Práce s daty, missingy, outliery

„Abraháme!“ „No tak, Abraháme! Probudil vás naléhavý hlas a bušení na dveře. //Ještě že tak, ten sen byl nanejvýš podivný,// řeknete si v duchu. Vaše práce v poslední době sklízí spíše posměch než ocenění a dokonce se ve zdech Akademie začíná mluvit o konci jedné kdysi zářné kariéry. Ohlédnete se a vidíte, jak za vámi po chodbě spěchá kolega. Jeho jméno si najednou nevybavujete. „Abraháme, no konečně.“ Když jste otevřel dveře, našel jste za nimi jednoho ze svých kolegů. Taky výzkumník, ale kdybyte měl říct čemu se věnuje, nevěděl byste. I na jméno byste musel dlouho vzpomínat. Ale co, máte teď dost jiných starostí. "Abraháme," promluví onen kolega, "máte hned jít za ředitelem.“ „Jasně, hned tam jdu, díky.“ //A to jsou přesně ty moje starosti. Vyhodí mě. Bylo nám ctí, ale už tu pro vás nemáme místo. Sbohem.// V doprovodu podobných myšlenek dojdete až před dveře ředitelovy kanceláře. Tam se zastavíte. Upravíte si kravatu a uhladíte vlasy na pravé straně. Spíš ze zvyku, než že by se tím skutečně něco změnilo. Až potom se odhodláte zaklepat. „Dobrý den, Abraháme,“ uvítá vás ředitel, když vstoupíte. „Pojďte se prosím posadit. Asi tušíte, proč jsem si vás nechal zavolat. Vaše práce nám za posledních několik měsíců nepřinesla žádná pozitiva. Akademie se spíše stává terčem posměchu a o vás se mluví jako o člověku, jehož vědecká sláva dávno zapadala prachem. Dělám to nerad, ale jsem nucen v tomto ohledu něco podniknout.“ //Děkujeme, na shledanou!// „Dostali jsme návrh na velký výzkum. Trochu to spěchá, ale vše důležité je v téhle modré složce. Chci, abyste se toho ujal vy. Úspěch či neúspěch rozhodne o vašem setrvání v Akademii. Vezměte si tu složku, proveďte kvalitní výzkum, publikujte!“ [[Ne, pane řediteli, nechci být dál vysmíván a dál kazit pověst Akademie. Raději odejdu se vztyčenou hlavou.“->2 - Ne pane řediteli]] [[Vzít modrou složku a odejít do své pracony->6 - Základní zadání]]

„Abraháme, jste si tím jistý? Je vám jasné, že to znamená odchod z Akademie?“ „Ano, vím. Přemýšlel jsem o tom a myslím, že je na čase vyklidit pole.“ „No dobrá, v tom případě mi bylo ctí s vámi pracovat. Doufám, že se potkáme někdy někde jinde.“ Potom, co jste opustil ředitelovu kancelář, opustil jste Akademii a došel domů. Tam jste dlouhou dobu prostě jenom seděl na gauči a přemýšlel co dál. Nakonec jste začal přebírat věci, které jste si přinesl z teď už bývalého zaměstnání. Hrneček na čaj, zarámovaný diplom, nějaké knihy. Strukturální modelování, Kvalitativní analýza a tak dále. Nakonec vám v ruce zůstanou „Pokročilé postupy řešení statistických problémů“. Lehnete si na gauči a začnete knihou listovat. [[Vzteky zahodit knihu->3 - Vzteky zahodit knihu]] [[Číst v //"Pokročilých postupech"//->4 - Celá kniha]]

Sedíte za stolem ve své pracovně. Modrou složku máte položenou před sebou a zíráte na ni, jako byste mohl prohlédnout skrz její desky. Otevřít jste se ji zatím neodhodlal. „Takže poslední možnost zachránit si kariéru. No dobrá, tak se do toho pustíme.“ Po otevření složky zjistíte, že se jedná o plán výzkumu na velkém množství lidí napříč populací celého Státu. Zdá se, že celý výzkum je rozdělen do několika částí, přičemž do vašich rukou se dostala pouze jedna z nich. Samotný sběr dat zajišťuje Institut, jehož kontaktní adresu máte ve složce uvedenou. Vaší povinností je však na tuto adresu doručit materiály, pomocí kterých bude Institut data sbírat. Na další straně dokumentace je specifikován účel vašeho výzkumu. Klade si za cíl zodpovědět na tři otázky: a) (text-style:"italic")[Zda se lidé různých věkových kategorií liší ve schopnosti rozpoznávat spolehlivé mediální zprávy od nespolehlivých.] V této souvislosti složka obsahuje také kvalitně zpracovanou rešerši literatury, ze které vyplývá, že v současné době vědecké důkazy napovídají, že co do schopnosti rozpoznávat spolehlivost mediálních zpráv lze populaci rozdělit do tří věkových skupin: 15 – 22 let, 23 – 60 let a 60 a více let. Tyto tři skupiny obyvatel by se měly od sebe vzájemně lišit. Populace do 15 let věku úmyslně není zahrnuta, neboť se předpokládá, že není dostatečně zasažena mediálním životem. Je zde také přiložen již hotový testový sešit, obsahující různé zprávy, u kterých má respondent rozhodovat zda jsou či nejsou spolehlivé. Vyšší dosažený skór indikuje lepší schopnost rozpoznávat spolehlivé zprávy. b) (text-style:"italic")[Zda se u lidí liší jejich ochota k veřejnému vystupování v závislosti na výši jejich čistého příjmu. ] Teorie předpokládá, že lidé s vyššími příjmy budou ochotnější k vystupování na veřejnosti, neboť jejich bohatství jim dává pocit důležitosti, a také schopnosti ovlivňovat mínění davu. I k této výzkumné otázce je přiložen dotazník hodnotící ochotu respondenta k veřejnému vystupování. Vyšší skór zde indikuje vyšší ochotu k takovému vystupování. c) (text-style:"italic")[Zda se liší muži a ženy v úrovni projevované agrese vůči vládě Státu.] I k této výzkumné otázce je přiložen dotazník, hodnotící projevovanou agresivitu vůči vládě. Čím vyšší je dosažený skór, tím více agresivity daný respondent projevuje. „Zdá se, že pro první fázi je tady už všechno připravené,“ říkáte si. „Stačí tyhle připravené materiály vzít a poslat je na adresu Institutu. Oni se už postarají o zbytek.“ [[Vzít připravené dotazníky a odeslat je do Institutu->7 - Data 0]] [[Shrnout si, co je obsahem výzkumu->8 - Shrnutí]] [[Znovu projít modrou složku->9 - Postupovat rozvážně]]

„Tohle je mi k ničemu!“ Kniha letí přes pokoj a otevřená zůstane ležet u stěny, do které narazila. Od té chvíle Abrahám upadl do apatie. Zmizela všechna motivace k práci a k čemukoliv dalšímu. Snad to brzo přejde a najde si jiné místo. Přejme mu, aby se zase dostal k vědecké činnosti, která pro něj je tak důležitá. Odmítl jste svoji šanci a tak vaše cesta skončila velmi záhy. [[Vyzkoušet novou cestu->1 - Postava]] [[Zvrátit poslední volbu->2 - Ne pane řediteli]]

(text-style:"bold","shadow")[Pokročilé postupy řešení statistických problémů] (text-style:"italic")[Práce s chybějícími hodnotami] (text-style:"underline")[Předmluva] V ideálním světě, kde výzkumníkům vždy vše hraje pěkně do karet zůstávají naše p-hodnoty nízké, věcné významnosti vysoké a naše datové soubory vždy bezchybné a kompletní. Bohužel, nežijeme v ideálním výzkumnickém světě, a tak se se všemi těmito problémy musíme v naší výzkumnické praxi neustále potýkat. V této kapitole se zaměříme na poslední zmíněný problém, tedy chybějící hodnoty. Chybějící hodnoty lze rozdělit do několika typů, přičemž od konkrétního typu se pak odvíjí různé způsoby práce s nimi. Prvním typem je Missing by definition of the subpoppulation, nebo by se to také dalo nazvat „Hodnota chybějící z definice.“ K může docházet například u velkých výzkumných souborů, kde se setkávají různé podskupiny (subpopulace), u kterých nás pak zajímají rozdílné věci. Například, pokud se zajímám o prožívání po dobu těhotenství, žen se budu ptát na prožívání vlastního těhotenství. Mužů se pak budu ptát na prožívání těhotenství jejich partnerek. Z toho logicky vyplývá, že u mužů zaznamenám chybějící hodnoty u otázek na vlastní těhotenství a u žen pak chybějící hodnoty u otázek na těhotenství jejich partnerky (v tomto příkladu jsou záměrně vynechána registrovaná partnerství). Druhým typem chybějících hodnot je Missing completely at random (MCAR). V tomto případě jsou chybějící hodnoty roztroušeny napříč datovým souborem zcela náhodně a nezávisle na jakémkoliv ukazateli. Představíme-li si datový soubor jako matici dat a zvýrazníme v ní chybějící hodnoty, v tomto případě zde nebude možné odhalit jakýkoliv vzorec. Třetím typem je Missing at random (MAR). Sem spadají takové chybějící hodnoty, u kterých lze pozorovat mechanismus, nicméně ten mechanismus nesouvisí přímo se zjišťovanou proměnnou. Například, když lidé neodpoví na položky týkající se depresivity, nicméně mechanismem, který za tím lze najít není samotná úroveň depresivity, ale například jejich rodinná situace. Tedy, že lidé ve špatné rodinné situaci spíše neodpovídají na otázky o depresivitě než lidé kteří jsou v dobré rodinné situaci. Posledním typem je Missing not at random (MNAR), kdy neochota odpovídat na konkrétní položku přímo souvisí s konceptem, který daná položka zjišťuje. Například když se v datovém souboru ukáže, že lidé trpící depresí neodpovídají na položky ohledně vlastní depresivity. Způsoby, jak se s chybějícími hodnotami vypořádat lze rozdělit na tzv. tradiční a moderní. Mezi tradiční způsoby patří Case delation, Pairwise delation, Mean substitution a Indicator Adjustment. Mezi moderní se pak řadí Simple imputation, Multiple imputation nebo Full imformation maximum likelihood approach. Case delation je nejběžnější metoda řešení problému chybějících hodnot. Jedná se o vyřazení všech nekompletních záznamů ze vzorku. To může vést ke ztrátě 20% - 50% datového souboru a tím pádem ke značnému omezení statistické síly. U rozsáhlých vzorků však je tento problém do značné míry zanedbatelný. Další komplikací je však typ chybějící hodnoty. Case delation je vhodné použít především u MCAR missingů. Jestliže se však chybějící hodnoty ve vzorku řídí nějakým pravidlem, hrozí, že tento postup dá nakonec vzniknout nereprezentativnímu vzorku (například pokud by lidé žijící na hranici chudoby odmítli uvést svůj měsíční příjem, case delation těchto případů z výzkumu vyřadí tuto část populace. Pairwaise delation je postup, který do zpracování zařazuje všechny, kteří odpověděli na položky, kterých se analýza týká, nehledě na to, že u jiných položek, které v tu chvíli nejsou předmětem analýzy, mají chybějící hodnoty. Například, pokud chci srovnat věkové skupiny z hlediska jejich asertivity, vezmu do analýzy všechny respondenty, u kterých mám k dispozici jak věk tak míru asertivity bez ohledu na to, že u některých z nich nemám k dispozici míru well-beingu. Stejně tak pokud chci srovnat věkové skupinyz hlediska well-beingu, neberu v úvahu, zda u některých respondentů chybí uvedení míry asertivity. Tento postup zachovává co největší množství respondentů a tedy co největší statistickou sílu, je zde však problém, že vzorek pro zpracování každé jednotlivé analýzy je pak odlišný a nelze tudíž jednotlivé kroky slučovat. V tomto případě by tedy nebylo možné studovat interakce věku a asertivity z hlediska prožívaného wellbeingu. Tento postup by vyžadoval Case delation. Mean substitution je postup, který nahrazuje chybějící hodnoty průměrem vzorku v dané proměnné. Jedná se o logický postup, jestliže lze předpokládat, že daná proměnná ve vzorku odpovídá normálnímu rozložení. V takovém případě, neznáme-li hodnotu dané proměnné u konkrétní osoby, nejpravděpodobnější je, že tato hodnota se pohybuje blízko kolem průměru. Tento postup je však problematický, pokud v konkrétním vzorku je velké množství chybějících hodnot. Jestliže ve vzorku chybí například 30% hodnot, pak nahrazení všech těchto hodnot průměrem značně redukuje rozptyl ve vzorku, a tím pádem podhodnocuje hodnotu korelace. Dalším problémem je necitlivost takového postupu vůči extrémním hodnotám. Jestliže mi u respondenta chybí jeho měsíční příjem a tento člověk je buďto milionář nebo se naopak potácí na hranici chudoby, pak v obou těchto případech je nahrazení této chybějící hodnoty průměrem velmi nešťastné a povede ke zkreslení výsledků. Pomocí Mean substitution není také vhodné doplňovat z definice chybějící hodnoty. Indicator adjustment je lehká úprava strategie doplňování průměru. V tomto případě se do datového souboru přidá proměnná, tzv. indikátor, který nabývá hodnoty 0 v případech, kdy cílová hodnota chybí a 1 v případech, kdy je cílová hodnota pozorována. Následně jsou chybějící hodnoty doplněny průměrem vzorku. Přítomnost indikátoru zde udržuje informaci o počtu doplněných hodnot a tím pádem možného rozsahu zkreslení. Více o tomto postupu například (Cohen and Cohen 1983) a následně (Cohen, Cohen, West, and Aiken 2003). Shrneme-li jednoduše vlastnosti tradičních přístupů, žádný z nich neposkytuje ideální řešení. Jejich použitelnost vždy závisí na konkrétních okolnostech. K moderním přístupům, které se snaží některé tyto nedostatky kompenzovat, patří Simple imputation postup, který vytvoří nový dataset, kde jsou všechny chybějící hodnoty doplněny metodou maximální pravděpodobnosti. Tento přístup je založen na ve vzorku obsažených vztazích mezi všemi proměnnými do kterého vstupuje určitý stupeň náhodných chyb. Tento postup však podhodnocuje standardní chybu měření a tedy nadhodnocuje přesnost. Tento problém řeší Multiple imputation, postup který vytvoří 5 – 10 datových souborů různým doplněním. Analýza je pak provedena na všech těchto souborech a uchovává všechny hodnoty parametrů i standardních chyb. Pak je možné použít průměrné hodnoty. Full information maximum likelihood approach nepoužívá doplnění chybějících hodnot, ale využívá všechny dostupné informace ze vzorku, aby metodou maximální věrohodnosti poskytl co nejpřesnější odhady chybějících parametrů. Tyto pokročilé moderní metody jsou ve srovnání s tradičními přesnější, nicméně výpočetně náročnější. Existují však softwary, které použití těchto přístupů umožňují. Jedná se například o NORM, ale Simple imputation umí třeba i SPSS. Více o práci s chybějícími hodnotami i o zmíněných softwarech (Acock, 2005). (text-style:"italic")[Práce s outliery] (text-style:"underline")[Předmluva] Mluvím-li o outlierech, mám na mysli pozorované hodnoty měření, které se výrazně liší od ostatních hodnot zjištěných ve vzorku. Jestliže Vám, vážený čtenáři, tato definice připadá vágní pak vězte, že je tomu opravdu tak. Na to, co je to vlastně outlier, jak ho poznat a jak pak s takovým záznamem naložit existují desítky různých názorů. A to na každou ze zmíněných tří věcí. Následující stránky pokrývají část možných variant. Přidržíme se předpokladu, že outlier znamená nepřiměřeně vysokou, či nepřiměřeně nízkou hodnotu konkrétního parametru ve srovnání s ostatními hodnotami stejné proměnné napříč datovým souborem. Pro další uvažování je důležité určit, co takovou nepřiměřenou hodnotu parametru způsobilo. První, nejběžnější a nejlépe řešitelná varianta je, že tato hodna je způsobená prostou chybou v přepisu dat. K tomu může snadno docházet při manuálním přepisu fyzických dat to elektronické podoby. Řešením je samozřejmě kontrola původních dat a korekce takových chybových hodnot. Druhým typem chyby, která vede k přítomnosti outlierů v datech jsou chyby na straně respondenta, které mohou plynout z nepochopení zadání, špatné interpretaci odpověďové škály a tak podobně. V tomto případě nezbývá, než takové hodnoty vyloučit z analýzy, protože jejich zachování by vedlo ke zkreslení výsledků. Třetí zdroj outlierů lze shrnout jako netypické projevy. Zde již narážíme na všudypřítomný problém „sto lidí sto názorů.“ Netypické projevy samozřejmě zahrnují některé jednotlivce, kteří ve srovnání s ostatními jednoduše dosahují v konkrétní proměnné extrémních hodnot. Je však popisována řada definic outlierů, některé odvozené od konkrétních statistických postupů. Od toho se potom odvíjí jak způsoby identifikace takových případů tak způsoby nakládání s nimi. Samozřejmě, existuje také přístup, který nenakládá s outliery jako s problémem, který musí být nutně vyřešen, ale jako se zdrojem zajímavého druhu informací. Takový zdroj může v některých případech mnoho napovědět. Dalo by se říct, že studiem výjimek se nejvíce dovídáme o pravidlech. Tento způsob práce samozřejmě vyžaduje individuální přístup a studium různých příčin u každého jednotlivého respondenta klade vysoké nároky jak na čas, tak na úsilí. Je nicméně pravda, že i korekce chybových dat vyžaduje zvýšené úsilí a individuální přístup ke každé hodnotě, kterou lze považovat za outlier. Může se zdát, že v případě outliery zatížených dat máme k dispozici dvě možnosti. První možností je investovat mnoho času a úsilí k individuálnímu posouzení každé takové hodnoty, což pravděpodobně povede k jejich redukci, či k tomu, že se z těchto hodnot dozvíme něco, co z celkového obrazu není patrné. Druhá možnost znamená plošné řešení outlierových hodnot. Zde buď zahrneme takové hodnoty do analýz nebo je z nich vyloučíme. V prvním případě riskujeme značně zkreslené výsledky, zatímco v druhém případě riskujeme ztrátu důležitých informací a nereprezentativnost takových výsledků. Také riskujeme nařčení ze strany oponentů, že manipulujeme s daty záměrně takovým způsobem, abychom získali výsledky, které se nám, abych tak řekl, hodí do krámu. V každém případě jsou tyto možnosti značně omezující. Proto někteří výzkumníci aplikují třetí možnost. Tou třetí možností je publikování dvojích výsledků, jak včetně outlierů tak bez nich. Takový postup lze označit za sebeprotektivní, protože přesouvá část zodpovědnosti za interpretaci na čtenáře. Je to však způsob, jak se vyhnout náročné individuální analýze i problémům plynoucím z plošných řešení. To je jen plošné shrnutí toho, co lze říct o práci s outliery. V tomto ohledu důrazně doporučuji nahlédnout do článku autorů Hermana Aguinise, Ryana K. Gottfredsona a Harryho Joo, publikovaného v roce 2013, který shrnuje 14 definic otlierů, 39 způsobů jejich identifikace a 20 postupů nakládání s nimi. Některé jsou poměrně komplikované, takže se i já v tomto místě zbavím části interpretační zodpovědnosti. [[Zavřít knihu->5 - Chytřejší beznaděj]] [[A Seznam literatury]]

