{"id":29197,"date":"2023-08-25T09:37:03","date_gmt":"2023-08-25T12:37:03","guid":{"rendered":"https:\/\/mindthegraph.com\/blog\/one-way-anova-copy\/"},"modified":"2024-12-05T15:49:02","modified_gmt":"2024-12-05T18:49:02","slug":"types-of-sampling","status":"publish","type":"post","link":"https:\/\/mindthegraph.com\/blog\/lv\/paraugu-nemsanas-veidi\/","title":{"rendered":"K\u0101 izv\u0113l\u0113ties pareizos izlases veidus savam p\u0113t\u012bjumam?"},"content":{"rendered":"

Izlases veido\u0161ana ir b\u016btisks jebkura p\u0113tniec\u012bbas projekta aspekts, un izv\u0113l\u0113t\u0101s izlases veids var b\u016btiski ietekm\u0113t p\u0113t\u012bjuma rezult\u0101tu der\u012bgumu un ticam\u012bbu. T\u0101 k\u0101 ir pieejams tik daudz da\u017e\u0101du izlases veido\u0161anas meto\u017eu, var b\u016bt sare\u017e\u0123\u012bti izv\u0113l\u0113ties vispiem\u0113rot\u0101ko savam p\u0113tniec\u012bbas projektam. \u0160\u0101 raksta m\u0113r\u0137is ir sniegt visaptvero\u0161u p\u0101rskatu par da\u017e\u0101diem izlases veidiem un to priek\u0161roc\u012bb\u0101m un tr\u016bkumiem, k\u0101 ar\u012b par faktoriem, kas j\u0101\u0146em v\u0113r\u0101, izv\u0113loties izlases veidu, un bie\u017e\u0101k sastopamaj\u0101m k\u013c\u016bd\u0101m, no kur\u0101m j\u0101izvair\u0101s.<\/p>\n\n\n\n

Kas ir paraugu \u0146em\u0161ana?<\/h2>\n\n\n\n

Paraugu \u0146em\u0161ana ir process, kur\u0101 no liel\u0101kas popul\u0101cijas tiek atlas\u012bta indiv\u012bdu vai vien\u012bbu apak\u0161kopa, ko reprezent\u0113t un p\u0113t\u012bt. T\u0101 ir b\u016btiska liel\u0101k\u0101s da\u013cas p\u0113t\u012bjumu sast\u0101vda\u013ca, jo \u013cauj p\u0113tniekiem izdar\u012bt pamatotus secin\u0101jumus par visu popul\u0101ciju, pamatojoties uz maz\u0101ku izlasi. Paraugu \u0146em\u0161anas m\u0113r\u0137is ir ieg\u016bt reprezentat\u012bvu izlasi, kas prec\u012bzi atspogu\u013co p\u0113t\u0101m\u0101s popul\u0101cijas \u012bpa\u0161\u012bbas. Izmantot\u0101 izlases metode ir atkar\u012bga no p\u0113t\u012bjuma jaut\u0101juma, popul\u0101cijas raksturojuma un pieejamajiem resursiem.<\/p>\n\n\n\n

Paraugu \u0146em\u0161anas veidi<\/h2>\n\n\n\n

Paraugu \u0146em\u0161ana ir process, kur\u0101 no liel\u0101kas popul\u0101cijas tiek atlas\u012bta reprezentat\u012bva indiv\u012bdu vai vien\u012bbu grupa. Divi galvenie izlases veidi ir varb\u016bt\u012bbas izlase un izlase bez varb\u016bt\u012bbas.<\/p>\n\n\n\n

Paraugu \u0146em\u0161ana p\u0113c varb\u016bt\u012bbas<\/h3>\n\n\n\n

Varb\u016bt\u012bbas izlas\u0113 izmanto nejau\u0161\u012bbas metodi, kas nodro\u0161ina, ka katram popul\u0101cijas loceklim ir vien\u0101da vai zin\u0101ma iesp\u0113ja tikt izv\u0113l\u0113tam, t\u0101d\u0113j\u0101di nodro\u0161inot taisn\u012bgu un reprezentat\u012bvu izlasi. Past\u0101v vair\u0101ki varb\u016bt\u012bbas izlases veidi, tostarp:<\/p>\n\n\n\n

