{"id":36,"date":"2022-11-18T18:55:51","date_gmt":"2022-11-18T17:55:51","guid":{"rendered":"https:\/\/jasp.promind.sk\/?page_id=36"},"modified":"2022-11-23T21:10:19","modified_gmt":"2022-11-23T20:10:19","slug":"pearsonov-sucinovy-korelacny-koeficient","status":"publish","type":"page","link":"https:\/\/jasp.promind.sk\/?page_id=36","title":{"rendered":"Pearsonov s\u00fa\u010dinov\u00fd korela\u010dn\u00fd koeficient (r)"},"content":{"rendered":"\n

<\/p>\n\n\n\n

Defin\u00edcia:<\/strong><\/p>\n\n\n\n

Pod\u013ea Rabu\u0161ica et al. (2019) sa s\u00favislos\u0165 medzi dvoma intervalov\u00fdmi znakmi meria prostredn\u00edctvom jedin\u00e9ho koeficientu – Pearsonovho koeficientu line\u00e1rnej korel\u00e1cie (zna\u010d\u00edme ho p\u00edsmenom r). Vypo\u010d\u00edtame ho prostredn\u00edctvom kovari\u00e1cie, teda spolo\u010dn\u00e9ho rozptylu pre dve premenn\u00e9. Koeficient je symetrick\u00fd, nadob\u00fada hodnoty od -1 do +1 a fakt, \u017ee je to koeficient line\u00e1rnej korel\u00e1cie, znamnen\u00e1, \u017ee dok\u00e1\u017ee zachyti\u0165 a zmera\u0165 len line\u00e1rny vz\u0165ah medzi dvoma premenn\u00fdmi. <\/p>\n\n\n\n

<\/p>\n\n\n\n

Predpoklady pod\u013ea JASP, n.d. a SPSS Statistics Tutorials and Statistical Guides | Laerd Statistics, n.d.:<\/strong><\/p>\n\n\n\n

    \n
  1. Premenn\u00e9 musia by\u0165 nomin\u00e1lne alebo ordin\u00e1lne. V pr\u00edpade, \u017ee bude premenn\u00e1 ordin\u00e1lna, test s \u0148ou bude pracova\u0165 ako s nomin\u00e1lnom a poradie hodn\u00f4t nebude zohr\u00e1va\u0165 \u017eiadnu rolu.<\/li>\n\n\n\n
  2. Premenn\u00e9 musia obsahova\u0165 dve, \u010di viac kateg\u00f3ri\u00ed, teda musia by\u0165 minim\u00e1lne bin\u00e1rne.<\/li>\n\n\n\n
  3. Vo v\u0161etk\u00fdch bunk\u00e1ch kontingen\u010dnej tabu\u013eky by mali by\u0165 o\u010dak\u00e1van\u00e9 po\u010detnosti vy\u0161\u0161ie ako 5.<\/li>\n<\/ol>\n\n\n\n

    Koeficient phi<\/strong> je \u0161tatistick\u00e1 met\u00f3da, ktorou vieme mera\u0165 vz\u0165ah medzi dvoma dichotomick\u00fdmi premenn\u00fdmi<\/strong>. Napr\u00edklad pohlavie (mu\u017e – \u017eena) a typ \u0161portovej aktivity (kolekt\u00edvny – individu\u00e1lny). V\u00fdsledok nadob\u00fada hodnoty v intervale 0 a\u017e 1, kde 0 znamen\u00e1 \u017eiadny vz\u0165ah a 1 \u00fapln\u00fd vz\u0165ah (Han\u00e1k, 2016). Koeficient phi je vhodn\u00e9 po\u010d\u00edta\u0165 iba vtedy, ke\u010f m\u00e1te dve dichotomick\u00e9 premenn\u00e9.<\/p>\n\n\n\n

    Cramerovo V<\/strong> meria vz\u00e1jomn\u00fd vz\u0165ah medzi dvoma premenn\u00fdmi, z ktor\u00fdch jedna \u010di obe maj\u00fa vy\u0161\u0161\u00ed po\u010det kateg\u00f3ri\u00ed ako dve – teda polynomick\u00e9 premenn\u00e9. Podobne ako koeficient phi nadob\u00fada hodnotu od 0 do 1, kde 0 znamen\u00e1 \u017eiadny vz\u0165ah a 1 \u00fapln\u00fd vz\u0165ah (Rabu\u0161ic et al., 2019).<\/p>\n\n\n\n