(text-style:"italic")[Seznam literatury] Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum. Cohen, J., Cohen, P., West, S., & Aiken, L. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum. Acock, A. C. (2005). Working with missing values. Journal of Marriage and family, 67(4), 1012-1028. Aguinis, H., Gottfredson, R. K., & Joo, H. (2013). Best-practice recommendations for defining, identifying, and handling outliers. Organizational Research Methods, 16(2), 270-301. [[Zpět->4 - Celá kniha]]

„Tohle všechno já vím! Bohužel, mně to nijak Nepomáhá! Sakra, co teď budu dělat?" Od té chvíle Abrahám upadl do apatie. Zmizela všechna motivace k práci a k čemukoliv dalšímu. Snad to brzo přejde a najde si jiné místo. Přejme mu, aby se zase dostal k vědecké činnosti, která pro něj je tak důležitá. Odmítl jste svoji šanci, a tak vaše cesta skončila velmi záhy, ale alespoň něco jste se dozvěděl. [[Vyzkoušet jinou cestu->1 - Postava]]

Nadšený z toho, jak kvalitní materiály byly obsaženy v modré složce, a že vám neznámý autor všech těch metod ušetřil spoustu práce, vkládáte Dotazník rozpoznávání spolehlivosti zpráv, Dotazník ochoty k veřejnému vystupování a Dotazník projevované agresivity vůči vládě do obálky a podle instrukcí odesíláte na uvedenou adresu. Teď nezbývá než čekat, až institut dodá požadovaná data. Za nějaký čas skutečně obdržíte datový soubor. Když ho otevřete v počítači, uvidíte následující tabulku: <img src="data:image/png; data:image/png;base64,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"/> Takto tabulka pokračuje až po ID 800, nicméně je vám jasné, že v tomto datovém souboru chybí zásadní informace. Z této tabulky nelze testovat jedinou z výzkumných otázek! Kontaktujete Institut s tím, že zapomněli doplnit anamnestická data. Institut však odvětí, že nasbírali data z obdržených materiálů tak, jak bylo dohodnuto. Žádná další data pro vás k dispozici nemají a nelze je již doplnit, protože sběr dat byl ukončen. Nezbývá, než řediteli oznámit neúspěch zapříčiněný vlastní roztržitostí. Jste nucen vyklidit kancelář a v tichosti opustit Akademii. Doma usedáte k počítači a začínáte procházet aktuální pracovní nabídky. [[Zvrátit poslední volbu->6 - Základní zadání]] [[Znovu zahájit výzkum->1 - Postava]]

(text-style:"italic")[„Pomalu, pomalu, musím krotit svoje nadšení, to vede k chybám. Dobře. To znamená, že mám tři úkoly. Srovnat věkové skupiny, co do hrubého skóru Testu spolehlivosti zpráv, posoudit korelace mezi výší platu a hrubým skórem Dotazníku ochoty a srovnat muže a ženy v hrubých skórech Dotazníku agresivity. Fajn. Jaká data k tomu teda potřebuju? Věk, Test zpráv, Výši platu, Dotazník ochoty, Pohlaví a Dotazník agresivity. Dotazníky mám hotové. Takže stačí vymyslet jakým způsobem sbírat a kódovat ty ostatní proměnné. Potřebuju prostě vytvořit (text-style:"bold")[anamnestický dotazník].“ ] [[Vrhnout se na vytváření Anamnestického dotazníku->10 - AD kreativita]] [[Prolistovat modrou složku->9 - Postupovat rozvážně]]

Rozhodl jste se, že je lepší postupovat rozvážně. Znovu usedáte za stůl a podrobně zkoumáte obsah modré složky. Základní instrukce k výzkumu. (text-style:"italic")[„Jasně, to jsem četl.“] Test spolehlivosti zpráv, Dotazník ochoty a dotazník agresivity. Dále nacházíte rozpracovaný Anamnestický dotazník. Obsahuje několik tištěných položek a u každé z nich je rukou napsána poznámka. (text-style:"bold")[Pohlaví:] (text-colour:#a5d8ff)[1 – muži, 2 – ženy] (text-style:"bold")[Věk:] (text-style:"bold")[Příjem:] (text-colour:#a5d8ff)[zaokrouhlovat na stovky] (text-style:"bold")[Kraj:] (text-colour:#a5d8ff) [1 – 12 podle čísla kraje] (text-style:"bold")[Velikost bydliště:] (text-colour:#a5d8ff)[1 – do 500 obyvatel; 2 – 501 – 2 000; 3 – 2 001 – 10 000; 4 – 10 001 – 50 000; 50 001- 100 000; 5 – 100 001 – 500 000; 6 – nad 500 000 obyvatel] (text-style:"bold")[Vzdělání:] (text-colour:#a5d8ff)[1 – základní; 2 – výuční; 3 – maturita; 4 – nižší vysoké; 5 – vyšší vysoké; 6 – doktorské; 7 – postdoktorské] (text-style: "bold") [Politická aktivita:] (text-colour:#a5d8ff)[0 – ne; 1 – ano] (text-style:"italic")[„Hm, v tomhle návrhu je toho víc, než by bylo potřeba, ale více dat je vždycky lepších než méně dat. Bude lepší to zachovat, ostatně, můžu případně zkusit ještě něco nad rámec těch tří zadaných otázek. Jediné, co v tom Anamnestickém dotazníku chybí je kódování věku. V zásadě existují dvě varianty, jak k tomu přistoupit. První možnost je nechat všechny vyplnit svůj věk v letech. Snadné a rychlé. Ale trochu problematické. Občasné Státu jsou v tomto ohledu dost hákliví a neradi takové informace sdělují. Ale vzhledem k anonymitě dotazování by to snad ani nemusel být problém. Ač, to vlastně nevím, jakým způsobem to v Institutu sbírají. No, je to trochu riskantní i když se ten risk vyplatí na kvalitě dat. Nicméně nějaký šťoura to může napadnou z hlediska etiky, ale není to zas tak neetické. Je to jenom věk. Abych se tomu vyhnul, můžu se přidržet teorie a nechat každého vybrat, do kterého ze tří intervalů spadá. To znamená 15 – 22; 23 – 60; 60+"] [[Zvolit variantu konkrétních věků->11 - MS Konkrétní věk]] [[Přidržet se teorie a zvolit variantu intervalů->19 - MS intervaly]]

(text-style:"italic")[„Fajn, potřebuju vytvořit Anamnestický dotazník, který mi bude pokrývat potřebné výzkumné otázky. Takže k výzkumné otázce a) potřebuju věk, k výzkumné otázce b) potřebuju čistý příjem a k výzkumné otázce c) potřebuju znát pohlaví.] (text-style:"italic")[Co se týče kódování, tak nejjednodušší je pohlaví. Tam není co řešit. 1 – muži, 2 – ženy. Čistý příjem se u nás, až na nějaké extrémy, běžně pohybuje mezi 10 000 mincí a 50 000 mincí. Při takovém rozpětí hodnot nemá význam řešit každou jednotku. Nemůžu to ale zase zkrouhnout moc. Kdybych to rozlišoval příliš hrubě, nebylo by možné udělat korelační analýzu. Proto by bylo nejspíš užitečnější zaokrouhlovat příjem na stovky Mincí. Ovšem větší problém bude s tím, jak kódovat věk. V zásadě existují dvě varianty, jak k tomu přistoupit. První možnost je nechat všechny vyplnit svůj věk v letech. Snadné a rychlé. Ale trochu problematické. Občané Státu jsou v tomto ohledu dost hákliví a neradi takové informace sdělují. Ale vzhledem k anonymitě dotazování by to snad ani nemusel být problém. Ač, to vlastně nevím, jakým způsobem to v Institutu sbírají. No, je to trochu riskantní. Navíc nějaký šťoura to může napadnou z hlediska etiky. Abych se tomu vyhnul, můžu se přidržet teorie a nechat každého vybrat, do kterého ze tří intervalů spadá. To znamená 15 – 22; 23 – 60; 60+. Ten samý problém vlastně vyvstává i u příjmů, nicméně kdybych i ten sbíral jako interval, opět narazím na problém s korelační analýzou. Takže tam nemám na výběr. Ale co s tím věkem?“] [[Zvolit variantu konkrétních věků->11 - AD Konkrétní věk]] [[Přidržet se teorie a zvolit varinatu intervalů->19 - AD Intervaly]]

(set: $vek to 1) (set: $tabulka to 1) Rozhodl jste se pro variantu konkrétních věků. To znamená, že finální verze vašeho Anamnestického dotazníku je strukturovaná následujícím způsobem: (text-style:"bold")[Pohlaví:] 1 – muži, 2 – ženy (text-style:"bold")[Věk:] udávaný v letech (text-style:"bold")[Příjem:] zaokrouhlovat na stovky K tomu přikládáte (text-style:"bold")[Test spolehlivosti zpráv], (text-style:"bold")[Dotazník ochoty] a (text-style:"bold")[Dotazník agresivity]. (text-style:"italic")[„V tomto formátu bych snad měl mít všechny potřebné informace. Ale možná by bylo dobré ještě zapřemýšlet, jestli něco nepřidat“] [[Zkompletovat sadu a odeslat do Institutu->14 - Data I]] [[Přemýšlet dál->12 - Přemýšlecí pauza]]

(set: $vek to 2) (set: $tabulka to 2) Rozhodl jste se pro variantu intervalů. To znamená, že finální verze vašeho Anamnestického dotazníku je strukturovaná následujícím způsobem: (text-style:"bold")[Pohlaví:] 1 – muži, 2 – ženy (text-style:"bold")[Věk:] 1 – 15–22 let; 2 – 23–60 let; 3 – 60+ let (text-style:"bold")[Příjem:] zaokrouhlovat na stovky K tomu přikládáte (text-style:"bold")[Test spolehlivosti zpráv], (text-style:"bold")[Dotazník ochoty] a (text-style:"bold")[Dotazník agresivity]. (text-style:"italic")[„V tomto formátu bych snad měl mít všechny potřebné informace.“] [[Zkompletovat sadu a odeslat do Institutu->14 - Data I]] [[Přemýšlet dál->12 - Přemýšlecí pauza]]

(set: $vek to 1) (set: $tabulka to 3) Rozhodl jste se pro variantu konkrétních věků. To znamená, že finální verze vašeho Anamnestického dotazníku je strukturovaná následujícím způsobem: (text-style:"bold")[Pohlaví:] 1 – muži, 2 – ženy (text-style:"bold")[Věk:] udávaný v letech (text-style:"bold")[Příjem:] zaokrouhlovat na stovky (text-style:"bold")[Kraj:] 1 – 12 podle čísla kraje (text-style:"bold")[Velikost bydliště:] 1 – do 500 obyvatel; 2 – 501–2 000; 3 – 2 001–10 000; 4 – 10 001– 0 000; 5 - 50 001-100 000; 6 – 100 001–500 000; 6 – nad 500 000 obyvatel (text-style:"bold")[Vzdělání:] 1 – základní; 2 – výuční; 3 – maturita; 4 – nižší vysoké; 5 – vyšší vysoké; 6 – doktorské; 7 – postdoktorské (text-style:"bold")[Politická aktivita:] 0 – ne; 1 – ano K tomu přikládáte (text-style:"bold")[Test spolehlivosti zpráv], (text-style:"bold")[Dotazník ochoty] a (text-style:"bold")[Dotazník agresivity]. (text-style:"italic")[„V tomto formátu bych snad měl mít všechny potřebné informace. A i nějaké navíc.“] Přesto. Nebylo by dobré se ještě zamyslet, zda tam ještě nepřidat něco navíc? Přece jenom, taková příležitost nasbírat velké množství kvalitních dat často nepřichází. [[Přemýšlet dál->12 - Přemýšlecí pauza]] [[Zkompletovat sadu materiálů a odeslat do Institutu->20 - Data II]]

(set: $vek to 2) (set: $tabulka to 4) Rozhodl jste se pro variantu intervalů. To znamená, že finální verze vašeho Anamnestického dotazníku je strukturovaná následujícím způsobem: (text-style:"bold")[Pohlaví:] 1 – muži, 2 – ženy (text-style:"bold")[Věk:] 1 – 15–22 let; 2 – 23–60 let; 3 – 60+ let (text-style:"bold")[Příjem:] zaokrouhlovat na stovky (text-style:"bold")[Kraj:] 1–12 podle čísla kraje (text-style:"bold")[Velikost bydliště:] 1 – do 500 obyvatel; 2 – 501–2 000; 3 – 2 001–10 000; 4 – 10 001–50 000; 5 - 50 001-100 000; 6 – 100 001–500 000; 7 – nad 500 000 obyvatel (text-style:"bold")[Vzdělání:] 1 – základní; 2 – výuční; 3 – maturita; 4 – nižší vysoké; 5 – vyšší vysoké; 6 – doktorské; 7 – postdoktorské (text-style:"bold")[Politická aktivita:] 0 – ne; 1 – ano K tomu přikládáte (text-style:"bold")[Test spolehlivosti zpráv], (text-style:"bold")[Dotazník ochoty] a (text-style:"bold")[Dotazník agresivity]. (text-style:"italic")[„V tomto formátu bych snad měl mít všechny potřebné informace. A i nějaké navíc.“] Přesto. Nebylo by dobré se ještě zamyslet, zda tam ještě nepřidat něco navíc? Přece jenom, taková příležitost nasbírat velké množství kvalitních dat často nepřichází. [[Přemýšlet dál->12 - Přemýšlecí pauza]] [[Zkompletovat sadu materiálů a odeslat do Institutu->20 - Data II]]

