtojuma vai saist\u012bbas starp d\u017eeza, kantri un roka kategorij\u0101m.<\/p>\n\n\n\n

Nesaska\u0146ot\u0101s kategorij\u0101s kategorijas joproj\u0101m var sak\u0101rtot, pamatojoties uz respondenta individu\u0101laj\u0101m v\u0113lm\u0113m vai uztveri, ta\u010du nav objekt\u012bvas vai konsekventas sak\u0101rto\u0161anas, kas attiektos uz visiem respondentiem. Tas var apgr\u016btin\u0101t datu anal\u012bzi un interpret\u0101ciju sal\u012bdzin\u0101jum\u0101 ar saska\u0146otaj\u0101m kategorij\u0101m, kur\u0101m ir skaidra un konsekventa sec\u012bba.<\/p>\n\n\n\n

Ordin\u0101lo datu piem\u0113ri<\/strong><\/h2>\n\n\n\n

Ordin\u0101lo datu piem\u0113ri ir atrodami daudz\u0101s p\u0113tniec\u012bbas jom\u0101s un da\u017e\u0101dos m\u0113r\u012bjumu veidos. Da\u017ei ordin\u0101lo datu piem\u0113ri ir \u0161\u0101di:<\/p>\n\n\n\n

Interv\u0101la skala<\/h3>\n\n\n\n

Interv\u0101la skala ir m\u0113r\u012bjumu skalas veids, kur\u0101 katrai kategorijai vai atbildei ir pie\u0161\u0137irta skaitliska v\u0113rt\u012bba, un at\u0161\u0137ir\u012bbas starp v\u0113rt\u012bb\u0101m ir noz\u012bm\u012bgas un vien\u0101das. T\u0101 ir l\u012bdz\u012bga attiec\u012bbu skalai, tikai tai nav \u012bsta nulles punkta.<\/p>\n\n\n\n

Piem\u0113ram, Celsija temperat\u016bras skala ir interv\u0101la skalas piem\u0113rs. Starp\u012bba starp 10\u00b0C un 20\u00b0C ir t\u0101da pati k\u0101 starp\u012bba starp 20\u00b0C un 30\u00b0C. Tom\u0113r 0\u00b0C nenoz\u012bm\u0113 piln\u012bgu temperat\u016bras neesam\u012bbu, bet gan konkr\u0113tu punktu skal\u0101.<\/p>\n\n\n\n

Likerta skala<\/h3>\n\n\n\n

Likerta skala ir izplat\u012bts ordin\u0101lo datu veids, kur\u0101 tiek izmantots atbil\u017eu variantu kopums, piem\u0113ram, \"piln\u012bgi piekr\u012btu\", \"piekr\u012btu\", \"neitr\u0101li\", \"nepiekr\u012btu\" un \"piln\u012bgi nepiekr\u012btu\", lai nov\u0113rt\u0113tu attieksmi, viedokli vai uztveri. Katrai atbildei tiek pie\u0161\u0137irta skaitliska v\u0113rt\u012bba, parasti no 1 l\u012bdz 5 vai no 1 l\u012bdz 7, kur augst\u0101ka v\u0113rt\u012bba nor\u0101da uz pozit\u012bv\u0101ku vai sp\u0113c\u012bg\u0101ku atbildi. Likerta skalu bie\u017ei izmanto aptauj\u0101s un anket\u0101s, lai ieg\u016btu ordin\u0101lus datus, kurus var analiz\u0113t, izmantojot \u012bpa\u0161as metodes.<\/p>\n\n\n\n

K\u0101 analiz\u0113t ordin\u0101los datus?<\/strong><\/h2>\n\n\n\n

Ir vair\u0101kas metodes, k\u0101 analiz\u0113t ordin\u0101los datus, tostarp:<\/p>\n\n\n\n

Apraksto\u0161\u0101 statistika:<\/strong> Apraksto\u0161o statistiku izmanto, lai apkopotu un aprakst\u012btu ordin\u0101lo datu centr\u0101lo tendenci un sadal\u012bjumu. Da\u017eas no parasti izmantotaj\u0101m apraksto\u0161aj\u0101m statistik\u0101m ordin\u0101lajiem datiem ir medi\u0101na, moda un procentiles. Apraksto\u0161\u0101 statistika var pal\u012bdz\u0113t sniegt visp\u0101r\u0113ju p\u0101rskatu par datiem un identific\u0113t iesp\u0113jam\u0101s probl\u0113mas, piem\u0113ram, novirzes vai izklied\u0113tu sadal\u012bjumu. Tom\u0113r t\u0101 nesniedz nek\u0101du inform\u0101ciju par at\u0161\u0137ir\u012bbu statistisko noz\u012bm\u012bgumu vai attiec\u012bb\u0101m starp grup\u0101m.<\/p>\n\n\n\n