Vienk\u0101r\u0161\u0101 izlases veida paraugu \u0146em\u0161ana<\/h4>\n\n\n\n

Vienk\u0101r\u0161\u0101 izlases veida izlase ir popul\u0101ra un vienk\u0101r\u0161a izlases metode statistik\u0101. T\u0101 ietver indiv\u012bdu vai elementu apak\u0161kopas atlasi no liel\u0101kas popul\u0101cijas t\u0101, lai katram indiv\u012bdam vai elementam b\u016btu vien\u0101das iesp\u0113jas tikt iek\u013cautam izlas\u0113.<\/p>\n\n\n\n

Sistem\u0101tiska paraugu \u0146em\u0161ana<\/h4>\n\n\n\n

Sistem\u0101tisk\u0101 izlase ir metode, ar kuras pal\u012bdz\u012bbu no popul\u0101cijas regul\u0101ros interv\u0101los atlasa dal\u012bbniekus. Piem\u0113ram, ja popul\u0101cijas lielums ir 100 un v\u0113lamais izlases lielums ir 20, izlas\u0113 tiks atlas\u012bts katrs piektais popul\u0101cijas loceklis.<\/p>\n\n\n\n

Stratific\u0113t\u0101 izlase<\/h4>\n\n\n\n

Stratific\u0113ta izlase ir metode, kas paredz iedz\u012bvot\u0101ju grupas sadal\u012b\u0161anu atsevi\u0161\u0137\u0101s apak\u0161grup\u0101s vai stratos, pamatojoties uz konkr\u0113tiem raksturlielumiem, piem\u0113ram, vecumu vai dzimumu. P\u0113c tam no katra sl\u0101\u0146a atlasa dal\u012bbniekus proporcion\u0101li attiec\u012bg\u0101 sl\u0101\u0146a lielumam popul\u0101cij\u0101.<\/p>\n\n\n\n

Klasteru paraugu \u0146em\u0161ana<\/h4>\n\n\n\n

Klasteru izlase ietver popul\u0101cijas sadal\u012b\u0161anu klasteros vai grup\u0101s un p\u0113c tam \u0161o klasteru izlases veida izlases veida atlasi. P\u0113c tam izlas\u0113 iek\u013cauj visus atlas\u012bto klasteru locek\u013cus.<\/p>\n\n\n\n

Daudzpak\u0101pju paraugu \u0146em\u0161ana<\/h4>\n\n\n\n

Lai ieg\u016btu reprezentat\u012bvu izlasi, daudzpak\u0101pju izlas\u0113 tiek izmantotas da\u017e\u0101das izlases metodes. Piem\u0113ram, p\u0113tnieks var izmantot stratific\u0113tu izlasi, lai atlas\u012btu kopas, un p\u0113c tam, lai atlas\u012btu dal\u012bbniekus no \u0161\u012bm kop\u0101m, izmantot vienk\u0101r\u0161u nejau\u0161o izlasi.<\/p>\n\n\n\n

Nelabai ticam\u012bbas izlases metode<\/h3>\n\n\n\n

Paraugu atlase bez varb\u016bt\u012bbas ir izlases metode, kur\u0101 dal\u012bbnieku atlase ir balst\u012bta uz faktoriem, kas nav varb\u016bt\u012bba. Tas noz\u012bm\u0113, ka da\u017eiem popul\u0101cijas locek\u013ciem var b\u016bt liel\u0101ka iesp\u0113ja tikt iek\u013cautiem izlas\u0113 nek\u0101 citiem. Ir vair\u0101ki ar varb\u016bt\u012bbu nesaist\u012btas izlases veidi, tostarp:<\/p>\n\n\n\n

\u0112rt\u0101 izlase<\/h4>\n\n\n\n

\u0112rta izlase ir metode, kur\u0101 dal\u012bbnieki tiek atlas\u012bti, pamatojoties uz to vieglu pieejam\u012bbu vai sasniedzam\u012bbu. Piem\u0113ram, p\u0113tnieks var atlas\u012bt dal\u012bbniekus no klases, kuru vi\u0146\u0161 vada, vai no tie\u0161saistes foruma.<\/p>\n\n\n\n