(if:$vek is 1) [ Je čas dát všechno do obálky a poslat to Institutu. Nezbývá než čekat, až data nasbírají a pošlou vám je k analýze. Chvíle klidu je zasloužená. Zatím jste odvedli dobrou práci. Ona chvíle odpočinku ale netrvá zase příliš dlouho. Za nějaký čas obdržíte datový soubor. Když ho otevřete v počítači, uvidíte následující tabulku: <img src="data:image/png; 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6x/bzg/6xzzktLNO27/wHhol9dEJ58ovBM8F22jBnUJPEl3o7bb5O1CptDXlHyO7PPvvuHC845e97SOnEWHVRlo8aND02aNEn1PXfdOVxaanPTV996N5VfTZ2aSio/Qexy0rBhw7DCCvXDD4t6WFhIDG2jGDjdcdc9aSftr776KrwydGg44+xz5x1FxXav3ku+ARIsK61bt8lqIZx25lnzjkN/ckSoXatWOvbed79w6hlnZmdVrLgOjodigMonvjD7cvLkVO/Ro2cq4zqu0b77H7jAfcyeexc29jr8qKNTu2XLVqm9JHr1LqynPH0R6woubtRtcUPU2bNnp7KocePCg8/CluYmY7A4Lw0amNY+LopLNN395zvD2tk1Ep8LoCaKz8f9nntugf0mpkyZEv56319C89Wbp/aECRNSCVXR6iXf4+JsnYkTy36Xmzdvnl549+/3XOi8aZd5R1G3bbZJ7eJSHSx/7iCXgQblrAPbuk2b0LDhimmJgiVR3NTr22+/DY2bNElvQ+Lxy9NOX+AoKraPKHmQgcqm9MPwaWecNe846phjQ5OmTcPKjRqFww4/Ipx40i+ysypWXKgfgMpp7NjCOpfRaqsVQtio9D1MPA459LDUf8xxx6f2Kqsu2bp+cbmm4q7YH44YkcqFLWr34aK4+WO08BTvxk3sts3yd9Thh4VjjvxJeP21V8Nf770nXH7JRam/uFRZXBcQaqrjjzkynHnaL9MI2Nt+f0s45MD9Un/TZqulclw5y9VAVdCiRYvw+NPPpvrn48aFbbtukeoLi7OBdtxpl/Dnv9w37yiGt5dcfkVqL7wGPsuPIDZHX3z+eSov+/WVqSytONLvr3/5SyoXp1GjRql87ZVXwtAhL4chLw8u9yia3/dy1gOVR/xRiMtqLHy8+srQNCV09uxZ6UHjrbfezP6JisXgNpo6tbBOGgDL1rrrrR+GvvFWGP15+aOOmjZtmspatWuFmTNnlLpPWfCIf/uj4r3O998t2cyhBx9+NAWpcS3Nww45KOtd0LlnnTFvZG55Om2wYVYrKN7HrbXW2qksbb31C+uT27CLvD3yxNPhtbfeDaefeXb45OOPw8H77xsuOPec8Ngj/wrX3nBjWL/kuxi/qz//6XHZPwE1x3MDBqW1YQ86uFd47dVXw0H77ROu+vVl6Vr56wP/SH+/P/rww3DZxYUXF1CVXHn1NeGVN99JG9HFPVK23WqLcpdfipueTvvqq/Dd3LkLHEULt1n+BLE5iqFStMOOZdc/a5JNc/twxPBURsW1nVZccaVUlue9d98O11x1ZTis10HlHkXFdvwhgsomjozqfdABZY6L+/YJc7/7ruRa+D5c/9trFvhOV2SFFVZI5dfTyp+OCkC+PvpwxLxNg+rVq5fK0uJsh2ji+AkpvCzepyx83Py7G9J5fc8/L7U/+ujD1F6cutkSTq8MXfQL6Bjuzq1gHc2VVlrw/mvSpMIIw+KIktKOPf6nqSyGtZCX+L1cvXnzsNe++2Y98+28y66pHDXq01RCTVOndp0UUh1y2OFZz3wbb1LYOyJubARVTVz+aJ/9Dkj1xW2+1W2LLuUexVmocU+W2B7/xRepzfIniM3RaaecHF4aNCht/vDqm+9kvQXF5QqeevKJVEZ33XlHesPx6ptvh2f6Dch645uQ36YyPrhcf22hDjXNiOHDUjn840/TRhVFBx7cK6uV/Mgc9p9t/AXA0hM3j4jirr7P9Hsh1aNHn3w6NG1amB56wL57pXJpW2PNNVN54w3Xp3JRtttqy1Q+P2BQ2HW33VM9ev2dwoN66bXX9t97zzSqKuqVLZkQbbPtdmnX+ujQXgemEvKy/957pGUHOnToGPoNfDHrLXnw3nqbtEN2dHjv+fdCUJMcc8RPwuTJk8JW3bqFl4YWNuaK4vXRqFFhaZv4TA5VzcdjxoWVV145vNC/XzjuqCNC1626lTk236JwTxMD1/KOomnTpi3QZvkTxOasGAw1b9Ei/Pb6G8L9Dz0c3vmgsHbZwmuV/frSS8KY0aPSNOt27duH62+6Je0qXLzZHz6sEERBTdTrgP3SxhRxbeWTTvlluPdvD6QXFsWlP+JuqHHdHACWr/jgEJcq6HPhxeE3v702bNJ509T/+bjPUpmn6dOnZ7XyxZ3n43r7HTquG674zTVp3bT+Lw4OzbLNw84+/bRUFu23VyGsvejiS8PAwUPCP/71aJoOHsURKnE6IOQpfs+GDCmM9G7foWO6rh7457/CFVdfk/rKm6YKNUWcZReXpIlatmoVzv9V3/DIE0+Fa64rzK5YePNFqAriPVRxQ9Aum20e/nDnXeUel1z+63QOVY8gdhnYdccdwpOPPxZ23GnntE5NfEiIa3ysu07r7Iz5dtph+3DbLTelG/vttu+ezh332Wfp/PhGfHE+/fSTdEBVVKdO3TB1ypdh8qSJZd7azZgxI2y1WedwzJGHhw/efz+s12n9tOD4N998k66Pzhuun50J1VscGTV50qSsBctW7dq1KrzPiH+PH7z/72nK/k+OODLsvude4e0338zWNiuM3KhI3MQ0/vvrlrO8QUWK9z+zZ83KehZt/fZtwztvvxW++/77sM9++6WprU8+8Xj633HQwPkzkqIYgsX+eD8WZzPF+7i4vMEF554dNlqvQ3YW5OvUk08Mfc47Jw3KiJuZtmzZKjQs+T7G72aHNoVN5qC6ir8no0eNCuMW8TIvLmMTr5ExY0aHw488OjRbvXna6DpeH506rJOdBVXHlb+5Jt3vx2PunDmLPBa3jn5cF99GjpVTrTZrtUhpR/9Bg9MozEWJP/x77NIza/Hf6N6jR/jh+x/SguFxCsXixPPjCL/itDiWvWf7Dwi79uyRtahM4pSjeB25PvIXR/LHNXxZ/uLb8SZNCmttTpnyZSpZNuJD3eJ23WdBe+y5Vwo7n3/2maynconBatettgozps8Ib735RtZbvvjff7311w8//PBDmDxp8hLdx1VHAwcPTeu5x6mSLB+77r5HGP3pp2H06FH+JsFC4vXx8ciPwqiSa8RU7Hw1btIkzRTpvIHBMEvbaqsVZuksTr369cKE8eOzVlnFf8/XX08ze2IpiiPue/U+NGuV1aljuwoHBpzXp68RscvSoAEDwuCXXlzim/d4vpAJyhc3XXF9UNPEadUxgBXCUhXEdfArawgbfTt7drrXWlwIG8XA67133w3DPvigxoawVA7PPv1U2sROCAtlxevj008+EcJSpRXv9Rd3VBTCRsXzhLCVjyAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIBQAAAADImSAWAAAAACBnglgAAAAAgJwJYgEAAAAAciaIXYYaNGgQ6tatm7UWr169elkNqr9atWqFRo0apRIAACCKzwjNmq2etaDmWWWVVbLakllppZWyGpVRrTZrtfgxVvoPGhzatW+fOsszfNiwsMcuPbMW/6n99j8wnHP+BSlk+u6778IO23bLPimrTdu24YyzzglbbNk1/Pvf/w7vvfduOOn/fpp9Ol8Mak8/8+xQf4X6WU9Zl19ycVbjv/Fs/wFh1549shZ5ad68eXjkiadL/iIV2gfuu3cY/8UXhUbmoksvz2oVu+TCX2U1lpb7H3o49D7ogKzFsrbxJp3DlVf/Nvz4449h0sSJ4fhjjsw+YVmqX79+mDt3btbivzX0jbdTef45Z4YX+vVL9fIUz+u2+aapLM9LQ18LdUvuhWbN+ib03H7brJc8DRw8NFzct094of+i/9uxdPW96JJQZwkGcsydMydccfmlWQuql7feGxYmTJiwyDzivAsWff//xuuvheeefSZrsbQ0btKk5DdhSOi8wfpZD3nY/4ADwy677R4232LLMGfunPDWm2+E035xcvZpWX+8867Qcd11Q9269cKoUZ+GG679bboGWHquue6G0Kv3oVmrrE4d24XZs2ZlrbLO69NXELssjP58QirHjh0T6pVcEE2aNgkNGjQMX02dGrpsvEH6rOi++x8M2263fap/Pm5cevPRqOSYM+fbMOTll8MxR/wkfRad9ItTwjnn98la5Wu79hpZjf+GIDZ/xetj0qSJ6SVFgxUahNWaNUt9pb+/xfMWx3d+6RPELh/77LtfuOnW21N9+vSvQ61atcPKK688b9S47/qyJYhdOkr/LZ89e3bo1GGdrLWg4nkVfc9L/7t6dt82fPrJJ1mLvAhil73X334vNFt98SMBx4//Imy9xWZZC6qH9dbvFJ5+vn+694mh0o7bbZN9sqCKnhMevP9v4ewzTs9aLC2C2Hy1bNkq3H3f30L7Dh1Se+RHH6XBS6s2bpzaY8aMDjtsM39w34CXhoS26xTuqb7++uswY/r00LJVq9R+/bVXw8H775vq/O+WRhBraYKcXXXNtamcOXNm6L71VmHHkgeFE356fOpr0rRpKouOOOqY0HnTLmkU7Ijhw8K2W20R/u+nx4Zx48aFFVZoELputeAo2q7dtk7lpEmTwscjPyrnGJk+h8pq1913z2px1FOXsG3XLcLuO5f/wueTj0cu8ij67LOxWQ2qtnXatQt9L7ks1adNmxZ23mH70OuA/cJZp5+a+qA6aNiwYVb778V7pujEk3+RSqhuJk+eVO69TzymTpmSzpny5ZfhT7fflupQndx8623zXkAvifKuk8/Gej6g6jnltNNTCPvtt9+mGdK77Ng97Nyje3ohEbVu3SaVRWu3bJnKMaNHhcN79wrbddsy3Hv3XSmUjTOt77nv7+lzKgdBbI7WXW/9sEXXrqn++quvpvLb2bPDSy8OSvWFHf+zn6XRTmNGj54XRg0ZPDjsmY1EXnHFFUOdOnVSPWpX8qAePf/s0+miLHsURtZCZfXrK69O5Ucfjpj3MB0fOIo27TJ/ZMdOO2y/yKPozFN/mdWgarv9T3emt95x5sRnY8ekF27xBd1DDz6QnRHCrrvNf5EBNU3xZfYXn3+eyu477PgfrcMPVcUeu+xU7r1PPOrVLyxPNnTIkHDHH/+Q6lCddFx3vay2aKWD2vKukxtvuD77FKqOnjvvnMp33n4rnHFK4WVzfE4uDuor/b2Ps7aK+wsd2uug8P5776b672+5KTz6r4dTfcONN0ollYMgNkd33HVP6NChYzj3rDPCMUfOX1Lg+++/T0PJo0effDqV0TrtCktD7Lj9glMupk+fntVCeHHo/PU91m5ZGGre57xzUwlVyZDX3wyrN28ezjz1lDLLP/z02KPCNVddmdbErMjgV15PU5HisgZx+uqrrwzNPoGqLb7IizbbZMOwzx67pXrRdb8tvMC49NdXphKqoqeffCL88MMPFU4nXZRNOm8a+g98KcycOSPcc/ef07+nxRolvwFvvpOdAdXfXff+NW1gNOubb8LJJ/ws64XqIf42xOPLyZPDgBf6Z73lO/eCwlJ9cRlAqA7i4Lu4FMG4zz5Ly8ONHPlR9kkIH44YntXmO+yII8PoUaPCxAkTFthnJb6s7nvBeam+2mqFpf+oHASxOVq50cqpHDG87MXyypCXU9m8eYtULqk6tef/JyuOjl1cWAWVUfG7/9A/Hkxlac8/+2y45abfpTeAFVlzrbVSee6ZZ6QSqou33nwz3VCV9/e9RYvCmpnFUeRQFV191RVp87notDPPSuWSOuGkk9OI2K+nfR3+9dA/wvSvv079TVdbLZVQ3R1y6GFpsEf09mLulaAq23rLxa97vNlmW6TyE8vyUU3EF8w/OeTgtLzAkrj7zjtCj+22DocctH/WM99KK62U1ahMBLE5atKkMG2uvDDp+eeeC3PmzAkrNFgh66nYlGwNqDiCcGFxVOCIT0bPe3M4/ONR2SdQeZVeZqN1m7bh7Q+GZ9/fT8P+Bx6UfVKx2tmLCZuGUN0csM+e6YaqPHvuvU8q49qxUJUVH7BPO+Os0G/gi6m+JHruvEsqzz7jtLRsx0V9K964FKqbSy6/Im3CEtcNjw/rUN1cefllabZb3Mi3IvEFXNygaNq0r8J7770373k4HnFzwe2675CdCVVfgwYN0qzrqLxrIy5xWdrNt96eroNo6MuDU0nlIIhdTuI0iznffhvq1lmy9cz+/cMPqSwGT6XFhZnjRVkUN7+48urfho032STrgcprlcaNw933/jU0btwktRs2XDH0ufDi8I9/PZrai7LxJp2zGtQsTZoUrpW77vxTKqE6KC63tCSK9zwvD34plU88VvHvBVQ3xY3u/vHA/amE6ub2W2/JahXr2HHd9JsQN7b++YknZb0Fbdq2DbfcZu1kqoeLLrksvPrWO2GnXXZNM+aGffB+9klZ22y7XfjzPfeFvffdLzRbffXwxRdfhGOOOiL7lMpAELucxLd2c7+bGxqtskrWU7GKlh9o33rt9MaweMSH88MOPyI89tSz6ccJKrN3PxhR8j9/nPf9XaflmmH1kh+MuLtjcTOW8jz21DPhq6++Cp06Fjatg+pu++49wtA3CjMsvp42LTzw97+lOlRl8e9+FB+kzz7v/FT/T8UpfGPHFNYGjOvEWqKA6uy9EYW1AuP3Hmq631x7XVhl1VXT9XDrzTfOe56Ix3dz54ZVSz57fsAgvwtUaWusuWY4qNchYZVVVk3tuFFXy1atU31h8bN4XRQ3jY/q1asbrr/x5qxFZSCIXU7iBVK7Vu3w/WKmW8wzf1O8eT77bGwaWbvwjdif75g/Smr3PffMalA5TRg/Pvz+5puyVuGlQ9yAJfrVhRencmHdti5saDdk8Eth9qxZqQ7V3XW/uzGsscaaYeqUKWnxfqgupnz5ZSoP6X1YmmJakR167JjKuSUP2KU9cP/f0v1QHPlx7HE/zXqh+mnUqDCIY+rUwrJlUJO9/eab4YP33wsXnHt2uOG6a7PeglGjCsv1rbV2y9B9hwU3BoaqJM6OPmi/fcI5Z54ePv64sBbyaot4uRBzpoEv9A8H7rNXeOP119NymKuv3jxsvc222RlUBoLY5aRevfqhVu3a4duSC2NJ1Conid2+W9fQs3vZC6r02iA77rRzVoPK6fXXXgsP3v/3rFUw6tNPU9m1W7dULuy4nxZ2B77pdzekEqq7w488at4a4ff95Z6w1+6FNTKhOnj26afSWmfxO37nPfdmveU74eRfpDKukbzv/gfMO6ZOmRpmzJielnA6+JDe6Ryozl4aNCirQc112iknh7122yW8+frrWc98l19yUZg+/euw4oorhl133yPrhaonroc/cuRHaTbczjtsn/WWL27m+6vzz0vnH7Tf3uHAffdO/Y0bN04llYMgdjlZa621QsMGDZZ4NF/tOoX/VN99t+AIkOnTp2e18rVu3SarQeV0xqmFh+rSrrnqyvRQXt6bvv0OODB03rRLqg8f9kEqobr79VVXpzLOhLj2mt+kOlQX5597dtqgLmrXrn0qF6XrVoUXdM2bNw833nLrvOOK31w9b63xNUvusaA6WmGFwia/n48bF07/Zdn7J6ipxo0rO1No0MABYcqXhZHjm3YpPDtAdVCcEfH08/1TWZE4YryovP2GWD78l1gGSm+kVdS2XbvQoGHD9MZiSdSvX7jxmjVrdiqX1Pfff5/VoHJaeHpp9Pnn48I333yTFt5f2Pbddwgt1iisKQjVXacNNgwvDX0t1T/5+ONw0w3XpzpUN++/N/9BoSJ16tTJalDzdFy3sPfDU08+kUqgYsXlzuKSgFBdzJwxM5XFNWOXVLNmzbIay5u/SDkqruF30623p7K0E048OZX33nNXKhenUaNGqXztlaGp3GPPvcLozyekoyLvvfduVoPK5csvJ6fyrHPPS2Vphxx6WJo+UTyntL322TeV1oaluttwo43CU8/1Cy1btQrbdN087LTDdjboolr7y91/TrMhyru3qVevXnjlzbdTvfRmLAsfRfvtf0BWg+ohrvH3+9sL+0DEKddACKPGjU+/Gb0P+0nWM1+8jypuaPTUE15eUHW0btO2wqxn5SwbiksyRXEz34rOL5o8ueyzNcuHIDZHQ4e8nMouXTYL9evXT/Wiptlu8B+PLCy2HH07uzDatV37RU/Le+/dd1I5JPt3L46Hdiqrb2Z+k8rd99grlaXtufc+qfz4o/nXR1HDhg1TOUsQSzV3fp8LUxlnNnzx+eepDtVZ3wvODxMnlP8QEZelWXXVxa9vVpxl8fOTTNumetlgow3T0gRxU1OgYNpXX6Vy7333S2VpfS68ODRp0iSM/+KL8PA//5H1QuU37rOxWa18xUF6zz/3XCq/mlq4DhbH70flIYjN0Vmnnxr6P/982sH3jXfnr2XZdLXVQt169VK99NSiO/70xzB79uzwTL8B4YUXC0Fr45Ifj789+FCqz5w5M/zu+utSvfijE/3shBOzWggX9L0oPPlsv1QfPerT8NwzT6c6VDY7bNstrfHaoWPH8Po786ekxqU8WmVvr4847JBUlmfEiOFZDaqf195+N2zXvXsYM2Z0OOeM08OBB/Uq94DqZrtuW2a1BZ17fp9yl3pa2OAXCxsYbbDhhuHaG25MdagODup1SFqaaeLEikc8QU1y6y03hylTvkxLl73yRmHWRBSXsdlm2+1S/ewzTw/vvjP/M6js4vKV8QVC9Nb7w8NJv/hlqkcHHnxwmiUUXXPVFancY5eeqYxijtSs2eqpfsRRR4chr72R6kLYykUQm7Pjjj4ilfGtRXG4+JvvfpCm3j3z1JPps6J4IXXqsE66sNZp1y6d+3bJhbf1NtuGEcOHhY3W65CdWXBbyQ9P3KyrT9+L5v27/++EE9PDx3FHHxl6bLdNdiZUTnvsslMq449F8Ts84pPRqW/Up59W+IPxxGOPZjWofuIU1KhNm7bhuhtvKve44MLCiFmobr4sZ+rc6s0L18TEiRNTuSjHHnVEWmc8Km+EFFRVm3bZPJUzFrNRL9Qkf7jt92HzTTZK9fiiovg88cnYwkyiOMjppUEDUx2qkq233Cz0PviANKr7nPMvmPfdvu53N6eZoff95Z7szIIdttkqTJs2LeVIcZBTPPfyK38T1lxr7dDv+efC/x1/bHYmlYEgdhn4+OORYc6cOVmr4I+33xp+/tPjstaCZswoLCpe2u47z3/LUXTVFZeHP99RWCuqtDiN9YV+z2ctqNzeefut8M3MwoLjRXHn057dt81a5Stvd1QAqr7HHn0kq5U1ZtSorLZoD/+jMAU1TuNeu2XLVIeqrm7dwkZ11viDsj4bOyarzTe2pG+TToUN7qAqemXIkPDtt99mrfni8pN9zjsnaxWMGTMmdNmoU9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"/> Tímto způsobem tabulka pokračuje až po ID 800. (text-style:"italic")[„Výborně, a teď je čas se pustit do analýz.“] ] (if:$vek is 2) [ Je čas dát všechno do obálky a poslat to Institutu. Nezbývá než čekat, až data nasbírají a pošlou vám je k analýze. Chvíle klidu je zasloužená. Zatím jste odvedli dobrou práci. Ona chvíle odpočinku ale netrvá zase příliš dlouho. Za nějaký čas obdržíte datový soubor. Když ho otevřete v počítači, uvidíte následující tabulku: <img src="data:image/png; 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"/> (text-style:"italic")[„Výborně, a teď je čas se pustit do analýz.“] ] [[Zahájit analýzu dat->15 - AD Výsledky hypotéza a)]]