    Pr\u00edklad:<\/strong><\/p>\n\n\n\n

    Pod t\u00fdmto textom uv\u00e1dzam pr\u00edklad pou\u017eitia Ch\u00ed – kvadr\u00e1t testu a koeficientu phi a Cramerovho V. D\u00e1tov\u00fd s\u00fabor obsahuje tri premenn\u00e9: pohlavie = mu\u017ei a \u017eeny; sport_typ = kolekt\u00edvny a individu\u00e1lny a sport_intenzita = n\u00edzka, stredn\u00e1, vysok\u00e1. Chceme zisti\u0165, \u010di existuje asoci\u00e1cia medzi pohlav\u00edm a typom \u0161portu a tie\u017e \u010di exituje asoci\u00e1cia medzi pohlav\u00edm a intenzitou \u0161portu. Ak by sme z vy\u0161\u0161ie uveden\u00e9ho formulovali v\u00fdskumn\u00e9 ot\u00e1zky, zneli by nasledovne:<\/p>\n\n\n\n

    VO1: Existuje asoci\u00e1cia medzi pohlav\u00edm a typom \u0161portovej aktivity – individu\u00e1lnej, \u010di kolekt\u00edvnej?<\/p>\n\n\n\n

    VO2: Existuje asoci\u00e1cia medzi pohlav\u00edm a intenzitou \u0161portovej aktivity – n\u00edzkou, strednou, vysokou?<\/p>\n\n\n\n

    D\u00e1tov\u00fd s\u00fabor si m\u00f4\u017eete stiahnu\u0165 tu.<\/a><\/strong><\/p>\n\n\n\n

    Postup:<\/strong><\/p>\n\n\n\n

    Z ni\u017e\u0161ie uveden\u00fdch mo\u017enost\u00ed v menu budeme pracova\u0165 s „Descriptives“, t\u00fdm z\u00edskame z\u00e1kladn\u00e9 \u00fadaje popisnej \u0161tatistiky a „Frequencies“, kde zvol\u00edme „Contingency Tables“, \u010d\u00edm z\u00edskame kontingen\u010dn\u00fa (kr\u00ed\u017eov\u00fa) tabu\u013eku.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Vo vo\u013eb\u00e1ch popisnej \u0161tatistiky „Descriptive Statistics“ presunieme premenn\u00fa sport_typ a sport_intenzita do okna „Variables“ a premenn\u00fa pohlavie do okna „Split“, ako je to zobrazen\u00e9 ni\u017e\u0161ie.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    V \u010dasti „Statistics“ \u00faplne sta\u010d\u00ed zvoli\u0165 „Valid“ a „Missing“, \u010d\u00edm dostaneme inform\u00e1cie o po\u010dte platn\u00fdch a ch\u00fdbaj\u00facich hodnot\u00e1ch v d\u00e1tovom s\u00fabore. Zvy\u0161n\u00e9 polo\u017eky nem\u00e1 zmysel zvoli\u0165, ke\u010f\u017ee pracujeme s nomin\u00e1lnymi premenn\u00fdmi.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Presunieme sa do \u010dasti „Basic plots“. Tu zvol\u00edme „Distribution plots“, \u010d\u00edm z\u00edskame st\u013apcov\u00e9 grafy porovnania pohlav\u00ed v \u0161portov\u00fdch aktivit\u00e1ch. Ak zvol\u00edme mo\u017enos\u0165 „Pie charts“, z\u00edskame kol\u00e1\u010dov\u00e9 grafy, vi\u010f ni\u017e\u0161ie.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    V \u010dasti „Tables“ zvol\u00edme mo\u017enos\u0165 „Frekvency tables“, \u010do n\u00e1m vo v\u00fdstupe poskytne \u00fadaje o po\u010dtoch – frekvencii pohlav\u00ed a \u0161portov.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Vo vo\u013eb\u00e1ch kontingen\u010dnej tabu\u013eky „Contingency Tables“ presunieme premenn\u00fa sport_typ do okna „Columns“ a premenn\u00fa pohlavie do okna „Rows“, ako je to zobrazen\u00e9 ni\u017e\u0161ie. Tak vypo\u010d\u00edtame \u0161tatistiky pre VO1.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    \u010ealej otvor\u00edme \u010das\u0165 „Statistics“ a zvol\u00edme „\u03c72<\/sup>“ a „Phi and Cramer\u00b4s V“.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    V \u010dasti „Cells“ zvol\u00edme „Expected“ – to n\u00e1m zobraz\u00ed o\u010dak\u00e1van\u00e9 po\u010detnosti. Budeme teda vo v\u00fdsledku vidie\u0165, ak\u00fd je rozdiel medzi zisten\u00fdmi po\u010detnos\u0165ami (Count) a o\u010dak\u00e1van\u00fdmi po\u010detnos\u0165ami (Expected). M\u00f4\u017eeme zvoli\u0165 aj zobrazenie percent pod „Percentages“. „Row“ pre riadkov\u00e9 percent\u00e1 a „Column“ pre st\u013apcov\u00e9 percent\u00e1 kontingen\u010dnej tabu\u013eky.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Tak ako sme na za\u010diatku anal\u00fdzy zvolili „Frekvencies“ a n\u00e1sledne „Contingency Tables“, teraz to urob\u00edme znova, preto\u017ee chceme zodpoveda\u0165 na VO2, kde sk\u00famame asoci\u00e1ciu medzi pohlav\u00edm a intenzitou \u0161portovej aktivity.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    V novo vytvorenej \u010dasti „Contingency Tables“ presunieme do okna „Rows“ premenn\u00fa pohlavie a do okna „Columns“ premenn\u00fa sport_intenzita.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Podobne, ako sme to robili pri zad\u00e1van\u00ed anal\u00fdzy pre VO1, zvol\u00edme mo\u017enosti aj pre VO2. Teda v \u010dasti „Statistics“ zvol\u00edme „\u03c72<\/sup>“ a „Phi and Cramer\u00b4s V“.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    V \u010dasti „Cells“ zvol\u00edme „Expected“ – to n\u00e1m zobraz\u00ed o\u010dak\u00e1van\u00e9 po\u010detnosti. Budeme teda vo v\u00fdsledku vidie\u0165, ak\u00fd je rozdiel medzi zisten\u00fdmi po\u010detnos\u0165ami (Count) a o\u010dak\u00e1van\u00fdmi po\u010detnos\u0165ami (Expected). M\u00f4\u017eeme zvoli\u0165 aj zobrazenie percent pod „Percentages“. „Row“ pre riadkov\u00e9 percent\u00e1 a „Column“ pre st\u013apcov\u00e9 percent\u00e1 kontingen\u010dnej tabu\u013eky.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    V\u00fdsledok:<\/strong><\/p>\n\n\n\n