Kvotu atlase<\/h4>\n\n\n\n

Kvotas izlase ir dal\u012bbnieku atlases metode, kuras m\u0113r\u0137is ir nodro\u0161in\u0101t konkr\u0113tu raksturlielumu p\u0101rst\u0101v\u012bbu izlas\u0113, atspogu\u013cojot popul\u0101cijas daudzveid\u012bbu. Piem\u0113ram, p\u0113tnieks var censties atlas\u012bt noteiktu skaitu v\u012brie\u0161u un sievie\u0161u vai noteiktu skaitu dal\u012bbnieku no da\u017e\u0101d\u0101m vecuma grup\u0101m.<\/p>\n\n\n\n

Izlases veido\u0161ana p\u0113c sprieduma<\/h4>\n\n\n\n

Sprie\u017eot p\u0113c sprieduma, tiek atlas\u012bti dal\u012bbnieki, pamatojoties uz p\u0113tnieka spriedumu vai pieredzi. T\u0101 var b\u016bt piem\u0113rota, ja tiek p\u0113t\u012bta \u013coti specializ\u0113ta vai gr\u016bti sasniedzama iedz\u012bvot\u0101ju grupa.<\/p>\n\n\n\n

Sniega bumbas parauga \u0146em\u0161ana<\/h4>\n\n\n\n

\"Sniega bumbas\" izlase ir dal\u012bbnieku atlases metode, kas balst\u0101s uz eso\u0161o dal\u012bbnieku ieteikumiem. T\u0101 var b\u016bt noder\u012bga, ja tiek p\u0113t\u012bta popul\u0101cija, kuru ir gr\u016bti identific\u0113t vai kurai ir gr\u016bti tie\u0161i piek\u013c\u016bt, piem\u0113ram, narkotiku lietot\u0101ji vai imigranti bez dokumentiem.<\/p>\n\n\n\n

P\u0101rbaudiet m\u016bsu satura emu\u0101ru par \"\"Sniega bumbas\" parauga \u0146em\u0161ana: V\u0113rien\u012bga p\u0113tniec\u012bbas r\u012bka nosl\u0113pumu atkl\u0101\u0161ana<\/a>“.<\/p>\n\n\n\n

<\/div>\n\n\n
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\"paraugu
Izgatavots ar Mind the Graph<\/a><\/em><\/figcaption><\/figure><\/div>\n\n\n
<\/div>\n\n\n\n
\"\"<\/a><\/figure>\n\n\n\n

Da\u017e\u0101du paraugu veidu priek\u0161roc\u012bbas un tr\u016bkumi<\/h2>\n\n\n\n

Katram izlases veidam ir savas priek\u0161roc\u012bbas un tr\u016bkumi, kas p\u0113tniekiem j\u0101\u0146em v\u0113r\u0101, izv\u0113loties izlases metodi. \u0160eit ir da\u017eas visp\u0101r\u012bgas da\u017e\u0101du paraugu veidu priek\u0161roc\u012bbas un tr\u016bkumi:<\/p>\n\n\n\n

Vienk\u0101r\u0161\u0101 izlases veida paraugu \u0146em\u0161ana<\/strong><\/h3>\n\n\n\n

Priek\u0161roc\u012bbas: Tas ir viegli lietojams un nodro\u0161ina reprezentat\u012bvu iedz\u012bvot\u0101ju izlasi.<\/p>\n\n\n\n

Tr\u016bkumi: Pilna iedz\u012bvot\u0101ju saraksta izveide var b\u016bt d\u0101rga un laikietilp\u012bga.<\/p>\n\n\n\n

Sistem\u0101tiska paraugu \u0146em\u0161ana<\/strong><\/h3>\n\n\n\n

Priek\u0161roc\u012bbas: T\u0101 ir maz\u0101k laikietilp\u012bga nek\u0101 vienk\u0101r\u0161a izlases veida izlase un var nodro\u0161in\u0101t reprezentat\u012bvu popul\u0101cijas izlasi.<\/p>\n\n\n\n

Tr\u016bkumi: Ja popul\u0101cijai ir periodisks modelis, t\u0101 var nenodro\u0161in\u0101t reprezentat\u012bvu izlasi.<\/p>\n\n\n\n

Stratific\u0113t\u0101 izlase<\/strong><\/h3>\n\n\n\n

Priek\u0161roc\u012bbas: Tas var palielin\u0101t izlases reprezentativit\u0101ti, nodro\u0161inot, ka tiek iek\u013cautas svar\u012bgas apak\u0161grupas.<\/p>\n\n\n\n