(if:(history: where its name contains "10 - AD kreativita")'s length >= 1) [ Následujících několik dnů přemýšlíte, co ještě přidat k Anamnestickému dotazníku. Přemýšlíte, k čemu je celý výzkum vlastně určený a co by ještě bylo dobré k tomu přidat. (text-style:"italic")[„Jedna věc je splnit zadání, ale když už je tady taková příležitost, mělo by se toho pořádně využít. Přece jenom, bude to výzkum na velkém množství lidí a když přijdu navíc s nějakým zajímavým zjištěním, mohl bych napravit pošramocené jméno Akademie. Beztak za to můžu já. A co být troškař, třeba se zase stanu slavným vědcem!“] Chodíte ode zdi ke zdi a hlava se vám plní různými nápady, které ale vzápětí zase zahazujete. Nic z toho nemá potenciál k převratnému objevu, který potřebujete. Nakonec začnete uvažovat, jestli má cenu se do toho vlastně pouštět. [[Vzdát snahu o rozšíření výzkumu->14 - Data I]] ] (if:(history: where its name contains "9 - Postupovat rozvážně")'s length >= 1) [ Následujících několik dnů přemýšlíte, co ještě přidat k Anamnestickému dotazníku. Přemýšlíte, k čemu je celý výzkum vlastně určený a co by ještě bylo dobré k tomu přidat. (text-style:"italic")[„Jedna věc je splnit zadání, ale když už je tady taková příležitost, mělo by se toho pořádně využít. Přece jenom, bude to výzkum na velkém množství lidí a když přijdu navíc s nějakým zajímavým zjištěním, mohl bych napravit pošramocené jméno Akademie. Beztak za to můžu já. A co být troškař, třeba se zase stanu slavným vědcem!“] Chodíte ode zdi ke zdi a hlava se vám plní různými nápady, které ale vzápětí zase zahazujete. Nic z toho nemá potenciál k převratnému objevu, který potřebujete. Nakonec začnete uvažovat, jestli má cenu se do toho vlastně pouštět. [[Vzdát snahu o rozšíření výzkumu->20 - Data II]] ] [[Dát své kreativitě ještě šanci->13 - Vidina úspěchu]]

Vidina převratného úspěchu je tak silná, že se vám nechce hned tak vzdát. Dalších několik dní chodíte po pracovně ode zdi ke zdi. Nakonec se vám v hlavně přece jenom zrodí nápad, který stojí za pokus. (text-style:"italic")[„Když k tomu přidám ještě proměnou týkající se Akademie samotné, můžou z toho být docela zajímavé výsledky. Buď se ukáže, že lidé vnímají Akademii jako užitečnou a prospěšnou organizaci, což určitě rádo uslyší naše PR oddělení a nebo se ukáže, že jsme vnímání jako zbytečný žrout peněz občanů našeho Státu. To zas bude důležité pro ředitele, protože takové smýšlení je pro nás ohrožující a budeme s tím muset něco dělat. Tak jako tak to pro nás bude užitečné. K tomu, abych něco takového zjistil ale potřebuju nějakou metodu, která zjistí vztah běžného občana k Akademii.“] Hned se do svého záměru pustíte a začnete skládat metodu, která má odhalit vztah běžného člověka k Akademii. A tak ponořen do této práce trávíte den za dnem. Až jednoho odpoledne vás ze zadumání vytrhne zaklepání na dveře. Za nimi stojí onen kolega, kterého jste onehdá potkal na chodbě. „Abraháme, máš jít za ředitelem.“ „Jo, hned tam budu,“ odpovíte a zamíříte ven z pracovny. „Abraháme,“ uvítá vás ředitel, „přišel mi dopis z Institutu. Poslouchejte: Vážená Akademie, k dnešnímu dni jste nedodali materiály ke sběru dat v Celostátním víceúčelovém výzkumu. Musíme vám tedy s politováním oznámit, že vaše účast v tomto šetření je tímto zrušena. Víte, co to znamená, Abraháme? Znamená to, že jste zklamal důvěru, kterou jsem ve vás vložil!“ Jste nucen vyklidit kancelář a v tichosti opustit Akademii. Doma usedáte k počítači a začínáte procházet aktuální pracovní nabídky. [[Zvrátit poslední volbu->12 - Přemýšlecí pauza]] [[Vyzkoušet jinou cestu->1 - Postava]]

(if:$vek is 1) [ Je na čase pustit se do další práce. Zkoumáte, jak souvisí věk se schopností posuzovat spolehlivost mediálních zpráv. V tuto chvíli je čas přidržet se známé teorie a ze všeho nejdřív sloučit respondenty do skupin podle věku tak, jak bylo uvedeno v rešerši. Vytvoříte si tedy novou proměnnou (text-style:"bold")[„Věk-Int“], která nabývá hodnoty 1 pro lidi ve věku 15 – 22 let, hodnoty 2 pro lidi mezi 23 a 60 lety a hodnoty 3 pro lidi starší 60 let. Tyto tři skupiny pak porovnáte v hrubých skórech Dotazníku mediálních zpráv. Výsledky lze shrnout asi takto: Všechny tři skupiny se od sebe vzájemně liší statisticky významně. (text-style:"italic")[„Což vzhledem k velikosti vzorku není nijak překvapivé.“] Věcný rozdíl mezi skupinou od 15 let a skupinou 60+ je zhruba půl směrodatné odchylky, přičemž skupina od 15 dosahuje vyššího skóru. Skupina 23 – 60 let dosahuje v Testu schopnosti ze všech tří skupin nejlepších výsledků. Věcný rozdíl mezi skupinou od 15 let a skupinou 23 – 60 se rovná jedné směrodatné odchylce. Rozdíl mezi skupinou 23 – 60 a 60+ je tedy jedna a půl směrodatné odchylky. Tuto otázku lze tedy uzavřít tak, že skupina od 15 let je o trochu lepší v hodnocení spolehlivosti mediálních zpráv, než skupina 60+. Skupina 23 – 60 je pak výrazněji silnější než obě zbývající skupiny. ] (if:$vek is 2) [ Je na čase pustit se do další práce. Zkoumáte, jak souvisí věk se schopností posuzovat spolehlivost mediálních zpráv. V datech tedy máte proměnnou (text-style:"bold")[Věk], která nabývá hodnot 1 pro lidi ve věku 15 – 22 let, hodnoty 2 pro lidi mezi 23 a 60 lety a hodnoty 3 pro lidi starší 60 let. Tyto tři skupiny pak porovnáte v hrubých skórech Dotazníku mediálních zpráv. Výsledky lze shrnout asi takto: Všechny tři skupiny se od sebe vzájemně liší statisticky významně. (text-style:"italic")[„Což vzhledem k velikosti vzorku není nijak překvapivé.“] Věcný rozdíl mezi skupinou od 15 let a skupinou 60+ je zhruba půl směrodatné odchylky, přičemž skupina od 15 dosahuje vyššího skóru. Skupina 23 – 60 let dosahuje v Testu schopnosti ze všech tří skupin nejlepších výsledků. Věcný rozdíl mezi skupinou od 15 let a skupinou 23 – 60 e rovná jedné směrodatné odchylce. Rozdíl mezi skupinou 23 – 60 a 60+ je tedy jedna a půl směrodatné odchylky. Tuto otázku lze tedy uzavřít tak, že skupina od 15 let je o trochu lepší v hodnocení spolehlivosti mediálních zpráv, než skupina 60+. Skupina 23 – 60 je pak výrazněji silnější než obě zbývající skupiny. ] [[Blíže prozkoumat výsledky->17 - AD Výsledky A+]] [[Zapracovat výsledky do článku->16 - AD Výsledky A]]

(if:$vek is 1) [ Vaše vědecká zvídavost vám nedá a rozhodnete se detailněji prozkoumat výsledky. Proto si v dalším kroku zobrazíte rozložení proměnné Věk-Int pomocí scatterplotu. Rozložení grafu napovídá, že by v daném vzorku mohly existovat nikoli tři, ale čtyři skupiny. Proto si zobrazíte ještě jeden scatterplot, tentokrát z proměnné Věk. Analýzou jednotlivých případů se pak ukáže, že se vzorek skutečně rozdělil do čtyř nezávislých skupin. Zůstala zachována skupina 15 – 22 let a skupina 60+, ale prostřední skupina se navíc rozdělila na dvě. Vznikla tak skupina 23 – 45 let a skupina 46 – 60 let. Vytvoříte si další proměnnou (text-style:"bold")[„Věk-Int2“], která nabývá hodnoty 1 pro interval 15 – 22let, 2 pro interval 23 – 45, 3 pro interval 46 – 60 let a 4 pro skupinu 60+. Tyto čtyři skupiny pak znovu porovnáte v hrubých skórech Testu důvěryhodnosti mediálních zpráv. Ukázalo se, že v takovém rozložení jsou výsledky poněkud jiné. Skupina 4 dosahuje nejhorších výsledků. Skupiny 1 a 3, které se vzájemně neliší, dosahují o půl směrodatné odchylky lepších výsledků než skupina 3. Skupina 2 dosahuje zdaleka nejlepších výsledků o dvě a půl směrodatné odchylky lepších než skupina 4. Z těchto výsledků tedy vyplývá, že lidé starší 60 let dokáží jen velmi špatně rozlišovat spolehlivé a nespolehlivé zprávy. O něco lépe jsou na tom v tomto směru lidé mezi 15 a 22 a stejně tak o něco lépe je a tom věková skupina mezi 46 a 60 lety. Zdaleka nejlepší v tomto jsou pak lidé mezi 23 a 45 lety. [[Zapracovat výsledky do článku->18 - AD Shrnutí výsledků A+]] ] (if: $vek is 2) [ Vaše vědecká zvídavost vám nedá a rozhodnete se detailněji prozkoumat výsledky. Proto si v dalším kroku zobrazíte rozložení Věku pomocí scatterplotu. Rozložení grafu napovídá, že by v daném vzorku mohly existovat nikoli tři, ale čtyři skupiny. Zjistíte, že dvě z těchto čtyřech skupin jsou tvořeny výhradně lidmi z věkové skupiny tři. Protože je ale vzhledem ke struktuře vašich dat nejste schopni rozlišit, nelze nijak získat další informace. Chvíli nad celou situací dumáte, přece jenom, ta situace najednou vypadá trochu jinak, než se původně zdálo. (text-style:"italic")[„Sakra, v důsledku vlastního špatného rozhodnutí jsem se připravil o další informace. Kdybych se nerozhodl sbírat věk pomocí intervalů, mohlo z toho vzejít ještě něco zajímavého. No co, z článku, se to nikdo nemusí dozvědět. Podám to jako úspěšné potvrzení dosavadní teorie.“ ] Veden touto úvahou do článku zapracujete původní výsledky, které pracují se třemi skupinami tak, jak to definuje teorie. [[Zapracovat výsledky do článku->16 - AD Výsledky A]] ]