    Pod t\u00fdmto textom n\u00e1jdete nieko\u013eko tabuliek. Tabu\u013eka s n\u00e1zvom „Descriptive Statistics“<\/strong> zobrazuje vy\u0161\u0161ie zvolen\u00e9 hodnoty popisnej \u0161tatistiky. Zvolili sme si len zobrazenie „Valid“ (platn\u00fdch) a „Missing“ (ch\u00fdbaj\u00facich) hodn\u00f4t. M\u00f4\u017eeme vidie\u0165, \u017ee v\u0161etky hodnoty s\u00fa platn\u00e9 pre mu\u017eov aj \u017eeny v premenn\u00fdch sport_typ a sport_intenzita. Ak by v niektorej z buniek d\u00e1tovej tabu\u013eky nebola \u010d\u00edseln\u00e1 hodnota ale zostala by pr\u00e1zdna, videli by sme, \u017ee hodnota „Missing“ by mala in\u00fa ako nulov\u00fa hodnotu.<\/p>\n\n\n\n

    \u010eal\u0161ie tabu\u013eky pod t\u00fdmto textom s\u00fa „Freqency Tables“<\/strong>, teda frekven\u010dn\u00e9 tabu\u013eky s po\u010dtami hodn\u00f4t a percent pre jednotliv\u00e9 premenn\u00e9. Vrchn\u00e1 tabu\u013eka je „Frequencies form sport_typ“ – teda po\u010dty pre typ \u0161portovej aktivity u mu\u017eov a \u017eien, ktor\u00e9 maj\u00fa dve kateg\u00f3rie – kolekt\u00edvny a individu\u00e1lny. Pop\u00ed\u0161me si prv\u00fa tabu\u013eku. M\u00f4\u017eeme z nej vy\u010d\u00edta\u0165, \u017ee mu\u017eov, ktor\u00ed sa venuj\u00fa kolekt\u00edvnemu \u0161portu je 38 (Frekvency = po\u010det\/po\u010detnos\u0165), \u010di je 70,370% z celkov\u00e9ho po\u010dtu mu\u017eov. Mu\u017eov, ktor\u00ed sa venuj\u00fa individu\u00e1lnemu \u0161portu je 16, \u010do tvor\u00ed 29,630% z celkov\u00e9ho po\u010dtu mu\u017eov. \u017dien, ktor\u00e9 sa venuj\u00fa kolekt\u00edvnemu \u0161portu je vo v\u00fdskumnom s\u00fabore 28, \u010do tvor\u00ed 41,176% po\u010dtu \u017eien a individu\u00e1lnemu \u0161portu sa venuje 40 \u017eien, \u010do je 58,824%. Obdobne by sme vedeli pop\u00edsa\u0165 aj tabu\u013eku ni\u017e\u0161ie „Frequencies form sport_intenzita“. St\u013apec „Valid Percent“ n\u00e1m hovor\u00ed o percente platn\u00fdch \u00fadajov. V na\u0161om pr\u00edpade je toto \u010d\u00edslo rovnak\u00e9 ako v st\u013apci „Percent“. Ak by sme mali ale v d\u00e1tovom s\u00fabore ch\u00fdbaj\u00face hodnoty (Missing), hodnota „Valid Percent“ by sa od hodnoty „Percent“ l\u00ed\u0161ila. Posledn\u00fd st\u013apec „Cumulative percent“ je s\u00fa\u010det percentu\u00e1lnych hodn\u00f4t predch\u00e1dzaj\u00facich kateg\u00f3ri\u00ed. V tabu\u013eke „Frequencies form sport_typ“ m\u00f4\u017eeme pomocou „Cumulative percent“ napr. vidie\u0165, \u017ee mu\u017eov, ktor\u00fdch intenzita \u0161portovania je n\u00edzka a stredn\u00e1 je 66,667%. \u017deny s n\u00edzkou a strednou intenzitou \u0161portovania tvoria 85,294% v\u0161etk\u00fdch \u017eien.