Tr\u016bkumi: Var b\u016bt gr\u016bti noteikt atbilsto\u0161os sl\u0101\u0146us un to lielumus.<\/p>\n\n\n\n

Klasteru paraugu \u0146em\u0161ana<\/strong><\/h3>\n\n\n\n

Priek\u0161roc\u012bbas: Tas ir noder\u012bgs liel\u0101m \u0123eogr\u0101fiski izklied\u0113t\u0101m iedz\u012bvot\u0101ju grup\u0101m un var samazin\u0101t izmaksas un laiku.<\/p>\n\n\n\n

Tr\u016bkumi: Tas var samazin\u0101t izlases reprezentativit\u0101ti, ja klasteri nav reprezentat\u012bvi attiec\u012bb\u0101 pret popul\u0101ciju.<\/p>\n\n\n\n

Daudzpak\u0101pju paraugu \u0146em\u0161ana<\/strong><\/h3>\n\n\n\n

Priek\u0161roc\u012bbas: Tas var b\u016bt noder\u012bgs liel\u0101m \u0123eogr\u0101fiski izklied\u0113t\u0101m iedz\u012bvot\u0101ju grup\u0101m un var samazin\u0101t izmaksas un laiku.<\/p>\n\n\n\n

Tr\u016bkumi: Tas var samazin\u0101t izlases reprezentativit\u0101ti, ja klasteri nav reprezentat\u012bvi attiec\u012bb\u0101 pret popul\u0101ciju.<\/p>\n\n\n\n

\u0112rt\u0101 izlase<\/strong><\/h3>\n\n\n\n

Priek\u0161roc\u012bbas: Tas ir viegli un \u0101tri \u012bstenojams.<\/p>\n\n\n\n

Tr\u016bkumi: Tas var rad\u012bt neobjektivit\u0101ti un var neb\u016bt reprezentat\u012bvs attiec\u012bb\u0101 uz popul\u0101ciju.<\/p>\n\n\n\n

Kvotu atlase<\/strong><\/h3>\n\n\n\n

Priek\u0161roc\u012bbas: Tas ir viegli \u012bstenojams un var nodro\u0161in\u0101t, ka izlas\u0113 ir iek\u013cautas svar\u012bgas apak\u0161grupas.<\/p>\n\n\n\n

Tr\u016bkumi: Tas var rad\u012bt neobjektivit\u0101ti un var neb\u016bt reprezentat\u012bvs attiec\u012bb\u0101 uz popul\u0101ciju.<\/p>\n\n\n\n

Izlases veido\u0161ana p\u0113c sprieduma<\/strong><\/h3>\n\n\n\n

Priek\u0161roc\u012bbas: Tas ir noder\u012bgs specializ\u0113t\u0101m iedz\u012bvot\u0101ju grup\u0101m un var b\u016bt efekt\u012bv\u0101ks nek\u0101 citas metodes.<\/p>\n\n\n\n

Tr\u016bkumi: Tas var rad\u012bt neobjektivit\u0101ti un var neb\u016bt reprezentat\u012bvs attiec\u012bb\u0101 uz popul\u0101ciju.<\/p>\n\n\n\n

Sniega bumbas parauga \u0146em\u0161ana<\/strong><\/h3>\n\n\n\n

Priek\u0161roc\u012bbas: Tas ir noder\u012bgs gr\u016bti sasniedzam\u0101m iedz\u012bvot\u0101ju grup\u0101m un var b\u016bt efekt\u012bv\u0101ks nek\u0101 citas metodes.<\/p>\n\n\n\n

Tr\u016bkumi: Tas var rad\u012bt neobjektivit\u0101ti un var neb\u016bt reprezentat\u012bvs attiec\u012bb\u0101 uz popul\u0101ciju.<\/p>\n\n\n\n

P\u0101rbaudiet m\u016bsu satura emu\u0101ru par \"\"Sniega bumbas\" parauga \u0146em\u0161ana: V\u0113rien\u012bga p\u0113tniec\u012bbas r\u012bka nosl\u0113pumu atkl\u0101\u0161ana<\/a>“.<\/p>\n\n\n\n

Faktori, kas j\u0101\u0146em v\u0113r\u0101, izv\u0113loties parauga veidu<\/strong><\/h2>\n\n\n\n