Neparametriskie testi: <\/strong>Neparametriskos testus parasti izmanto ordin\u0101lo datu anal\u012bzei, jo tie neprasa, lai dati atbilstu noteiktam sadal\u012bjumam, piem\u0113ram, norm\u0101lajam sadal\u012bjumam, un neparedz, ka interv\u0101li starp kategorij\u0101m ir vien\u0101di. \u0160o testu pamat\u0101 ir nov\u0113rojumu rangi, nevis to prec\u012bz\u0101s v\u0113rt\u012bbas. Neparametriskie testi ir iztur\u012bgi pret novirz\u0113m, un tos bie\u017ei izmanto, ja parametrisko testu pie\u0146\u0113mumi nav izpild\u012bti. Tom\u0113r tiem var b\u016bt maz\u0101ka statistisk\u0101 jauda nek\u0101 parametriskajiem testiem, jo \u012bpa\u0161i, ja izlases lielums ir neliels. <\/p>\n\n\n\n

Ordin\u0101l\u0101 lo\u0123istisk\u0101 regresija:<\/strong> Ordin\u0101l\u0101 statistisk\u0101 regresija ir statistikas metode, ko izmanto, lai model\u0113tu sakar\u012bbu starp vienu vai vair\u0101kiem ordin\u0101lajiem neatkar\u012bgajiem main\u012bgajiem un ordin\u0101lo atkar\u012bgo main\u012bgo. \u0160\u012b metode ir noder\u012bga, ja v\u0113laties noteikt faktorus, kas ietekm\u0113 ordin\u0101l\u0101 main\u012bg\u0101 izn\u0101kumu. Ordin\u0101l\u0101 lo\u0123istisk\u0101 regresija pie\u0146em, ka atkar\u012bg\u0101 main\u012bg\u0101 kategorijas ir sak\u0101rtotas un ka att\u0101lums starp kategorij\u0101m ne vienm\u0113r ir vien\u0101ds. T\u0101 ar\u012b pie\u0146em, ka saist\u012bba starp atkar\u012bgo main\u012bgo un neatkar\u012bgajiem main\u012bgajiem ir logaritmiski line\u0101ra.<\/p>\n\n\n\n

Korespondences anal\u012bze:<\/strong> \u0160o metodi izmanto, lai izp\u0113t\u012btu attiec\u012bbas starp diviem vai vair\u0101kiem k\u0101rtas main\u012bgajiem. T\u0101 pal\u012bdz noteikt mode\u013cus un sakar\u012bbas starp main\u012bgajiem un vizualiz\u0113t tos divdimensiju telp\u0101. \u0160\u012b metode ietver kontingences tabulas izveidi, kur\u0101 par\u0101d\u012btas katras kategorijas bie\u017eumu v\u0113rt\u012bbas katram main\u012bgajam. P\u0113c tam katrai kategorijai tiek apr\u0113\u0137in\u0101ts punktu kopums, pamatojoties uz datu kop\u0113jo sadal\u012bjumu. \u0160os v\u0113rt\u0113jumus izmanto, lai izveidotu divdimensiju diagrammu, kur\u0101 katra kategorija ir att\u0113lota ar punktu. Att\u0101lums starp punktiem nor\u0101da l\u012bdz\u012bbas vai at\u0161\u0137ir\u012bbas pak\u0101pi starp kategorij\u0101m.<\/p>\n\n\n\n

Struktur\u0101lo vien\u0101dojumu model\u0113\u0161ana:<\/strong> Struktur\u0101lo vien\u0101dojumu model\u0113\u0161ana (SEM) ir statistikas metode, ko izmanto, lai analiz\u0113tu attiec\u012bbas starp main\u012bgajiem un p\u0101rbaud\u012btu sare\u017e\u0123\u012btus mode\u013cus. T\u0101 ir daudzdimensiju anal\u012bzes metode, kas var apstr\u0101d\u0101t vair\u0101kus main\u012bgos - gan nov\u0113rotos, gan latentos - un p\u0101rbaud\u012bt c\u0113lo\u0146sakar\u012bbas starp main\u012bgajiem. Analiz\u0113jot k\u0101rtas datus, SEM var izmantot, lai p\u0101rbaud\u012btu mode\u013cus, kas ietver vair\u0101kus k\u0101rtas main\u012bgos un latentos konstruktus. T\u0101 var ar\u012b pal\u012bdz\u0113t noteikt un nov\u0113rt\u0113t main\u012bgo savstarp\u0113j\u0101s tie\u0161\u0101s un netie\u0161\u0101s ietekmes lielumu.<\/p>\n\n\n\n