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Pod t\u00fdmto textom n\u00e1jdeme st\u013apcov\u00e9 grafy<\/strong> premennej sport_typ a pod nimi premennej sport_intenzita rozdelen\u00e9 pod\u013ea pohlavia. V anal\u00fdze sme si ich volili v menu „Basic plots“.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Pod t\u00fdmto textom n\u00e1jdeme kol\u00e1\u010dov\u00e9 grafy<\/strong> (Pie Charts) premennej sport_typ a pod nimi premennej sport_intenzita rozdelen\u00e9 pod\u013ea pohlavia. V anal\u00fdze sme si ich volili tie\u017e v menu „Basic plots“.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Pod t\u00fdmto textom m\u00f4\u017eeme vidie\u0165 „Contingency Tables“<\/strong> – kontingen\u010dn\u00e9\/kr\u00ed\u017eov\u00e9 tabu\u013eky pre premenn\u00e9 sport_typ a pohlavie a pre premenn\u00e9 sport_intenzita a pohlavie. Op\u00e4\u0165 si m\u00f4\u017eeme prv\u00fa z nich pop\u00edsa\u0165. Vid\u00edme, \u017ee 38 mu\u017eov (57,576% venuj\u00facich sa kolekt\u00edvnemu \u0161portu) sa venuje kolekt\u00edvnemu \u0161portu, pri\u010dom o\u010dak\u00e1van\u00e1 (Expected count) hodnota je 29,213. Vid\u00edme, \u017ee v na\u0161ej v\u00fdskumnej vzorke sme zistili o takmer 9 mu\u017eov viac venuj\u00facich sa kolekt\u00edvnemu \u0161portu, ako by bolo o\u010dak\u00e1van\u00e9. Ak pokra\u010dujeme v interpret\u00e1cii kolekt\u00edvneho \u0161portu, vid\u00edme \u017ee v na\u0161ej v\u00fdskumnej vzorke sa 28 \u017eien (42,424% venuj\u00facich sa kolekt\u00edvnemu \u0161portu) venuje kolekt\u00edvnemu \u0161portu. O\u010dak\u00e1van\u00fd po\u010det je v tomto pr\u00edpade 36,787. V na\u0161ej vzorke sa teda nach\u00e1dza o takmer 9 \u017eien venuj\u00facich sa kolekt\u00edvnemu \u0161portu menej ako by sme o\u010dak\u00e1vali. Podobne interpretujeme individu\u00e1lne \u0161porty. Vid\u00edme, \u017ee 16 mu\u017eov (28,571% venuj\u00facich sa individu\u00e1lnemu \u0161portu) sa venuje individu\u00e1lnemu \u0161portu, \u010do je o viac ako 8 menej, ako je hodnota o\u010dak\u00e1vanej po\u010detnosti 24,787. \u017dien, venuj\u00facich sa individu\u00e1lnemu \u0161portu je vo v\u00fdskumnej vzorke 40 (71,429% venuj\u00facich sa individu\u00e1lnemu \u0161portu), \u010do je o takmer 9 viac, ako je o\u010dak\u00e1van\u00e1 po\u010detnos\u0165 31,213. Vid\u00edme teda, \u017ee o\u010dak\u00e1van\u00e9 po\u010detnosti sa od nami zisten\u00fdch l\u00ed\u0161ia vo v\u0161etk\u00fdch bunk\u00e1ch tabu\u013eky.<\/p>\n\n\n\n