Izlases veida izv\u0113le ir svar\u012bgs solis p\u0113tniec\u012bb\u0101, un t\u0101 ietver vair\u0101ku faktoru apsv\u0113r\u0161anu, lai nodro\u0161in\u0101tu, ka izlase ir reprezentat\u012bva attiec\u012bb\u0101 pret popul\u0101ciju un ka rezult\u0101ti ir der\u012bgi un ticami.<\/p>\n\n\n\n

P\u0113t\u012bjuma jaut\u0101jums: <\/strong>Tas ir s\u0101kumpunkts izlases veida izv\u0113lei, jo izlase j\u0101izv\u0113las, lai atbild\u0113tu uz p\u0113t\u012bjuma jaut\u0101jumu un m\u0113r\u0137iem. P\u0113tniekiem j\u0101nosaka, k\u0101du popul\u0101ciju vi\u0146i v\u0113las p\u0113t\u012bt, un j\u0101izv\u0113las izlase, kas ir reprezentat\u012bva \u0161ai popul\u0101cijai.<\/p>\n\n\n\n

Iedz\u012bvot\u0101ju skaits:<\/strong> Svar\u012bgi faktori, kas j\u0101\u0146em v\u0113r\u0101, ir ar\u012b iedz\u012bvot\u0101ju skaits un raksturojums. Liel\u0101kai popul\u0101cijai var b\u016bt nepiecie\u0161ams liel\u0101ks izlases lielums, un popul\u0101cijas raksturojums var ietekm\u0113t izlases veida izv\u0113li.<\/p>\n\n\n\n

Parauga lielums:<\/strong> Izlases lielumam j\u0101b\u016bt pietiekami lielam, lai nodro\u0161in\u0101tu rezult\u0101tu ticam\u012bbu un der\u012bgumu. Liel\u0101ks izlases lielums samazina k\u013c\u016bdas robe\u017eu un palielina rezult\u0101tu precizit\u0101ti. <\/p>\n\n\n\n

Izlases k\u013c\u016bda:<\/strong> P\u0113tniekiem j\u0101\u0146em v\u0113r\u0101 ar\u012b iesp\u0113jam\u0101 izlases k\u013c\u016bda un j\u0101izv\u0113las t\u0101ds izlases veids, kas \u0161o k\u013c\u016bdu samazina l\u012bdz minimumam. Izlases k\u013c\u016bda var rasties, ja izlase nav reprezentat\u012bva attiec\u012bb\u0101 pret popul\u0101ciju, k\u0101 rezult\u0101t\u0101 rezult\u0101ti ir neprec\u012bzi.<\/p>\n\n\n\n

Paraugu \u0146em\u0161anas metode:<\/strong> <\/em>Izmantotajai izlases metodei j\u0101b\u016bt atbilsto\u0161ai izlases veidam un p\u0113t\u012bjuma jaut\u0101jumam. Da\u017e\u0101d\u0101m izlases metod\u0113m ir da\u017e\u0101das stipr\u0101s un v\u0101j\u0101s puses, un p\u0113tniekiem j\u0101izv\u0113las metode, kas vislab\u0101k atbilst vi\u0146u vajadz\u012bb\u0101m.<\/p>\n\n\n\n

Datu anal\u012bze:<\/strong> <\/em>\u0160\u012bs metodes ar\u012b j\u0101\u0146em v\u0113r\u0101, izv\u0113loties parauga veidu. Izlases lielums un izlases metode var ietekm\u0113t datu anal\u012bzes meto\u017eu izv\u0113li, un p\u0113tniekiem j\u0101izv\u0113las metode, kas ir piem\u0113rota vi\u0146u izlasei un p\u0113t\u012bjuma jaut\u0101jumam.<\/p>\n\n\n\n

Paraugu \u0146em\u0161anas bie\u017e\u0101k\u0101s k\u013c\u016bdas, no kur\u0101m j\u0101izvair\u0101s<\/strong><\/h2>\n\n\n\n