Inferenci\u0101l\u0101 statistika<\/strong><\/h2>\n\n\n\n

Inferenci\u0101l\u0101 statistika ir statistikas nozare, kas ietver secin\u0101jumu izdar\u012b\u0161anu un secin\u0101jumu izdar\u012b\u0161anu par popul\u0101ciju, pamatojoties uz datu izlasi. Tas ir sp\u0113c\u012bgs instruments, kas \u013cauj p\u0113tniekiem izdar\u012bt visp\u0101rin\u0101jumus, prognozes un hipot\u0113zes par liel\u0101ku grupu \u0101rpus nov\u0113rotajiem datiem.<\/p>\n\n\n\n

Apraksto\u0161\u0101 statistika apkopo un apraksta datus, bet secino\u0161\u0101 statistika ir v\u0113l viens solis t\u0101l\u0101k, izmantojot varb\u016bt\u012bbu teoriju un statistikas metodes, lai analiz\u0113tu izlases datus un izdar\u012btu secin\u0101jumus par popul\u0101ciju, no kuras tika \u0146emta izlase. Izmantojot secino\u0161o statistiku, p\u0113tnieki var izteikt prognozes, p\u0101rbaud\u012bt hipot\u0113zes un pie\u0146emt pamatotus l\u0113mumus, pamatojoties uz ieg\u016btajiem rezult\u0101tiem.<\/p>\n\n\n\n

Ordin\u0101lo datu lietojums<\/strong><\/h2>\n\n\n\n

Ordin\u0101los datus izmanto visda\u017e\u0101d\u0101kajos lietojumos, un tos bie\u017ei v\u0101c, izmantojot aptaujas, anketas un citus p\u0113t\u012bjumu veidus. \u0160eit ir da\u017ei izplat\u012bt\u0101kie ordin\u0101lo datu izmanto\u0161anas veidi:<\/p>\n\n\n\n

Aptaujas\/aptaujas anketas<\/h3>\n\n\n\n

Aptaujas un aptaujas anketas ir izplat\u012bts veids, k\u0101 v\u0101kt k\u0101rtas datus. Piem\u0113ram, aptauj\u0101 respondentiem var l\u016bgt nov\u0113rt\u0113t, cik liel\u0101 m\u0113r\u0101 vi\u0146i piekr\u012bt k\u0101dam apgalvojumam, izmantojot skalu no \"piln\u012bgi nepiekr\u012btu\" l\u012bdz \"piln\u012bgi piekr\u012btu\". \u0160\u0101da veida datus p\u0113c tam var izmantot, lai analiz\u0113tu atbil\u017eu tendences vai mode\u013cus.<\/p>\n\n\n\n

P\u0113tniec\u012bba<\/h3>\n\n\n\n

Ordin\u0101los datus var izmantot ar\u012b p\u0113t\u012bjumos, lai nov\u0113rt\u0113tu saist\u012bbu starp da\u017e\u0101diem main\u012bgajiem. Piem\u0113ram, p\u0113tnieks var izmantot ordin\u0101lo skalu, lai izm\u0113r\u012btu konkr\u0113ta simptoma smagumu pacientu grup\u0101 ar konkr\u0113tu slim\u012bbu. \u0160\u0101da veida datus p\u0113c tam var izmantot, lai sal\u012bdzin\u0101tu simptoma smagumu da\u017e\u0101d\u0101s pacientu grup\u0101s vai lai sekotu l\u012bdzi simptoma izmai\u0146\u0101m laika gait\u0101.<\/p>\n\n\n\n

Klientu apkalpo\u0161ana<\/h3>\n\n\n\n

K\u0101rt\u0113jos datus var izmantot ar\u012b klientu apkalpo\u0161an\u0101, lai nov\u0113rt\u0113tu klientu apmierin\u0101t\u012bbu vai neapmierin\u0101t\u012bbu. Piem\u0113ram, klientam var l\u016bgt nov\u0113rt\u0113t savu pieredzi ar uz\u0146\u0113muma produktu vai pakalpojumu skal\u0101 no \"\u013coti neapmierin\u0101ts\" l\u012bdz \"\u013coti apmierin\u0101ts\". \u0160\u0101da veida datus p\u0113c tam var izmantot, lai noteiktu jomas, kur\u0101s nepiecie\u0161ami uzlabojumi, un sekotu l\u012bdzi klientu apmierin\u0101t\u012bbas izmai\u0146\u0101m laika gait\u0101.<\/p>\n\n\n\n

Darba pieteikumi<\/h3>\n\n\n\n

Ordin\u0101los datus var izmantot ar\u012b darba pieteikumos, lai nov\u0113rt\u0113tu pretendenta kvalifik\u0101ciju vai pieredzes l\u012bmeni. Piem\u0113ram, darba dev\u0113js var l\u016bgt darba mekl\u0113t\u0101jiem nov\u0113rt\u0113t savu pieredzes l\u012bmeni konkr\u0113t\u0101 jom\u0101, izmantojot skalu no \"nav pieredzes\" l\u012bdz \"eksperts\". \u0160\u0101da veida datus p\u0113c tam var izmantot, lai sal\u012bdzin\u0101tu da\u017e\u0101du darba pretendentu kvalifik\u0101ciju un izv\u0113l\u0113tos darbam viskvalific\u0113t\u0101ko kandid\u0101tu.<\/p>\n\n\n\n