    \u010eal\u0161ou tabu\u013ekou, pod t\u00fdmto textom je „Chi – Square Tests“<\/strong> – tabu\u013eka s hodnotou Pearsonov \u03c72<\/sup> testu, ktor\u00fd hovor\u00ed o tom, \u010di existuje asoci\u00e1cia medzi analyzovan\u00fdmi nomin\u00e1lnymi premenn\u00fdmi, v na\u0161om pr\u00edklade medzi pohlav\u00edm (mu\u017e, \u017eena) a typom \u0161portovej aktivity (kolekt\u00edvny, individu\u00e1lny). Pre rozhodnutie o tom, \u010di asoci\u00e1cia medzi premenn\u00fdmi existuje alebo nie sl\u00fa\u017ei hodnota p<\/strong>, teda signifikancie. V na\u0161om pr\u00edpade je p = 0,001. Je teda ni\u017e\u0161ie ako zvy\u010dajne stanoven\u00e1 \u00farove\u0148 signifikancie 0,05. Prij\u00edmame z\u00e1ver (alternat\u00edvnu hypot\u00e9zu) o tom, \u017ee existuje asoci\u00e1cia medzi analyzovan\u00fdmi nomin\u00e1lnymi premenn\u00fdmi pohlavie a typ \u0161portu. Ak by hodnota p bola vy\u0161\u0161ia ako 0,05, z\u00e1ver by znel opa\u010dne, teda neexistuje asoci\u00e1cia medzi analyzovan\u00fdmi premenn\u00fdmi.<\/p>\n\n\n\n

    \u010eal\u0161ou tabu\u013ekou s n\u00e1zvom „Nominal“<\/strong> je v\u00fdsledok v\u00fdpo\u010dtu koeficientu phi <\/strong>a Cramerovho V. Ako je vy\u0161\u0161ie uveden\u00e9, pri anal\u00fdze dvoch bin\u00e1rnych premenn\u00fdch, ako je to v tomto pr\u00edpade pohlavie a typ \u0161portovej aktivity, kde vznik\u00e1 kontingen\u010dn\u00e1 tabu\u013eka 2×2 interpretujeme koeficient phi<\/strong>. Cramerovo V v tomto pr\u00edpade nadob\u00fada rovnak\u00fa hodnotu ako phi ale neinterpretujeme ho. Koeficient phi podobne ako Cramerovo V interpretujeme v troch \u00farovniach, hladin\u00e1ch ako: n\u00edzky od 0 do 0,29; stredn\u00fd do 0,30 do 0,49 a ve\u013ek\u00fd viac ako 0,50 <\/strong>(Cohen, 1988).<\/strong> V na\u0161om pr\u00edpade je sila asoci\u00e1cie medzi pohlav\u00edm a typom \u0161portovej aktivity vyjadren\u00e1 koeficientom phi rovn\u00e1 0,291, \u010do predstavuje n\u00edzku mieru asoci\u00e1cie.<\/p>\n\n\n\n