Lai izvair\u012btos no k\u013c\u016bd\u0101m, p\u0113tniekiem r\u016bp\u012bgi j\u0101apsver izlases metodes un j\u0101cen\u0161as izmantot reprezentat\u012bvas un objekt\u012bvas izlases. Vi\u0146iem ar\u012b j\u0101veic pas\u0101kumi, lai l\u012bdz minimumam samazin\u0101tu izlases k\u013c\u016bdas, un j\u0101izmanto atbilsto\u0161as statistikas metodes datu anal\u012bzei. \u0160eit ir aprakst\u012btas bie\u017e\u0101k sastopam\u0101s k\u013c\u016bdas, no kur\u0101m j\u0101izvair\u0101s, veicot izlases veido\u0161anu p\u0113tniec\u012bb\u0101:<\/p>\n\n\n\n

Atlases neobjektivit\u0101te: <\/strong>Neobjekt\u012bvi rezult\u0101ti var rasties, ja vai nu izlases metode, vai pati izlase nav reprezentat\u012bva attiec\u012bb\u0101 uz p\u0113t\u0101mo popul\u0101ciju.<\/p>\n\n\n\n

Izlases k\u013c\u016bda:<\/strong> Veicot izlasi, dabiski rodas vari\u0101cijas, kas var izrais\u012bt popul\u0101cijas parametru neprec\u012bzu apl\u0113si.<\/p>\n\n\n\n

Neatbildes novirze:<\/strong> Tas notiek tad, ja da\u017ei izlases locek\u013ci neatbild uz aptaujas vai p\u0113t\u012bjuma jaut\u0101jumiem, un tas var rad\u012bt neobjektivit\u0101ti rezult\u0101tos.<\/p>\n\n\n\n

Paraugu atlases r\u0101mja novirze:<\/strong> To izraisa nepiln\u012bga, neprec\u012bza vai novecojusi izlases sist\u0113ma, kas rada neobjektivit\u0101ti. Vair\u0101k par to lasiet m\u016bsu satura blog\u0101 \"Probl\u0113ma, ko sauc par izlases novirzi<\/a>“.<\/p>\n\n\n\n

Br\u012bvpr\u0101t\u012bgas atbildes novirze:<\/strong> <\/em>Dal\u012bbnieki pa\u0161i izv\u0113las piedal\u012bties p\u0113t\u012bjum\u0101, kas var rad\u012bt neobjekt\u012bvus rezult\u0101tus, jo tie, kuri izv\u0113las piedal\u012bties, var at\u0161\u0137irties no tiem, kuri nepiedal\u0101s.<\/p>\n\n\n\n

Nepiln\u012bga sl\u0113p\u0161anas aizspriedumain\u012bba: <\/strong>Rezult\u0101ti var k\u013c\u016bt neobjekt\u012bvi, ja atsevi\u0161\u0137as iedz\u012bvot\u0101ju grupas nav p\u0101rst\u0101v\u0113tas izlases sist\u0113m\u0101, ko d\u0113v\u0113 par nepietiekamas aptver\u0161anas neobjektivit\u0101ti.<\/p>\n\n\n\n

P\u0101rm\u0113r\u012bga visp\u0101rin\u0101\u0161ana:<\/strong> <\/em>Pla\u0161u visp\u0101rin\u0101jumu izdar\u012b\u0161ana ir bie\u017ei sastopama k\u013c\u016bda p\u0113t\u012bjumos, kad, pamatojoties uz nelielu izlases apjomu, tiek izdar\u012bti visaptvero\u0161i secin\u0101jumi par popul\u0101ciju, k\u0101 rezult\u0101t\u0101 rezult\u0101ti ir neprec\u012bzi.<\/p>\n\n\n\n

Paraugu \u0146em\u0161anas metodes kvalitat\u012bvajos p\u0113t\u012bjumos<\/strong><\/h2>\n\n\n\n

Kvalitat\u012bvajos p\u0113t\u012bjumos da\u017eas izplat\u012bt\u0101k\u0101s izlases metodes ir \u0161\u0101das:<\/p>\n\n\n\n

M\u0113r\u0137tiec\u012bga izlase:<\/strong> T\u0101 ir dal\u012bbnieku atlase, pamatojoties uz konkr\u0113tiem krit\u0113rijiem, kas attiecas uz p\u0113t\u012bjuma jaut\u0101jumu vai m\u0113r\u0137i. Tas var ietvert t\u0101du personu izv\u0113li, kur\u0101m ir \u012bpa\u0161as zin\u0101\u0161anas, pieredze vai unik\u0101ls skat\u012bjums.<\/p>\n\n\n\n