(set:$vysledkyA to 1) (if: $vek is 1) [ První ze tří částí máte úspěšně provedenou a sepsanou. Vaše výsledky potvrzují předpoklad teorie, že v tomto směru lze rozdělit populaci do tří kategorií, které se mezi sebou liší. Dále se tedy věnujete zapracování těchto informací do článku, který hodláte na konci výzkumu publikovat. Po napsání příslušné části textu je čas přistoupit k dalším analýzám. Znovu jste sedl k datovému souboru. Je na čase pokračovat druhou částí. <img src="data:image/png; 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"/> ] (if: $vek is 2) [ První ze tří částí máte úspěšně provedenou a sepsanou. Vaše výsledky potvrzují předpoklad teorie, že v tomto směru lze rozdělit populaci do tří kategorií, které se mezi sebou liší. Dále se tedy věnujete zapracování těchto informací do článku, který hodláte na konci výzkumu publikovat. Po napsání příslušné části textu je čas přistoupit k dalším analýzám. Znovu jste sedl k datovému souboru. Je na čase pokračovat druhou částí. <img src="data:image/png; 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"/> ] [[Pokračovat na další výzkumnou otázku->25 - Úvod hypotéza B]]

(set: $vysledkyA to 2) První ze tří částí máte úspěšně provedenou a sepsanou. Dokonce jste přišel s výsledky, které do značné míry korigují dosavadní předpoklady. To lze považovat za velký úspěch. Ukázalo se totiž, že v populaci neexistují tři věkové skupiny, které se od sebe vzájemně liší z hlediska schopnosti rozlišovat nespolehlivé mediální zprávy, ale existují čtyři takové skupiny. Dále se tedy věnujete zapracování těchto informací do článku, který hodláte na konci výzkumu publikovat. Po napsání příslušné části textu je čas přistoupit k dalším analýzám. Znovu jste sedl k datovému souboru. Je na čase pokračovat druhou částí. <img src="data:image/png; 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"/> [[Pokračovat na další výzkumnou otázku->25 - Úvod hypotéza B]]

Dalším krokem je prozkoumání výzkumné otázky b). Tedy korelační vztah čistého příjmu s ochotou respondentů vystupovat na veřejnosti, která je reprezentována hrubým skórem v Dotazníku ochoty k veřejnému vystupování. V první fázi analýzy se bohužel ukázalo, že proměnná Čistý příjem obsahuje značné množství chybějících hodnot. Dále jste také zjistil, že tímto neduhem netrpí pouze proměnná Čistý příjem, ale také ostatní proměnné, se kterými jste doposud nepracoval. Tento problém je nutné nějak řešit. (text-style:"italic")[„Hm, musím nějak naložit s těmi chybějícími hodnotami. V zásadě existují dva běžné způsoby, jak s tím naložit. Buď se zabavím všech respondentů, od kterých nemám kompletní data a nebo chybějící data v Čistém příjmu nahradím průměrnými hodnotami.“] Jak tak v zamyšlení pochodujete ode zdi ke zdi po pracovně, padne vám zrak na knihu, na kterou jste dávno zapomněli. Její hřbet nese název(text-style:"italic")[„Pokročilé postupy řešení statistických problémů“]. [[Vymazat všechny nekompletní záznamy->26 - Zničená data]] [[Doplnit chybějící hodnoty hodnotami průměru->27 - MSub Výsledky hypotéza b)]] [[Nahlédnout do knihy //"Pokročilých statistických postupů"//->29 - Kapitola I]]

(if:$vek is 1) [ Je na čase pustit se do další práce. Zkoumáte, jak souvisí věk se schopností posuzovat spolehlivost mediálních zpráv. V tuto chvíli je čas přidržet se známé teorie a ze všeho nejdřív sloučit respondenty do skupin podle věku tak, jak bylo uvedeno v rešerši. Vytvoříte si tedy novou proměnnou (text-style:"bold")[„Věk-Int“], která nabývá hodnoty 1 pro lidi ve věku 15 – 22 let, hodnoty 2 pro lidi mezi 23 a 60 lety a hodnoty 3 pro lidi starší 60 let. Tyto tři skupiny pak porovnáte v hrubých skórech Dotazníku mediálních zpráv. Výsledky lze shrnout asi takto: Všechny tři skupiny se od sebe vzájemně liší statisticky významně. (text-style:"italic")[„Což vzhledem k velikosti vzorku není nijak překvapivé.“] Věcný rozdíl mezi skupinou od 15 let a skupinou 60+ je zhruba půl směrodatné odchylky, přičemž skupina od 15 dosahuje vyššího skóru. Skupina 23 – 60 let dosahuje v Testu schopnosti ze všech tří skupin nejlepších výsledků. Věcný rozdíl mezi skupinou od 15 let a skupinou 23 – 60 se rovná jedné směrodatné odchylce. Rozdíl mezi skupinou 23 – 60 a 60+ je tedy jedna a půl směrodatné odchylky. Tuto otázku lze tedy uzavřít tak, že skupina od 15 let je o trochu lepší v hodnocení spolehlivosti mediálních zpráv, než skupina 60+. Skupina 23 – 60 je pak výrazněji silnější než obě zbývající skupiny. ] (if:$vek is 2) [ Je na čase pustit se do další práce. Zkoumáte, jak souvisí věk se schopností posuzovat spolehlivost mediálních zpráv. V datech tedy máte proměnnou (text-style:"bold")[Věk], která nabývá hodnot 1 pro lidi ve věku 15 – 22 let, hodnoty 2 pro lidi mezi 23 a 60 lety a hodnoty 3 pro lidi starší 60 let. Tyto tři skupiny pak porovnáte v hrubých skórech Dotazníku mediálních zpráv. Výsledky lze shrnout asi takto: Všechny tři skupiny se od sebe vzájemně liší statisticky významně. (text-style:"italic")[„Což vzhledem k velikosti vzorku není nijak překvapivé.“] Věcný rozdíl mezi skupinou od 15 let a skupinou 60+ je zhruba půl směrodatné odchylky, přičemž skupina od 15 dosahuje vyššího skóru. Skupina 23 – 60 let dosahuje v Testu schopnosti ze všech tří skupin nejlepších výsledků. Věcný rozdíl mezi skupinou od 15 let a skupinou 23 – 60 e rovná jedné směrodatné odchylce. Rozdíl mezi skupinou 23 – 60 a 60+ je tedy jedna a půl směrodatné odchylky. Tuto otázku lze tedy uzavřít tak, že skupina od 15 let je o trochu lepší v hodnocení spolehlivosti mediálních zpráv, než skupina 60+. Skupina 23 – 60 je pak výrazněji silnější než obě zbývající skupiny. ] [[Blíže prozkoumat výsledky->23 - MS Výsledky A+]] [[Zapracovat výsledky do článku->22 - MS Výsledky A]]

(if:$vek is 1) [ Vaše vědecká zvídavost vám nedá a rozhodnete se detailněji prozkoumat výsledky. Proto si v dalším kroku zobrazíte rozložení proměnné Věk-Int pomocí scatterplotu. Rozložení grafu napovídá, že by v daném vzorku mohly existovat nikoli tři, ale čtyři skupiny. Proto si zobrazíte ještě jeden scatterplot, tentokrát z proměnné Věk. Analýzou jednotlivých případů se pak ukáže, že se vzorek skutečně rozdělil do čtyř nezávislých skupin. Zůstala zachována skupina 15 – 22 let a skupina 60+, ale prostřední skupina se navíc rozdělila na dvě. Vznikla tak skupina 23 – 45 let a skupina 46 – 60 let. Vytvoříte si další proměnnou (text-style:"bold")[„Věk-Int2“], která nabývá hodnoty 1 pro interval 15 – 22let, 2 pro interval 23 – 45, 3 pro interval 46 – 60 let a 4 pro skupinu 60+. Tyto čtyři skupiny pak znovu porovnáte v hrubých skórech Testu důvěryhodnosti mediálních zpráv. Ukázalo se, že v takovém rozložení jsou výsledky poněkud jiné. Skupina 4 dosahuje nejhorších výsledků. Skupiny 1 a 3, které se vzájemně neliší, dosahují o půl směrodatné odchylky lepších výsledků než skupina 3. Skupina 2 dosahuje zdaleka nejlepších výsledků o dvě a půl směrodatné odchylky lepších než skupina 4. Z těchto výsledků tedy vyplývá, že lidé starší 60 let dokáží jen velmi špatně rozlišovat spolehlivé a nespolehlivé zprávy. O něco lépe jsou na tom v tomto směru lidé mezi 15 a 22 a stejně tak o něco lépe je a tom věková skupina mezi 46 a 60 lety. Zdaleka nejlepší v tomto jsou pak lidé mezi 23 a 45 lety. [[Zapracovat výsledky do článku->24 - MS Shrnutí výsledků A+]] ] (if: $vek is 2) [ Vaše vědecká zvídavost vám nedá a rozhodnete se detailněji prozkoumat výsledky. Proto si v dalším kroku zobrazíte rozložení Věku pomocí scatterplotu. Rozložení grafu napovídá, že by v daném vzorku mohly existovat nikoli tři, ale čtyři skupiny. Zjistíte, že dvě z těchto čtyřech skupin jsou tvořeny výhradně lidmi z věkové skupiny tři. Protože je ale vzhledem ke struktuře vašich dat nejste schopni rozlišit, nelze nijak získat další informace. Chvíli nad celou situací dumáte, přece jenom, ta situace najednou vypadá trochu jinak, než se původně zdálo. (text-style:"italic")[„Sakra, v důsledku vlastního špatného rozhodnutí jsem se připravil o další informace. Kdybych se nerozhodl sbírat věk pomocí intervalů, mohlo z toho vzejít ještě něco zajímavého. No co, z článku, se to nikdo nemusí dozvědět. Podám to jako úspěšné potvrzení dosavadní teorie.“ ] Veden touto úvahou do článku zapracujete původní výsledky, které pracují se třemi skupinami tak, jak to definuje teorie. [[Zapracovat výsledky do článku->22 - MS Výsledky A]] ]