    <\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Pod t\u00fdmto textom je op\u00e4\u0165 „Contingency Tables“<\/strong> – kontingen\u010dn\u00e1\/kr\u00ed\u017eov\u00e1 tabu\u013eka pre premenn\u00e9 sport_intenzita a pohlavie. Pop\u00edsa\u0165 ju mo\u017eno podobne, ako je uveden\u00e9 vy\u0161\u0161ie.<\/p>\n\n\n\n

    \u010eal\u0161ou tabu\u013ekou, pod t\u00fdmto textom je „Chi – Square Tests“<\/strong> – tabu\u013eka s hodnotou Pearsonov \u03c72<\/sup> testu, ktor\u00fd hovor\u00ed o tom, \u010di existuje asoci\u00e1cia medzi analyzovan\u00fdmi nomin\u00e1lnymi premenn\u00fdmi, v na\u0161om pr\u00edklade medzi pohlav\u00edm (mu\u017e, \u017eena) a intenzita \u0161portovej aktivity (n\u00edzka, stredn\u00e1, vysok\u00e1). Pre rozhodnutie o tom, \u010di asoci\u00e1cia medzi premenn\u00fdmi existuje alebo nie sl\u00fa\u017ei hodnota p<\/strong>, teda signifikancie. V na\u0161om pr\u00edpade je p = 0,044. Je teda ni\u017e\u0161ie ako zvy\u010dajne stanoven\u00e1 \u00farove\u0148 signifikancie 0,05. Prij\u00edmame z\u00e1ver (alternat\u00edvnu hypot\u00e9zu) o tom, \u017ee existuje asoci\u00e1cia medzi analyzovan\u00fdmi nomin\u00e1lnymi premenn\u00fdmi pohlavie a intenzitou \u0161portu. Ak by hodnota p bola vy\u0161\u0161ia ako 0,05, z\u00e1ver by znel opa\u010dne, teda neexistuje asoci\u00e1cia medzi analyzovan\u00fdmi premenn\u00fdmi.<\/p>\n\n\n\n

    \u010eal\u0161ou tabu\u013ekou s n\u00e1zvom „Nominal“<\/strong> je v\u00fdsledok v\u00fdpo\u010dtu koeficientu phi <\/strong>– kde je uveden\u00e1 hodnota NaN, preto\u017ee koeficient phi sa v tabu\u013eke v\u00e4\u010d\u0161ej ako 2×2 ned\u00e1 vypo\u010d\u00edta\u0165. Tu n\u00e1s zauj\u00edma hodnota Cramerovho V<\/strong>, ktor\u00e1 hovor\u00ed o sile asoci\u00e1cie mezdi premennou pohlavie a premennou intenzita \u0161portovej aktivity. Dosahuje hodnotu 0,227 a interpretujeme ju pod\u013ea hlad\u00edn podobne, ako koeficient phi: n\u00edzky od 0 do 0,29; stredn\u00fd do 0,30 do 0,49 a ve\u013ek\u00fd viac ako 0,50 <\/strong>(Cohen, 1988). V na\u0161om pr\u00edpade je sila asoci\u00e1cie medzi pohlav\u00edm a intenzitou \u0161portovej aktivity vyjadren\u00e1 Cramerov\u00fdm V rovn\u00e1 0,227, \u010do predstavuje n\u00edzku mieru asoci\u00e1cie.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Ako v\u00fdsledok zap\u00edsa\u0165:<\/strong><\/p>\n\n\n\n

    VO1: Existuje asoci\u00e1cia medzi pohlav\u00edm a typom \u0161portovej aktivity \u2013 individu\u00e1lnej, \u010di kolekt\u00edvnej?<\/p>\n\n\n\n

    Realizovali sme test Pearonovho \u03c72<\/sup>, aby sme zistili, \u010di existuje asoci\u00e1cia medzi pohlav\u00edm a typom \u0161portovej aktivity – individu\u00e1lnej, \u010di kolekt\u00edvnej. V\u0161etky o\u010dak\u00e1van\u00e9 po\u010detnosti vo kontingen\u010dnej tabu\u013eke boli vy\u0161\u0161ie ako p\u00e4\u0165. Medzi pohlav\u00edm a v\u00fdberom typu \u0161portovej aktivity existuje \u0161tatisticky v\u00fdznamn\u00e1 asoci\u00e1cia \u03c7 2<\/sup> (1) = 10,330, p = 0,001, pri\u010dom mieru tejto asoci\u00e1cie m\u00f4\u017eeme hodnoti\u0165 ako slab\u00fa \u03c6 = 0,291.<\/em><\/p>\n\n\n\n