Sniega bumbas parauga \u0146em\u0161ana: <\/strong>S\u0101k ar nelielu dal\u012bbnieku grupu un p\u0113c tam l\u016bdz vi\u0146us nor\u012bkot citus potenci\u0101los dal\u012bbniekus, kas atbilst p\u0113t\u012bjuma krit\u0113rijiem. \u0160is pa\u0146\u0113miens var b\u016bt noder\u012bgs, ja interes\u0113jo\u0161o iedz\u012bvot\u0101ju grupu ir gr\u016bti sasniegt vai tai ir zems atbil\u017eu \u012bpatsvars. P\u0101rbaudiet m\u016bsu satura blogu par \"\"Sniega bumbas\" parauga \u0146em\u0161ana: V\u0113rien\u012bga p\u0113tniec\u012bbas r\u012bka nosl\u0113pumu atkl\u0101\u0161ana<\/a>“.<\/p>\n\n\n\n

Maksim\u0101l\u0101s vari\u0101cijas paraugu \u0146em\u0161ana:<\/strong> Ja m\u0113r\u0137is ir padzi\u013cin\u0101ti izp\u0113t\u012bt k\u0101du par\u0101d\u012bbu un atspogu\u013cot t\u0101s sare\u017e\u0123\u012bt\u012bbu, ir lietder\u012bgi izv\u0113l\u0113ties dal\u012bbniekus, kas aptver pla\u0161u ar p\u0113t\u012bjuma jaut\u0101jumu saist\u012bto perspekt\u012bvu vai pieredzes spektru. \u0160\u0101da pieeja \u013cauj p\u0113tniekam aptvert pla\u0161\u0101ku ieskatu kl\u0101stu un uzlabot p\u0113t\u012bjuma vispus\u012bbu.<\/p>\n\n\n\n

Teor\u0113tisk\u0101 paraugu \u0146em\u0161ana: <\/strong>\u0160\u012b metode prasa izv\u0113l\u0113ties dal\u012bbniekus, pamatojoties uz datu v\u0101k\u0161anas laik\u0101 izkristaliz\u0113ju\u0161aj\u0101m t\u0113m\u0101m vai mode\u013ciem. To parasti izmanto pamatot\u0101s teorijas p\u0113t\u012bjumos, kuru m\u0113r\u0137is ir izstr\u0101d\u0101t teoriju, kas sak\u0146ojas datos.<\/p>\n\n\n\n

\u0112rta paraugu \u0146em\u0161ana: <\/strong>\u0112rt\u0101 izlas\u0113 izv\u0113las dal\u012bbniekus, kuri ir viegli pieejami vai viegli pieejami, lai piedal\u012btos p\u0113t\u012bjum\u0101. P\u0113tnieki bie\u017ei izmanto \u0161o metodi izp\u0113tes p\u0113t\u012bjumos vai gad\u012bjumos, kad laiks un resursi ir ierobe\u017eoti. Tom\u0113r t\u0101 var rad\u012bt neobjekt\u012bvu izlasi, ja dal\u012bbnieki nav reprezentat\u012bvi p\u0101rst\u0101vo\u0161i interes\u0113jo\u0161o popul\u0101ciju.<\/p>\n\n\n\n

Paraugu \u0146em\u0161anas metodes kvantitat\u012bvajos p\u0113t\u012bjumos<\/strong><\/h2>\n\n\n\n

\u0160eit ir da\u017ei parasti kvantitat\u012bvajos p\u0113t\u012bjumos izmantotie atlases pa\u0146\u0113mieni:<\/p>\n\n\n\n

Vienk\u0101r\u0161\u0101 izlases veida izlase: <\/strong>T\u0101 ir pamata izlases metode, kur\u0101 katram popul\u0101cijas loceklim ir vien\u0101das iesp\u0113jas tikt atlas\u012btam izlas\u0113.<\/p>\n\n\n\n

Stratific\u0113ta izlases veida izlases veida p\u0101rbaude:<\/strong> Lai nodro\u0161in\u0101tu reprezentativit\u0101ti, stratific\u0113t\u0101s nejau\u0161\u0101s izlases metode ietver popul\u0101cijas sadal\u012b\u0161anu stratos vai grup\u0101s, pamatojoties uz noteiktiem krit\u0113rijiem, un paraugu atlasi no katra strata.<\/p>\n\n\n\n