(set:$vysledkyA to 1) (if: $vek is 1) [ První ze tří částí máte úspěšně provedenou a sepsanou. Vaše výsledky potvrzují předpoklad teorie, že v tomto směru lze rozdělit populaci do tří kategorií, které se mezi sebou liší. Dále se tedy věnujete zapracování těchto informací do článku, který hodláte na konci výzkumu publikovat. Po napsání příslušné části textu je čas přistoupit k dalším analýzám. Znovu jste sedl k datovému souboru. Je na čase pokračovat druhou částí. <img src="data:image/png; 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8A548EKnAJmTR9plm6co32tjsbeZ47SZVJ1qV33qp1Xs9ZWX7lda93q598DSBkXwiOxStW6bwq02bPtc86N5OmzdDX+WzAVxoxked9+/2Pdu3CkL8DidicgCPxuP+ns5HnfPiJV/Ynrh585NGw7yXbq1StZlau26DLXbs9YB/xyA032d69Z+ib+vKY9GI+H3Lx6t4Hlxe5ybl5x2696ekvI3a+Jk2caH4a/YNdw+XElXFs0LCRRqi52WQ0joxSlWOOP8SKZcsiyqudKynDJ6QUXNv2HXT57jtu05D50/zvJTfjG9Sro8+pXqmCeeSBbqZR46Ya/uf98cdB0+Phh/R5X33xhbayXZIMLtGAhOU+r+QGtFyTyLKU4gsmSXN57FwVKVrULoUmrylRLkQngLhwr9f/8wH6eRz8PbvHhZQtlgh+DpKel1943tSvU9PcEzj+yFyibgSUlJuWkDLhRfLn0W2iasVypkThAubI4SPm7ff6mTLlypkH7++i8cmHH+hz/vn3nyijAB3ZduL4MQ1HPtOHDBqofyfPvfiSjmKVbf4OLU7nju1Nnjx5dGRcuvTpTOlihU2zRg30MTlPOHH8hEbwe/958k/d5kZm1apW2XR/+EEN0f2xx037Dp3Mj9//zxQrmF+3iUbX1TVffPapJsT6vv+Bxp9//mkWL1qopV6FvOaNzZrofIH//vufua/r/br9YiW/+wIFC2nZT/c37SJjxozmpx9/MMOHDtYQk37/zXR/6AFNZMpzlgSuYaUVdWt5n3WxVThfbnPs6FENSWiv27xNrx3uf+DBwP52ONr5nyR4JORYK/PjSpJHkljyPSxattLMCxyD2nf0RpPmyZNXI7b09xAIOXa/2fdduzW0mdOnaThSQtVPXkd+dzLK243mlEoWy9es15GZH3/Qzz4ztDZ33aEdcdIHfjb53co9CWnld/7XX6dM37e8ihlxJefWMn/nksD35X/9AgULmvZt7jZ1alTViA9yLSmv7WL7tm0aicmf/Hf/7/6QsrhCRso+3C3y77tdh452yfP+h6FH8gZ7/ZWXtb3j1ltCRsHA35/8DcjfoHyOyvGn9S03RXw/4myf/9OmTDbNWtygHU7iyo3wl/eUzwApYb7rHEagdmjbRudvbdSkqf6OZT/zcx02ihYrrm04b77+qrblK0QtVS1lj2UfupT47z26mDF3fkRp8Lh46NHueqxxo+nlOCivu2xV5JzeTuUqkX/ni5evMvMWeRUGYkted+HSFXYtduT/cPL0+L1OcT9vcAebUOQ6T8j+rr+X1es0YiNt2rQaMnXBisB7+jVp1twuXTxIoAIAAAAAAAAAAACARQIVuIQsWbTYZMiYMaKXSCgtb75FQ54n5bJEw/rXaiulI+5p580/9OzTvbT1k68BhOwLwZEpU2aTJ29eky9fftP3vZh7ioZyVbJk+jrSSsREnhebHlNx5eZ7E00a1LdLuNg98mA3u3RhyAjXNGnS6vJnvlJVZ1Olmjen0rq15zfK/5HuPUzWrNdo739cPmQEnpsfTUaDxKfBA7/UEX3AwQNeGUY/KXe2f/9+uxZp1syZ5qYWTe1a3MiIUeHKFM6aMV1D5kf1k+OmjAJzwo2YlpFfrgygGy0hZA4rCSSsd/t9aLJnjz5a/vY777JL5y+2IxKWLY2cX/J8yAgoiRq1apnPvghdScWVd8XFZ2bg+NOpfRvzwnPPmsd7PKIj3yVciVq/U6dOmRpVKprnn+0deG53M3bMTxrOpx9/ZGoEjcITsq1h/boafs/1ftq0b3OXtjIaNiadO3bQ6/g7W7cyJ05EjlKVY6f7noPfW15Tto3+IfJvRkabStzWqqXpHXi9V19+MWJEqt+rL71gunTqoN+bxC03NDetbrrBPupZvmypjhKUeY4//SR2o9CSsr1795h/7H0Uv+3btoa81vgx8HvdYcuDykhLMXtm3Obl/OSjDzSEjCqVkcnis6B5Og8fOmSXAtfYV15lcufJo3OFC/ke5H6RVImYNtWrtHTHXXdpxEXDxlHnGY+Ju/8U7PBhb65cmUNWwpUa3rghduWNR44YHvEzyz0JISWw162N3Uips5H5uh33+mLq5El26fyFGmn635n/NJIy97l21z1tzLbA34DzaI+edsmTLkQZ61Bc6fLadbx7lE6+/AU0pGy07EeFCxfReVlThZgT+Gw++7S/toUKFzaHfH8r4bjRoGfToGHs/xbcqGw3t/biRUHl3W1FgLz58mkbzvKl3ihIV7HFSZs2nY7UvZT47z26kBGjNWrWMtNnz9M4V1KdQcqFJ0vu3Vs8E/gnr+vKUju//D7ZLnmSB46/V8Yw124o8rrJgkqVx4ZUoYhP/p83JlKx4CbffO7y/YebhzsUqUAhkSxZcpPWN0+3K3d/sbkif67sEbMpSfkiOYCEIhPFN2/slf9AVG5YvCsXIJ5/6WXT8d777FpUUhKrZtVKdg2IX6H2R79N272SbFLqolzJYrrsSPk/+QAJ97Xy2uEeQ1SDhg7XtkPbe7S91Lj97P133zHDBg/SZdl3cuXObZ7o9bSpU7eulqry3yw4m6mz5pj8gZNimWtIdO7gzfETirz/ooULol2gxxd5/XdsuZ8P+72v7aVE5gYJLhNzserzwkvm3vu6mJee72O+GuCVZQwmpavkRu35Hr+khK/s3zEdIyWZcG/7ttFuyLrvQf5m3n/nbbvVaMJzwdLl5mjgAif4mHyu3N/lpXCcllL0VSqUtWsQctNATAscK4X8P0vy1H2uh/p/lwRCyVKlzSsvPq9zt8kNgBcDfytD7YXYwmUrTJYsWXVZyM20imVL2TVjvhw8VMsVVShdwm6BkBKd3bpEzp0EIPbG/TrRlCpd2nz1xefmpRees1s97nPM3Ui85cbmplLlKlomUuY9iy258bVmw+awn4fa6SRw3iol96T0ZXwK9Vm8dNVaPe+S9xN163ud8+T+6HSbwLicrFi7wZQpXsSu4XzkL+CdG0ydOUdL+P7OXMLnbfTYCableXYCQtLjjs1Srrp6JW/uScRMPiv99w2Cyy87/s+96XO8JFbevF7iT6aAkIS4zBObJm1as2f3bu1gImVAXXng4M9qKS8tc/RKMlVKp0vyVdbFH4FrlUr2WsW9ryTP/zj0hylUyMtnrF+3LqJT6YYt2zVpFfwe7mtlXwguay7lRv86/ZeWaBdS7lQ6IQj3Ou7r5ftZuXy5KVWmTMScqCL4/UJxryFzRruy5450cpBS2+LAgQM6F2qWrN41m9xrmPj7b7ospJy3dKiQUtPy+05x9dWa/Ar+XkVsvq/zJeWcpSNRfJOfQ/IpUhbeyZw5sxn+7ShzzTXX2C3n9jM+3L2H6flEL+0YI1OBSGnuz74caM78d0bn15WpQ4RMJ3FryxvNwgXzdX38bxP1d31D09gnzeX7P3LkiClfKuayzI6U+5WS//J9xSf5PuTnrV2tStjOAalTpzGr1m80L/TpbQZ99aX+PQ8YPCQisd+kwXXaxiRT4P9GDBwyTDs6NLm+vv4dyd9T186dzC/jx+njCcX/d+BXtkTRiI4b4chc44xAvcBkZ5QDqoT0SBWSbQ/3HwfEl2rVa9ilqORmq8SIYYGDXxDpeVqtYjm7BpzdsWNHAyd0+zVkRMfSJYtNGzt5vszTI0nVi43MmSF/B5I4vRSTp5eal+3N124PPqxtKJK4DL4wOhcyd0NsyFxTVSuUPafRLPK3U7RAXlMrlh2r/PPQBJOLhYS4KELS4ZKnMidaKDJSXy6gO3X2OvXJ+tLFi80XAwebdZu2avLUnafKxbtc6GzYukOfK5JddVXEaAkAiA+SPP3zz5PRkqfif999q23FSpU0hHSYC5U8leuZuFYjkZvActM0LslTuakUE/dZ7H+e3CxzyVMxbcoUjXDJ05g+6/3kdxBOTI8BwOXq76BKFghPkqfyef1Qty4a4WzdstkuefPjSwz88gsdoS/J0507dpgff/ifPu4SNlddFf4zSu7FyMhWGSlXuEgRTZ7KuoRLnooObe/WhFTGTJk0eSrXMvL566/IE+5+lPs+Qt0jcPdMndrVq0SMnpaRsRLS2V8Sy5LAu7ZePU2eSiLGcQnfmEgSS4S6dyDJ0J7dH9H3zZo1qyZP3XynwYoVzKcjmCXpKqNy5dpP5qe+VLn7jxLr1q3V+y9+d7cNPxDjbLZs3qyJPkmexkTmFz+X5GlcNGt0fawSlQlBqg7J70W+n9h+T9IxW+LmG5rr117sOKu+wCS5IAd4ieKF8uvB3JWnkInRJYD4JCcnQkpJBvOfPLzx6it2yTNx6gwzecZs7e3107gJdmvMZOJn6ZU2IaicASCkVIMrTerMnr9Ib/hLJxLpdegf+RTsmT7P66jo1es3aY+62Ph10lSzct1GfX2Z1H3WvIX2Ea8HpcScBYvtlkhzA8+dPN0rayQn3mPG/2oaNGykgaRPLl7kYiGUBx56RNt727fRtljxErp/hAvZf5zvvh+t22SkhLQxkZ6fbt+TiE3ngV8mTtbnrt+yPeI9gi/Yej7ZS7eXLlNGW/m7Wbtpq5k1P3LfduRxCVwedARVgJTMq1c7dKcpR0ahSqK+7V136EV6o8ZNtHfy9XVrR5ynSs/nwQO/0oREw8DjABDfBgwarG33Rx7SNpjcLFy5YoXe+HM3/6TXf/BnmzuflA4fS1eu0XDncUJGnwr3uSjHy3Cfj3Xr1T/rZ6c83vHezoFrpVnaI1/WZ9hRNo5sk5DzV/mcluf9PmWang/IdjkvdaTKgoRs95NRhLJNPuul/Wlc9NGEsl3Oj+XndefUch4hpKKFrEvIY7JdStvh0iX7l4R0hAIQnVQxkCoAzrlUM7jcyf3rkkUKmfnz5mmEU692TX2ujnosWFDjpeef0xGcsl0SkPkLFNTnStUlUbpYEX1MIpSqFcvpY5KULJgnp65L+E2ZNEk7KMnzShYtFG0UpyicP0/I9ygSZrsoVaywqVgmMlEr5DpKni/l3CWkapp0xpLXke9RHpNRbNJK7ItFyX4d8Rh4riSYQpFOZfK+7vX7f/Shtv7Rp861Navp78n9XFJe3ZF1F5eqo0eOaAh/2dlRP4yOOG+T+yf33tfVPhKalATWe4YLvXuGAwYO1hCffPaFmTR9pi7LeeiMOd5oVOeBhx7WUaMS7v1iQ95vxtzI13q811N6X1PuUS5ZsTrKPc2zkepTswPvKyHfg9yfmh70fYYj34fE8G++C5xjej+n3EubNmuuyZEzZ8TjEtJBQNp+H0Wftkp+Fvn9ySAEiSkzZuvPIr4f/bO2L73ymn69K5Xc/bGeZnng3FfCncdKbiK2AxkSAgnURODKoElPHFfbH4gvUupKylxIb7Fg337/o12KSi6upWeX1DKX5FG58hXMq2+8pXNquHk1QvlsgDe/z9l65+DyJHPD7NrpdRJZuXaDhozAd8kh6V0v5SODyUWORJf7u+kNASn3IMmpsyWkps+ea4oVLx4xl5nMjyElhWWklZC/CQn58A+WPfDcvm++rstSpz/U3w+SrphKZd/bxTtJXrF8ubbCjbiTcL1ZnZ07vJuQS1etMVWrV9flTRs3atmh4LlcnDUbt2iSX3p+Hj7szaMiNzdj8l3gZL54iZLmxInj2qNvjy2NJDc8JYKN/eV3Lb0uvVHluJwrV+6Im2a4PMlxSvbLl159PcqN+VCOBy7opQfz9GlTdV32N+l4IPu23+uvvKRt0aLnV04aAEIpVdorh/fLuPBlu1o0aag3/vw3//zk81nOJ+V6R0ZvuHmwChYqZJ9hIm5cSishnUfE8y+9ouH39nv9Qs5nGKxrtwdNwYKFzLQpk7WEXJ68+XQu12A33HhTxPVTz+6P6nllMDcvlJ8kXV05Vik/KPOylSsfvsyk/Lzu/EVKHP86aYpOCyAjfiTkmC/bYzsXLC5OMlpGQjpCUb4XiO7dDz7UxJ5UHJCQjjq4MCSBKMkSiRdejjpaUirkybW3u+cSW/KaoT5Hg/158qRdSlgykvVs5T/PV2xfX35P4UqyXurSB/7GJcTmTZu0/eKrQaZKtepaEUQqgsr9kz4vvGjqXXedRigyB6pcY2fO7JVjdvcQ3bKrLiJlpf3zpC5esco8+XRvHSksIfOOy/tJkjAUOU90yUh53TrVq+r2e9q2Mw890l3va7r7PHJP86vBQzVi8tmAgdppLmfgfSXke5B7o3nz5jWbd0S/v+TMX7xMQ76P+7t0NqkCP5f7meWeaspUKXVZtrmfT86tU6VOZRo3baaP+UkSWu4zuHNduZ8r91uFe11p3XKFipVM955P6O9UQr5vIUlaGWSQVJBATST+iapJoiI+yYHMP3m7I+UcZH7JUOTiWshFV6/HH9Nlmcz8sSee1Ajlttvv1PbmG0P3lgJkTgA3Ilrmu5CQE7oxo380H33wvtlu99O720QtsSFlUCQkmf/W668FTrQP6vbfJns3/sPJmy+/fth+1v9j0/3hB82ff/6p22vUqKmtVASQCGf8uLF2CReb1au8+UFC8c9FIvbs3mU6tr0nIjq1b6P7mtOn9zPaZsjglS2dM3uWjtK7o3WrkCOhmwROGmWej7WrV2vPzztb36o3ss5GRgSKO25tpWWOpIS6m18mnKYNrjN1a3lJXSFzDUvg8iVli4Rc3MTkRNBNBdnfmoTo/HTbHd5ne9p0Sae3J4BLx5VXnv91r/t8rlapvI7ecDdoxN1t2mp7f+dO2t7YrLGGO1Y2bdZcw0/KlMdm9J50wNuxfbtpd89dpqYtu1+rdh1t/eRGl5Qsmzd3jk5tEVsu6Trq22+0/KCcewgZlSrhJzfVJHkqvwNXOlEqbMhyg3rXaqxds1q3A8Dl7LdfftFOL+VLl9DAheO/ppbqDo7c85braOnQOTtwbQ1cSLNneaMnr7fV5GTU+YP3dzEHDxzQ9RdeelUjNiZPmqghpkyeZOaG2H/lfnqmTN58nzKARKJLpw56b9IlCYPJqE6XRHRVSkW7Dt75q3ztU0/0jKjGUrV6DY2YVKnqJWFXrliu8UDX+/RnlnPkK64In/6TSm4S0ilx8cIFZtLE382vv3hVKeV3J+ezoZw4cUKTrX7Finnzu375xefaBnMdvWZMm6bvIdMFte/o/cxjx/ykId/3px9/FPH/lVSQQAUAAAAAAAAAAAAAiwRqIml/z112yZivhgyzS0D8aH3zTdpKD2jnt0lTdYRKz0cf1nDmL1muJZ6kHn7nju3Nd9+M1GUZUSWlqiSCST3yt997X+cZkN58uHzJ/iH17CWkhKSbi0nUqlZZ2xHf/k9bIXMyPPzA/abvm2+Ya2tWN1s2bzKvvfmWfdSzZ/duDZnD4pOPPjCVynoj9YrEUFJSauNLb3+ZQP/1V142P37/P1OySEEzY/o0LQMhZJJ3N9F7m3bttRVDR4zU1t9jEhenuYtCl0iRESOOzLsiI0JcjPpxjPb8k56KEtKr0M3NK6NA7mzdSpdXLF9mli9bpst+n305UFs3mm/N6lWmfh1v1HNM815MmTRR54OR13VkHpVw5O9mx47tWsZ3wXxvDpoXX35VA5cn+ay+5cYWEceumMr4HrIj+Z0bbmppHnv8yYg5Rly8/Nob+vhVvnl4ASC+5AjTE/9c+c/ZZLSmROcO7cyIYV55s1W2MoVUdvBXd5DSvxJ+MvJTRnKejZTQr1PD690vGtSrE3JaiFOnTpmKZUuZ21tFzsF1NjJ1iqhQpqR5vMejuizntX2eeUrL+rrSvn4yF5yMKpC55/bt26vbZPnwoUMacs57ocsKAkBSJ+XgXdUAXHhfDx+mIWXm3fXFxm07zZYtm7WSUmKV2sWlIX2G9BH3HyXWbdqq+5izY/s289OPP+iUYDLyWa6X77j1Fh35WLl8GfNc76d13/RP+xCTd99+S0N8+P575tGHHtBlP5nOSci5n9wDlZDRqnJvUrjytY7MDyr3n9yUC26aRykPnClTJvP+u331a0eOGK7Hr2lTp5h06dJphHNtvfrm38C5cfPGDUyLJo003M8sc+OGI787d64sVV3ER/3eN90f8vIA48f+bB66P/S8sTWrRD+ujho9Rlv5Pwjl+T69tR06ZJCO0pV7W/XqX2dWrlyho4Ql5Pt+47VX9HsXGQO/k6SAuyOJRG6COvF1IQkEu+POu+2S0TrlYnTgQCbhXHPNNeaULXUaG1Ku0uHkB1KXXk4IJCRB7+Y3lSSVkydPHrsU3aeffGyXIv3z7z8a50I+eDu2a2PXIslcVc7WLVs0hCvxJoqVKHHZzhVxKZE5bTKk9+a9EG5ONCEdQ8JxJfPuuu1WDeE6n7jSJc60qZPtUuy48uihPNSta0SJa+emm2+xS9EtWxqZHHalBpMnT6GBy5vMAyhiKuMbPHPQ3fe0Me06dLRrHrkBLzc8AOBCia/OanK++T97k2bD+vUav//2q66HE9M0DrEhpc38gueQduLyPpWrVNFWEp9+c2fPtksxk86wocicXwAAJJSnn3xcY/QP3+u9FylPKoMubr+lpX0GEHdp0qSNuP8okcLey3GeeuJxbf33wv2GDh5kl+KPm5ZROr4Fe/O1V6NM4Shy5MhpDuzfb1596UUN5+TJE5r4nDBunN3iKVSosF0Kb8a0qabVjS3Mzp077ZazkySzaNKgvkZc+Tv5pbeDV87F9fXqmFdffMGuRXdVPEz/ER9IoCYS/8WRm+gYiE9ykf/oYz3tWiRJFLlkkZuUWkboud5hLsJxo60AIbXtb2vVMiKkt5X08ipXMnK0aJasWcyRI0c0gkmvqmAygk/iXMnIgM07dkfZj3OHSN7+MmG8KVXa680ksmfPYbraubJw8ZLRJ/6J/CdOnaEhoy/6vfeO3RopQ+CzV/aRAwcO6D7rd+fd92g7PXAi6vf9qO/sUnT+/U5CXHNNNm1DkZu/wV/jRqCEInNpOP/YG6XyGq7TAi5fNapUjNiHw43CDlarzrXayte5qFaxvBk+dLBuT5Uqtba4/JQpW9aULVfOrl0Y8h4uHHlP976FCheO9j3c3OpWjZa3eFUB3HpsyNcEf23NWt78kkhYUmHkbIJHiAaT45XMi1S5StVon6MxubFZE7tkTNv2HfXzc+7CJRGfqWczbMgQu+Rx864GC5fMjEnGjF7v+uCf59fJUc9Dwgr9rZj//v3XLiGu3P+F4192pEOS2y6tjIpJCDIaxX1/8xdHr5ISWzL6w71OUvL9T2OT5PeFi4NUCJCIadRWbLjXwbmRkXr1atfQ0XUyAnjfvn32ESDuJBnvv//YqH7dKNezUoFOXFuvnrbB/OduefLktUsXTv+PP9T5TB25ByWyXnONebh7Dw2//fv3ma8GD41yXzNP3rN/n/JzyUC936dMi/g6fwSTe6Dffv+jLvd6urdGXEhu68efx5nyFSraLcbMnzfXLsWOvMY7738Qq+87MXHXLZHIqD+H0jq4EDp38EqUyg2iDp3u1eXgntPuw2Pl8uU6SXO48Nu2bWtEIkySE7i87dq108yfOzci/Eke55+//9HJwSWCuSR+VLIt1PaYTZs9V1/vaGD//HnMT6Zwvtxm4u+/2Ucjdb3XG3X12ptvaysmnmXUApI+d1FWoGBBbbNmzaoxckToEXVLV63VtootDeK32pb+y2hHsDpp04a/AC9aIG+0cGVbQpFy12Lz5k3aS7dM8SKmbImiug2ICzmflIshKc0bk5huJH386RfautLnuPx8OXiYGTP+wn4mDh/5nRk8fKSGI+/p3veR7o9F+x6e6fO8Rr+PPgm0z5n3P/zYPPtc1N7KL77yasgkwuZNm/T58rVvvfOeLkvJdiS8yZMmanv/gw9pG8rs+YvOeuNEynpJuXx/eV6xZOVquxTdls2bdUSmRPeePc2wkd9qCbUFC+bbZ8SscJEidin+nbEZ0FDnEi6QdA0Z5HUwXrVuo1Zoqh6LktDx4fcp083fp09rSEnnuJJjoniw633aJgXy91mpcuXAteYuDSSOmKaHSMry5S9g5ixYrHG+91vlNVyiAUDi899/XL9+nd0a1X//nb0DmUy5kBhcSV2pluevmCcWL