    VO2: Existuje asoci\u00e1cia medzi pohlav\u00edm a intenzitou \u0161portovej aktivity \u2013 n\u00edzkou, strednou, vysokou?<\/p>\n\n\n\n

    Realizovali sme test Pearonovho \u03c72<\/sup>, aby sme zistili, \u010di existuje asoci\u00e1cia medzi pohlav\u00edm a intenzitou \u0161portovej aktivity – n\u00edzkou, strednou, vysokou. V\u0161etky o\u010dak\u00e1van\u00e9 po\u010detnosti vo kontingen\u010dnej tabu\u013eke boli vy\u0161\u0161ie ako p\u00e4\u0165. Medzi pohlav\u00edm a intenzitou \u0161portovej aktivity existuje \u0161tatisticky v\u00fdznamn\u00e1 asoci\u00e1cia \u03c7 2<\/sup> (1) = 6,270, p = 0,044, pri\u010dom mieru tejto asoci\u00e1cie m\u00f4\u017eeme hodnoti\u0165 ako slab\u00fa \u03c6 = 0,227.<\/em><\/p>\n\n\n\n

    Pou\u017eit\u00e1 literat\u00fara:<\/strong><\/p>\n\n\n\n

    Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd Edition)<\/em> (2nd ed.). Routledge.<\/p>\n\n\n\n

    Han\u00e1k, R. (2016). D\u00e1tov\u00e1 anal\u00fdza pre soci\u00e1lne vedy<\/em>. Vydavate\u013estvo Ekon\u00f3m.<\/p>\n\n\n\n

    JASP. (n.d.). Study with us. University of Greenwich<\/em>. https:\/\/gala.gre.ac.uk\/id\/eprint\/25585\/<\/a><\/p>\n\n\n\n

    Rabu\u0161ic, L., Soukup, P., & Mare\u0161, P. (2019). Statistick\u00e1 anal\u00fdza soci\u00e1ln\u011bv\u011bdn\u00edch dat (prost\u0159ednictv\u00edm SPSS)<\/em>. Masarykova univerzita.<\/p>\n\n\n\n

    SPSS Statistics Tutorials and Statistical Guides | Laerd Statistics<\/em>. (n.d.). https:\/\/statistics.laerd.com\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    Defin\u00edcia: Pod\u013ea Rabu\u0161ica et al. (2019) sa s\u00favislos\u0165 medzi dvoma intervalov\u00fdmi znakmi meria prostredn\u00edctvom jedin\u00e9ho koeficientu – Pearsonovho koeficientu line\u00e1rnej korel\u00e1cie (zna\u010d\u00edme ho p\u00edsmenom r). Vypo\u010d\u00edtame ho prostredn\u00edctvom kovari\u00e1cie, teda spolo\u010dn\u00e9ho rozptylu pre dve premenn\u00e9. Koeficient je symetrick\u00fd, nadob\u00fada hodnoty od -1 do +1 a fakt, \u017ee je to koeficient line\u00e1rnej korel\u00e1cie, znamnen\u00e1, \u017ee dok\u00e1\u017ee […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_mi_skip_tracking":false},"yoast_head":"\nPearsonov s\u00fa\u010dinov\u00fd korela\u010dn\u00fd koeficient (r) - JASP - \u0161tatistick\u00e9 spracovanie d\u00e1t<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jasp.promind.sk\/?page_id=36\" \/>\n<meta property=\"og:locale\" content=\"sk_SK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pearsonov s\u00fa\u010dinov\u00fd korela\u010dn\u00fd koeficient (r) - JASP - \u0161tatistick\u00e9 spracovanie d\u00e1t\" \/>\n<meta property=\"og:description\" content=\"Defin\u00edcia: Pod\u013ea Rabu\u0161ica et al. 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(2019) sa s\u00favislos\u0165 medzi dvoma intervalov\u00fdmi znakmi meria prostredn\u00edctvom jedin\u00e9ho koeficientu – Pearsonovho koeficientu line\u00e1rnej korel\u00e1cie (zna\u010d\u00edme ho p\u00edsmenom r). Vypo\u010d\u00edtame ho prostredn\u00edctvom kovari\u00e1cie, teda spolo\u010dn\u00e9ho rozptylu pre dve premenn\u00e9. 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