Ordin\u0101lo un nomin\u0101lo datu at\u0161\u0137ir\u012bba<\/strong><\/h2>\n\n\n\n

K\u0101rt\u0113jie un nomin\u0101lie dati ir divi kategorisko datu veidi. Galven\u0101 at\u0161\u0137ir\u012bba starp tiem ir m\u0113r\u012bjumu l\u012bmen\u012b un inform\u0101cij\u0101, ko tie sniedz.<\/p>\n\n\n\n

K\u0101rt\u0113jie dati ir kategorisko datu veids, kur main\u012bgajiem ir dabiska sec\u012bba vai rangs. Tos m\u0113ra ordin\u0101laj\u0101 l\u012bmen\u012b, kas noz\u012bm\u0113, ka tiem ir dabiska sak\u0101rto\u0161ana, bet at\u0161\u0137ir\u012bbas starp v\u0113rt\u012bb\u0101m nevar kvantitat\u012bvi noteikt vai izm\u0113r\u012bt. Ordin\u0101lo datu piem\u0113ri ir klasifik\u0101cija, reitingi un Likerta skalas.<\/p>\n\n\n\n

No otras puses, nomin\u0101lie dati ar\u012b ir kategorisko datu veids, ta\u010du tiem nav dabiskas sak\u0101rto\u0161anas vai ranga. Tos m\u0113ra nomin\u0101laj\u0101 l\u012bmen\u012b, kas noz\u012bm\u0113, ka datus var klasific\u0113t tikai savstarp\u0113ji izsl\u0113dzo\u0161\u0101s kategorij\u0101s bez rakstur\u012bgas sak\u0101rto\u0161anas vai sec\u012bbas. Nomin\u0101lo datu piem\u0113ri ir dzimums, etnisk\u0101 izcelsme un \u0123imenes st\u0101voklis.<\/p>\n\n\n\n

Galven\u0101 at\u0161\u0137ir\u012bba starp ordin\u0101lajiem un nomin\u0101lajiem datiem ir t\u0101, ka ordin\u0101lajiem datiem ir dabiska sec\u012bba vai rangs, bet nomin\u0101lajiem datiem t\u0101 nav. Lai uzzin\u0101tu vair\u0101k par ordin\u0101lo un nomin\u0101lo datu at\u0161\u0137ir\u012bb\u0101m, skatiet \u0161aj\u0101 t\u012bmek\u013ca vietn\u0113.<\/a><\/p>\n\n\n\n

Nepiecie\u0161ama \u013coti specifiska ilustr\u0101cija? M\u0113s to izveidosim jums!<\/strong><\/h2>\n\n\n\n

Mind the Graph<\/a> platforma pied\u0101v\u0101 pla\u0161u zin\u0101tnisko ilustr\u0101ciju bibliot\u0113ku un veidnes ar sare\u017e\u0123\u012btiem zin\u0101tniskiem j\u0113dzieniem un konkr\u0113tiem nepiecie\u0161amajiem att\u0113liem. Mind the Graph sadarbosies ar jums, lai izveidotu augstas kvalit\u0101tes ilustr\u0101ciju, kas atbilst j\u016bsu v\u0113lm\u0113m. \u0160is pakalpojums nodro\u0161ina, ka j\u016bs varat ieg\u016bt tie\u0161i t\u0101du vizu\u0101lo noform\u0113jumu, k\u0101ds nepiecie\u0161ams j\u016bsu p\u0113t\u012bjumam, prezent\u0101cijai vai publik\u0101cijai, un jums nav nepiecie\u0161ama specializ\u0113ta dizaina programmat\u016bra vai prasmes.<\/p>\n\n\n\n

<\/div>\n\n\n
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\"\"<\/a><\/figure><\/div>\n\n\n
<\/div>\n\n\n\n
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S\u0101ciet veidot ar Mind the Graph<\/a><\/div>\n<\/div>\n\n\n\n
<\/div>","protected":false},"excerpt":{"rendered":"

Ieg\u016bstiet visaptvero\u0161u izpratni par ordin\u0101lo datu piem\u0113riem \u0161eit. Uzziniet, kas ir k\u0101rtas dati un k\u0101 tos efekt\u012bvi izmantot.<\/p>","protected":false},"author":35,"featured_media":29894,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[959,28],"tags":[],"yoast_head":"\nExploring Ordinal Data: Examples and Uses - Mind the Graph Blog<\/title>\n<meta name=\"description\" content=\"Get a comprehensive understanding of ordinal data examples here. 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