1+l1dDkvqZ09H/j1VfMb7/8Yh+N2YatO7QD/z///K2xfNlS0/i6enpfNJQ9gXPogwcPmrvatNWIi0FffWkyZcpsPv9yoHno0e667VymsBCTp8/SzpPHjh7VkDLM7/V9O+z3nVhIoCYSf5k1KTsIxDc5WIoejz9hOnXuostLFi/WNliatGm1xni48Ktbs3pEKcnY9IQBZMSzjAz0jw50/J1JzodLxM6aOcM0blDfPHR/F/Pvv//q3KjhSG9rV7YCl47mLW6wS56vBngJoVDCjR7Zv3+/tvkDF99+JUqWtEvRyYiT4PD3Ngxl3Ngx5ro6tbRsqpShDt2hAIidlSuWa+svUR5KuBtJkhxwJXhSpWYEKpKW9995W0Pc0qp14Jh5TC/a/e5p295cky36yH9/CfQGDRuZI0e49kosrlNTixtu1DauUqRIoQlRmTtcRqNKCDeSM5zDhw9pZMiQ0RQo4HW4+uKzT7U9m+CbXPH5mf3fv15p4+DzCHkPt4ykIXj6BUfK6adOk8YsWhR+/vv4liVLFrNp00aN85Epc2btWDD2Z68sdlJQvLg3J9wtNzTTQMKTqXrSBPbpi5F0tBKhSmrGxU3Nm9olABcDubdyNkePRq+QF9/KlovaoUq+r3D3oIR8Hu/ft8/MnjlDO/p/+slHJl36s4+iL1W6tEmWLJkOdrq/870aUnll3bq1IafPkGNjnepVtPTv+Rg+1KvOkjZdOnPb7Xfqckw/XygyH+1fp06ZO29rpVGnRjWtICf3c5MSEqiJxN/juuejj9glIP5Vq17D5MufXxOhbuSdnxyc3Ygtv5iGzbe96w4tjyBzB/Z65lm7FQjts/6f6LwAEilTprRbPT+MiVrfP64GDvVKAd99e+uI8nCNmzTT8m6hyPwCUt73swFfRRuZjYvX6tWrzOO9njbv9vvQbvHKvAST45r8v4ebUP+X8d5+OWTESL154Lzy+pt2KZLb36rXqKmtI+8xZnzMvQUf6BK1p//CpSvsEnDu7mzdSi80pCRQ5y73a4Tj5tH1f9ZLVYlnnvJGr9arF/d5UIDz1f3hB/U802/EsKEakqSQ0Xplihc1H33wvn3UIzcOwimUN5fON/Tvf/+a8qW8G/NIeMuXLTNbNm/SG0qhyijLsUjI8yRCkefIiCBHRt5JSC96J1zp2uqVKmjIvuLmO49tFZLrGjSIMjXEtFnnVqIsJtddW0vb4NFeM+bOjzhGIzpJMLtEdvC83kJGNEiEesx9bbgIN0VCnRqhry3u7dDW9HnmKdPtPq/yUzje99MpWmJK9kl531BCPZYvX37zZM/HTL9339Hwn68Kue5yXyNl0du272CaNm+uEUzOh0sUjtppMJj7vYQLx78u5dKDz4+Fe447Zsv/j5tDznnlpRf09ynHiuAb0P738HPbQz2GczfuV69iwMWoSdNmeh5RsWwpuyVuFi5baT58/71o81Mj6XOfnU8+9YzdEpV7PLbVJWTe9VCfxVKy3W1ft3mbWbZ6rWl9+x26jsTjOj0ED6jwV7g5W2d3cebMfxoi+HPKOW47J3fqHL2KQ/D9IJcU7PX4Y/p1Em7/+Wqw18mw0fX1zF23t9ZlEe6ept+Xg4ZqW75UcfN74LxWwnnvw6hVJcXpv72fXUptOxu3RVYTdJVRZM7ZmEjJYSHnNNIR++UXvM4r4bhkbvA1W5u77zArV6zQcDJnzqxtqhADcRIDCVQAAAAAAAAAAAAAsEigJpJMmSLLC8mcksCFIHNBOlIiMpQpk72ehTLZtFOlWjW7dHatb7/dLgGhff7pJ3Ypeg8w6ckfH2XM166OPufVO/0+iNazyXElLKXUX6ivxcVp4IAvdMRAjRCjWhzXy/SJHt117jMZgRIcjpQIvPHmW+yaMcmTJ7dLkdatXaPt62/31Vb0fu55bWW0VEw63dfVLnkyZ8lil4DoZECFhFSOcL09g60JHM/k8U733achPWtl/fRff9lneCqVLWUmBc0RPen3382IoUP0+SdOnLBbgaSl9c03mVtvOvfyr9J7udVNN8TpaxG/Hn3oQW2lCogbCSDTPPT/wivJLOV4W7ZoqhGO//NS5oGUkOtrmc9JuFEFFStVjjYy73x8/e0ou2RM3nz5zPZ4uo53369M83P/A97vR2TLlt38+eefGohuzsIlZurMOXp+9uIrr5kPPu5vHzHmxptamtE/j9eQx2RuXb9J02bo14eLzl1DV3GQx0Lp//kA89Krr5uxv/xuqlavbrdGNezrb813348OPO8185vv2luMHPV92NcOfuzTwN/OyP99b1576+3A9c6HGqN+jFp+V74X9zVDv/5Gfwdvv9tP47rrG+h2R543a17MpYdlRJU8L1TI/GWO2ybnxa+/+bZ5/6OPzWuB1m/mvAUaMq/koGEjzLPPvxj4f5xtihYtpo9/98No83rgZ5OfQa7nJO68+x59TLj3COa2S7gpCXB5euIxby6+8/X6Ky/pFEG4dG3csMEuxU2fZ3pp+/xLr+i9g3E//2xGffuNbkPicedVwRXv8p7jNHQnT57UEMWKF9c22P4D3vRPwecNTZu3sEvRffP1CJ2/1T+H65o1q7StWLGSto7/HlS4UbAyPYWQfdBPKjw0bRq98oSfVGaTkNd2o13dvYPSZctqG1vffRNZbTUUdz5bvkJFbZ2GDRvbpUjuPOnqlFdrm9hIoCawVq1v0+HZUlZk29atGsCF0qThdXYp/Fy7TzzWI3Dxv80UCVywyL4pMeqHn8zUyZO07ElwCTW/urVq6CTVC5Z4864B4SyYP8+LpSsi9jMJKVlRvvT5l9J7/dWXtfW/tuzzDerV0e3BpHyb0+6eu+wSLnbfjvxa21y5cumFc/DFs5xQjrA3P6U879yF3g2j4BCyX+7ds8f0ef7FiH3q5huin3zK/lOqaGFTqFDhiOfd17WbGfTlgLAdV8TXw4ea516IfG2JKuUjT1Cbn+f8cLj0bN2yRUPLVVYoZ7dG1aJJQ328VtXKGlLWXNZbtog+f1in9m0jPuclOrVvo9tdOUwh87Dg8uRKkvnjvQ+8ElBP9X5W15esiNoBqWSp0hry2LLVkTcE/K8hcTbvf/hxtOetXLtBY9rsuWb6nHn6+LJVa+2j3ns4we8zafpMs3Ldxihfi8SzdMliU6JIQZM+QwZTtlw5/f9Ys2Gzada8hVmyeJGpXL6MlvgKNWeTqFCmpJ7juf9nKU8qISV8a1SOPL+TOfh/GDPWzFscvRTwKXsDZ96cOdrGxqqVK/WY6N5X5o+6tmboRFlcyHFYXvOp3n0i3uOPwM9UMvC7kkB02bNnN1mzZjXrt2w3hwP7RJ68+cwNN7XU392H/T/TTnUSUgo2S+B5st05depUYD84GRF/njxpDh7YryGv+3n/yA6gfvKY348/j9PXlRusTz3RUzuLSpLUX2ba/X/WqVvXpEuf3jzT6wmTLFly3fbFV4P1OdI5RF5bbpQGd9irUrVaxPvOD+zPTZs1N6f/Om3efvN1s3DBfA2Zssf/88m0KfI1+r2dOGlWr1qpfzcSMvVJunSRc6rJ8872eS9zpAb/vq688gr92tmzZtpnea8lcdfdbUyHtveYM4G/Y5mb3Z/wzJQps0aJEiVN/euu1xuq8v8jN5Ll+61UqbL5asAX5tmnewXe50+NN95+xyxa7t1cllKB8h716kfe6xDuvSXkfOlSsmjZSrN5hzdtSDD5nUlIYl7ap58NXUJRHvtpnFdOcu6iJRFfJzFlxiwNR7bJlBBpAvuzLIu1m7bqct7A35lfzyd76Xa5rhLSacV7zdna+uO2O7w58vxmL1gU7XmTpkXuU7Iu32+w1YHPDQn5fHdmzl2gz+/7Xr+I15o2e5591PPLxClmzcbNJnfuPBHPkQhV+vnVN96K0jEDlwY5nvx9+nSM9zpj69cJE0yR/HlMx3s76zQT8jmApKFwvtymaLHI+9wSGTNlMtUrldeIjU0bN2oImc7JfQ75XVenlvn5p9EmV67cUd7r0y++DFybN7LPiu6WG1toCDmPeev11/SzSz6j/a/jpnkQ4aboadaogQ4SkTLS/q998uneZpqd51RK/4fiprcQUqpaSKnhHdu3meYtbjCbtkefEsvvpef7RMzVf/ToUW3DkXLo0lG7y/3d9Pu7vkFDM/qH703XBx6M8n1LyHma6P3cC9omtivy58oeMburfEjJxUcocsHQvHHUnmrwyH+s8B98n3/p5cABNHr9a0d6uYS7sQ/Ep5vs6KmffvxB23CqVK0aOGg9ZAoUKKgf+nIhFkxeK/h1Yvv6l5tBdk5OuXC8FLn/9/Xr1ukFeWzJvDsPPPSI3szYsH69znPmyI2z5ClS6Ogn4b8YD97PZP3IkcNm6uTJuu7IB37WrNeY115+0fwyYbxuk+cuX7bUbN60SdedcPuujGqoc21dvcEnLrULcCEjLyU5iLOTi+nGTZsFLo7Gn3VCfOnRv3nzJrNl82a7JWZyg6Fi5Sq6zy9auMBuhViwZLmpUuHcejwift1+5106b5AcE2X+SESSkUbdunS2a5cWSZzKzWfx8Yf9Ij4r5AJcEgPueifU9Y+7QZ4j8BkjN7/lgloSlnKz9dNPPjIrli/Xm1byOm6+ysrlSmvrfz1JoMrcee61X33jTXNP2/a6PODzz8y2rVt0ZJJ48P77zNgxY8yQ4V+buvZm+rQp3rmBdHD5ecKvpkzZcoFzhiPaOVASdM+9+LImpirZ90biatCwkSaf/Od9sSHJekm6zLA3hkKRfe/4ieOamPRbu3GLzgVdu3qVsIlaP9k/3f2QRk2amt27dur+HFfzl3hJXemI6v8bcpo0a242bliv58qXohVrN5gyxWM371xM3HHjp9E/mkceuF+riEz4fZJJnz6D/u4a1r9WHxeSIFy8fJUZMmigea7303ZrVO71pMqD66gUfKyTdf//2er1m0yq1KmjbPN/jXTekxuaou3dd5rpU6fosgj12tLhVEhCVUgiSEZ/yuhq6SAgz5Hr9FtbRu1oJ3MKf/3d/yJe6533PzC33na7XsfUq11DtznyGj9+PypwHfZQxLrcqCxxlnlQg8nXbd2yOfD6kfOcyjYh12Kff+olnib8PtmUKFlSkwvVKpXXpJeQygD169TU82Y535bzbPl6/+/Sca8rj73+Vl9z1z1tdMSMu+nrfn4hN2ilo0VcjR47IcYR8Imha7cHNDHaqH5dXfePWpLfjftdyPKhQ3+YimWizv35cPcepucTvUzRAnkDn4u/meIlSgQ+DxcH/k/2mPIVK0V87sv/hfyfyOd5unRpA/vv1RGdU9YEjpuSmL+2RjWzfbu3TwtJoD78aA89X6geeJ5c37iOKxPGjTXLlnpzO8vzZIST//9X5pKW5P/uXbvM8uXLTKPGTXS77A9//nnSlCxSSDsj5MiZM9p+4fYJ6UD7ZM8een9BbvTLvjxrxnT97M8SOIe9KlmyKF8rCdSChQrp36acG+zZvVt/HyL4PeSzQhICrmMhLh5u//jkww/MW2+8psvnQ+4zyei84H0E0clxwiW/4pPcv5PPiTGBz/zYypIlq/no089M7sD5gVybDPrKq3bilClb1hQq7J2PyH3BDBkzRnTO8d8nTJ06jVZWkGuJ70d9F/ZeolznCPlMkk5Oklh1wn1N8Pa3331fc3Nyb/Thbl31c9w9RwR/vd/nXw0yxYp5I2UXL1poejzifc7738Mtj/t5jHY2dNx2uW8q90+FXJOtW7vWDB862NS77nrdJoK/hxY33qTHd/92qS4jGgfOm4X/sX4ffaIds4YOHhh4r2XmiV5PmRtuulkfk4par73yYpSfO6afObbcMSFY2RJFIyoUhiOdG0mgxgP3n+A/kMaUQJWyqjKy5Ww3YgFcvC71BCoubiRQkdSRQE18coNbEl07d+zQJAMiXQ4J1OAbsE2btTCfDvjS1KxaSW90yohBuTD2X/8sX+Pd0D116i/T+uYbtdKOjJiRHsx1anjTQ5QqXcaM+/X3c0qgyogSV169YJ6c2spoHEnuS+cTKc0rQl2Tbdiy3SRLnjzKthlz55s8efJG2YbLi+wr/xv1nen56MN2S8zk+fF5P2TRcq/zYebMWS7L/TC+E6h1qlc1O3Zs12W3TUaX7N27V5cdeUySJXVqVI1y09BxX9u715Nm+LAhuuy2uf8nWff/n63fss0kT54iyjbpODJn1iythCLHMjmmieD/6+DXluTP/v1eKUA3OkZG9cl5u1QuefrJJ8zCwLGvS6f2ZuGCqB3vJIkryVz3Wi6B+vYbr5mPP/xAtznyvjL6+vZbvZuVsn6uCdTa115rho/8Tkde+X+XwT+TqFCxko7UlZGmMpran0D1P09GlkrirXSx6Pck/a8rpX4lWZo2XbqI7/mLrwZp5waxY/t2/T+Oq6SYQJVRU1L1YcjAr3T9uWef0VbI70Zu6rdvc3fI37+QEsn5CxTU7TKaM+XVKSOqQEnn4wVLvQ4h7v9IyLm4/B+Vtn+r55pAlcRjuZJeWWZx591tzBtv99XOIQ3qeZ0bNm7bGSWpOmS4V/6xbv362sr2F19+1bTvdK8mxSU57riftUmD68zaNavNr5OnauLgs/4fm9df8apTSQdtKQ/fqH7diKSzJFAlYSqdZ/r0ftoMHzLYTJg4WUdE161ZPcoUayRQkx5Jrsso/BMnT5hpUyI7pAj5/5akuJRddvtHcAJVzh3f7fehyZ+/gNm6dYvpEPi7CUU61Hd/7HGTPmMGc1PzpoHj3bdhE6jVqtcwb/R9x0yfOtU87/vbvFxdqAQqcD7cMSFYbBOolPCNB3IADT6Ivvhcn4jtwVGuVHGSpwAAAEiSipcoaXbt3Kkj8UmeXp6CR69MGD9W21E//qTtXbe39to2bbUV6dKl16hXq4b2Gp5pS6K75KlYtXLFOV8HyegRuWEm4ebIk9Gjcl3lkqfhSPJ0376oSRRJtohy5SPLveLS99vkqZqMcmUmY5s8jU/SOUFGkkniVALxwyVPnWNHj0ZLngpJTspINknI+ElCV26sbdq4QcMlT2PjOzvXnXy9hMw32rxRg4hpJKSSQzjz5821Sx4ZRSv7iBsNKCR5+vffpzV5KqTTyZVXXWXe7PuOzuu6btNWDUmehhKcPHUkIRdX8nNK8vSNV18JmYgOJiP/hSTj/KTssp+U9ZPkqVR0mTx9pibh3O/VTxJhMkpSbtI7kjyV95E4n+RpUuUSh+06dtJw3PFMkqdCRqIG8+aELWg++bCfrsuxJ2WqyN/dgQP7NXEoEZ/lwv3JUzFyhDe1SeEiRbUVwSNS291zp8YDtpOalHd8vk9vXZZjuHPvfV3sktHkaZGiRbX85Y/f/y8ieSrGj/POXULNXVgqsK9J8lTI36zo5HtdJE1yDilJcZds95PtMc1ZK8cS6YBXsVKlwN9BZi0hHnx8ETL1w7CR35piJUro86TTYKlSoauWyNfLfM4ZMmQ07QN/m957rLCPArhUkEAFAAAAEEFuRjW+vl7IuVNx+Tp9+rTOWyekLJRo07adtn4nT57Q9qorQ19qSlm+czHbdzNMRh01aNTYrsVMEq5i967dOpLAHyJXbkpTX06KFiuuySiJxOrMLEmMcPMTIv788++/dikqmY9TXJMtm7aOVFsQMirOjYyLrWeefMIcOHDArhlzbd16Zuwvv+koPBV9WsUIx48d19Ydq8aN/VnbYFJu2q/fR/3NHXfdY3LmymVSXH21RmKQ0uzn499/oydfM2TIoPPGFSxUOPJ3GMLx497vzu/LLz7XuBxI4lHIKFwpOe3MmuF9Xkq5SUdG9ophQ72OAQf27zdXX53STJ89V8vzCxl16R95eaH4j73u7y6UeXO9zgXX1qunrZCRso6UcPaTkt2///pLlOmBpIR7k6b2PNb+jfm5ubCFK+WeNm3k7w0Xl3DzOzrNWkR2uGveuKF2tJgxfZquy8hSCadQ4Pgjpf8fur+LdgI8ceK4jvIP9tqbb2srnT9qV6ts3njtFV3PnCWrtgAuHSRQAQAAAAAAAAAAAMAigQoAAAAAUMGjnZyjR4+Y1L4e+IsWLjSly3hzFc+YM19bPxkZE0qokUMx6fHIQzrSSSJL1qzmy0FDtESahBuFE4or91u+QgUtxeYPUbv2uY00w8Xt4w/6mc8/66/h5tKNLZn79/7OkWUz40rmFxzz02h9LQl/6UrEH/9xyu8KOyp+9apV2go5jsiceS+/8JyOjovL6OQq5cvo/2Wndm3M8WPHTL78BbT8rLgyzEh8kS+/N2LKvacbBSdefv0N8/xL3mim6pUiy43L9yujNJ99upfO/ynvK1E4nzdP9IUm7y9lYs9lvtRw/D+vkJG4S1etNZs3b4rys0mEIr83mW+2YqXKuj5m9I8al6reTz1pl4yZNd+rAiGjS2VOUkc+L//9918zZeYsu0XK63ufxTI1g5Dj2aaNG03efPnNAw89HPF5KjF0hFeS+kI5eDBytHblquFLLUtZYVG5qvezyXz8sn/I3L6iSODzffDArzT8/D+L7Bv9Pu6v290o77O58spkdglJ2Yfvv6etHAudn8ZNMIcPR86RG+yWVq11DmwpVS37k8x52ObO2/Wx+x98SEO4+Xcrli1lpk2doiOVSxcrYg755t8VUkL97jZtzcoVK7T0uMz5+enHH0WU0f5y8FBtAVwaSKACAAAAAFRaW+4vWNq06czJk5Hld1vffKO2T/V+1uTJm9esW7tWw5GbU6GkSHHu5SZlrj0JSXw91/vpiHn31m+JOveh365d3s1imQPNfyPeRZ/A6+Dy8fabr5vXXnpR41zt3rUrXspbLl+21DzcrauZMH6cBi6Mq8OUtL3mmmvMls2bI373o37w5nQuX6r4OZd+zZAxo4bMQenmH5008XdTpkRRnWvVmfjbb3Yputx5oic9Ze5WiUaNm5hmLVpEKc/qXFuzmhk2ZLDesHeqVa9hly6cuYu8+TYloet/77Nx5WT/Pn1a23De+8ArCdy04fWxen1JXEiZ1o/6f6ZJw0vdcFuCV2TPnsPUql1Hl4N/VwvmzzPZsmXXxKlLngYnlq6vW1uT7re2vFHnDHb8JXPDCU5G+kvrnk369JEJrz8Ohu6sJZInT66tS/q2aumVX23cpKn57ofRuvz8s89oCEl+LVrudYzYsX27ubFZY/2cd4n+uHSMQNL1zttvavvN/yI7TMi8ziOGhk9adrm3g6lRpaJ+lkv58xdfec3MWbBYH0sZ+MyQELff4c0nHEz+rvwy2XmkJdn/xVeDI2KfnX+7ZJg5UwFcnEigAgAAAABUuPn0UqZMaf44eNCuRY4euuXW1tq2uqmFhrPP9sIPljFjRrsUO/OXLDff//SzhhgyaKApkj+PJqNiGt3lRrpWtSNY/BYtW2nuvPseuwbgUuFG0MvooFD6f/yhWb1qpXm0R09TpVo1s3dP6OPU2Rw5fFhD5lOVufFcokoSPy75I/yjBu/rer9d8qRMmcouRXryse4aOXLk1HjnTS9R4Oc/DjsDhwyzSxeOJO2+++Zruxazdh0jR2zPnLdA2y1btmgbjr+DjlO9Zk2NUG65sbm2ufPkMQvnR6+CcCmSkXMS4o2334k2ilfccest2rrPTUkefj18uG4Tv06eapo2a65J54UL5puyJYtF7M8xkXkgRdp0UecurVmrll06uxQpUtglr0NJOB3vvU9bN6JY/k5lfvV06dOb4iVK6ja/tu06mMyZM2uSS+a2XL7MGwHuhBuVjouX7BPFS5Swa5633njNLoX266SpZvOO3fr50D5wjMqRM3o1iuIlitulqObMmmmXPClTecfvevWvM42aNImIsuXK6/bkyRnNDFxKSKACAAAAACKM/22iXYqqxyMP2iWPjASVm+pCEpb+8rzdH/aeGy6REVvJkiXTEo2uTKOQJGyu3HnsWsyyXhN1dMxnA74ymbNkMSNHRN5QBnBpqF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] (if: $vek is 2) [ První ze tří částí máte úspěšně provedenou a sepsanou. Vaše výsledky potvrzují předpoklad teorie, že v tomto směru lze rozdělit populaci do tří kategorií, které se mezi sebou liší. Dále se tedy věnujete zapracování těchto informací do článku, který hodláte na konci výzkumu publikovat. Po napsání příslušné části textu je čas přistoupit k dalším analýzám. Znovu jste sedl k datovému souboru. Je na čase pokračovat druhou částí. <img src="data:image/png; 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"/> ] [[Pokračovat na další výzkumnou otázku->25 - Úvod hypotéza B]]