Klasteru paraugu \u0146em\u0161ana: <\/strong>T\u0101 ir metode, kas ietver nejau\u0161as izlases veid\u0101 atlas\u012btu klasteru vai grupu, piem\u0113ram, skolu vai apkaimju, atlasi un p\u0113c tam indiv\u012bdu atlasi katr\u0101 atlas\u012btaj\u0101 klaster\u012b, lai veidotu izlasi. P\u0101rbaudiet m\u016bsu satura emu\u0101ru par \"Klasteru anal\u012bzes iesp\u0113ju atkl\u0101\u0161ana<\/a>“.<\/p>\n\n\n\n

Sistem\u0101tiska paraugu \u0146em\u0161ana: <\/strong>Sistem\u0101tisk\u0101 izlase ir metode, ar kuras pal\u012bdz\u012bbu atlasa indiv\u012bdus no popul\u0101cijas, izv\u0113loties katru n-to locekli, piem\u0113ram, katru desmito personu sarakst\u0101.<\/p>\n\n\n\n

Daudzpak\u0101pju paraugu \u0146em\u0161ana:<\/strong> Tas atlasa paraugus vair\u0101kos posmos. Piem\u0113ram, p\u0113tnieki var\u0113tu s\u0101kt ar nejau\u0161as izlases veida izlases veida izv\u0113li no \u0161tatiem, p\u0113c tam - no \u0161o \u0161tatu pils\u0113tu izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veida izlases veid\u0101.<\/p>\n\n\n\n

\u0112rta paraugu \u0146em\u0161ana:<\/strong> <\/em>Tas ir pa\u0146\u0113miens, kas attiecas uz p\u0113t\u012bjuma dal\u012bbnieku atlasi, kuri ir viegli pieejami vai \u0113rti p\u0113tniekam, piem\u0113ram, skol\u0113nu atlase no klases.<\/p>\n\n\n\n

Kvotu atlase:<\/strong> <\/em>Kvotu izlase ir metode, ar kuru atlasi veic, pamatojoties uz iepriek\u0161 noteikt\u0101m kvot\u0101m vai iepriek\u0161 noteiktiem skait\u013ciem p\u0113c konkr\u0113tiem krit\u0113rijiem, piem\u0113ram, vecuma vai dzimuma.<\/p>\n\n\n\n

200+ iepriek\u0161 sagatavotas skaistas veidnes profesion\u0101l\u0101m infografik\u0101m<\/strong><\/h2>\n\n\n\n

Mind the Graph<\/a> platforma ir v\u0113rt\u012bgs resurss zin\u0101tniekiem, kuri v\u0113las uzlabot savu p\u0113t\u012bjumu ietekmi un efekt\u012bvi inform\u0113t par saviem atkl\u0101jumiem. Viena no platformas galvenaj\u0101m funkcij\u0101m ir piek\u013cuve vair\u0101k nek\u0101 200 jau gatav\u0101m, skaist\u0101m veidn\u0113m profesion\u0101l\u0101m infografik\u0101m. \u0160\u012b funkcija \u013cauj p\u0113tniekiem viegli un efekt\u012bvi izveidot satrieco\u0161us savu datu vizu\u0101lus att\u0113lojumus, kas var pal\u012bdz\u0113t piesaist\u012bt m\u0113r\u0137auditorijas uzman\u012bbu un uzlabot p\u0113t\u012bjuma visp\u0101r\u0113jo ietekmi.<\/p>\n\n\n\n

<\/div>\n\n\n
\n
\"\"<\/a><\/figure><\/div>\n\n\n
<\/div>","protected":false},"excerpt":{"rendered":"

V\u0113laties uzzin\u0101t, k\u0101di ir paraugu \u0146em\u0161anas veidi? Turpm\u0101k nemekl\u0113jiet! Padzi\u013cin\u0101ti iepaz\u012bstieties ar da\u017e\u0101d\u0101m datu v\u0101k\u0161an\u0101 izmantotaj\u0101m metod\u0113m.<\/p>","protected":false},"author":35,"featured_media":29203,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[959,28],"tags":[],"yoast_head":"\nHow to Choose the Right Types of Sampling for Your Research - Mind the Graph Blog<\/title>\n<meta name=\"description\" content=\"Want to know what are the types of sampling? Look no further! 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