Po vymazání všech nekompletních záznamů, vám zůstal datový soubor o velikosti sotva tří set osob. Takto redukovaný vzorek již pro tak rozsáhlou populaci jako jsou všichni občané státu není v žádném případě dostačující. „Sakra,“ řekl jste si, „není možné, aby dvě třetiny lidí neuvedly svůj čistý příjem. Ano, ta otázka je trochu ošemetná, ale dvě třetiny?“ Samozřejmě, že to tak nebylo, ale vymazal jste ze souboru všechny nekompletní záznamy. To znamená i ty respondenty, kteří sice uvedli čistý příjem, ale neuvedli některou z jiných proměnných, jako například velikost bydliště nebo politickou aktivitu. S takto poničeným datovým souborem není možné ve výzkumu dále pokračovat. (if:(history: where its name contains "31 - Řešení missingů")'s length >= 1)[ [[Ještě štěstí, že jsem si vytvořil zálohu dat!->31 - Řešení missingů]] ] (else:)[ [[Ještě štěstí, že jsem si vytvořil zálohu dat!->25 - Úvod hypotéza B]] ] [[Znovu zahájit výzkum->1 - Postava]]

Po doplnění chybějících hodnot Čistého příjmu průměrnými hodnotami ze vzorku jste provedl korelační analýzu. Výsledky sice prokázaly statistickou významnost pozitivní korelace čistého příjmu a hrubého skóru (text-style:"bold")[Dotazníku ochoty], což by naznačovalo, že bohatší lidé jsou ochotnější k vystupování na veřejnosti, nicméně tato korelace se ukázala být velmi nízká. Věcný význam tohoto vztahu je tedy v podstatě nulový. Takový výsledek odporuje současné teorii, která předpokládá pozitivní a věcně významnou korelaci. To znamená, že jste zde prokázal nedostatky v současné teorii. Měla by být náležitě korigována. [[Zapracovat výsledky do článku->28 - MSub Shrnutí výsledků B ]] (if:(history: where its name contains "31 - Řešení missingů")'s length >= 1)[ [[Vyzkoušet ještě jiný postup->31 - Řešení missingů]] ]

(set: $vysledkyB to 1) (if:$tabulka is 1)[ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, kterou jste měl za úkol ověřit se ukázalo, že ono ověření opravdu bylo na místě, neboť se zdá, že v současné době platná teorie má jisté trhliny. (text-style:"italic")["Začínám se těšit, až ten článek publikuju. Tohle bude výsledek, o kterém se bude ve vědeckých kruzích určitě mluvit."] Teď je čas přistoupit k poslední zadané výzkumné otázce. Znovu jste sedl k datovému souboru. <img src="data:image/png; 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"/> ] (if:$tabulka is 2)[ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, kterou jste měl za úkol ověřit se ukázalo, že ono ověření opravdu bylo na místě, neboť se zdá, že v současné době platná teorie má jisté trhliny. (text-style:"italic")["Začínám se těšit, až ten článek publikuju. Tohle bude výsledek, o kterém se bude ve vědeckých kruzích určitě mluvit."] Teď je čas přistoupit k poslední zadané výzkumné otázce. Znovu jste sedl k datovému souboru. <img src="data:image/png; 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"/> ] (if:$tabulka is 3)[ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, kterou jste měl za úkol ověřit se ukázalo, že ono ověření opravdu bylo na místě, neboť se zdá, že v současné době platná teorie má jisté trhliny. (text-style:"italic")["Začínám se těšit, až ten článek publikuju. Tohle bude výsledek, o kterém se bude ve vědeckých kruzích určitě mluvit."] Teď je čas přistoupit k poslední zadané výzkumné otázce. Znovu jste sedl k datovému souboru. <img src="data:image/png; 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"/> ] (if:$tabulka is 4)[ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, kterou jste měl za úkol ověřit se ukázalo, že ono ověření opravdu bylo na místě, neboť se zdá, že v současné době platná teorie má jisté trhliny. (text-style:"italic")["Začínám se těšit, až ten článek publikuju. Tohle bude výsledek, o kterém se bude ve vědeckých kruzích určitě mluvit."] Teď je čas přistoupit k poslední zadané výzkumné otázce. Znovu jste sedl k datovému souboru. <img src="data:image/png; 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"/> ] [[Pokračovat na další výzkumnou otázku->36 - Úvod hypotéza C]]

Třetí otázkou je, zda se liší muži a ženy v úrovni projevované agrese vůči vládě. Analýza neprokázala, že by se muži a ženy v tomto ohledu lišili. Kromě toho jste nicméně dodatečnými analýzami odhalil přítomnost řady outlierů v obou skupinách. (text-style:"italic")[„V tom množství outlierů se může potenciálně skrývat docela problém. No, můžu outliery z analýz vyřadit a zkusit to znovu... Počkat! Někde jsem tu měl tu knihu. Třeba zase něco vyčtu.“] [[Použít výsledky včetně outlierů->37 - OI Výsledky hypotéza c)]] [[Vyřadit outliery a zkusit analýzy znovu->38 - OE Výsledky hypotéza c)]] [[Nahlédnout do knihy//"Pokročilých statistických postupů"//->39 - Kapitola II]]

Četbou knihy jste si ověřil své domněnky a také načerpal novou inspiraci pro řešení nastalého problému. Teď je potřeba toto úsilí zužitkovat. Musíte se rozhodnout, jak tedy vyřešíte problém s chybějícími hodnotami. Vzhledem k tomu, že pro takzvané moderní postupy nemáte zařízení s dostatečnou výpočetní kapacitou, musíte vybrat ten z tradičních postupů, který je podle vás nejvhodnější. [[Použít Case delation->26 - Zničená data]] [[Použít Mean substitution->27 - MSub Výsledky hypotéza b)]] [[Použít Pairwaise delation->32 - PD Výsledky hypotéza b)]] [[Použít Indicator Adjustment->33 IA Výsledky hypotéza b)]]

Po použití Pairwise delation z této konkrétní analýzy vypadlo 25% výzkumného vzorku. Vzhledem k jeho rozsahu si analýza i v tomto rozsahu zachovává svou statistickou sílu. Odhalila silnou pozitivní korelaci mezi výší čistého příjmu a ochotou k veřejným vystoupením. Tento výsledek podporuje dosavadní mínění teorie, že bohatší lidé jsou ochotnější k veřejnému vystupování. Z dostupných dat však nelze zjistit nic o tom, jaký mechanismus tuto větší ochotu způsobuje. (text-style:"italic")[„To by chtělo navazující kvalitativní studii, která by se bohatých lidí ptala na důvody, proč jsou ochotní vystupovat na veřejnosti a snažila se odhalit nějaký mechanismus. Hm. Tohle je dobrá věta do diskuse. To si musím někam poznačit.“] [[Zapracovat výsledky do článku->34 PD Shrnutí výsledků B]] [[Vyzkoušet ještě jiný postup->31 - Řešení missingů]]

Po aplikování Indicator adjustment postupu a doplnění chybějících hodnot Čistého příjmu průměrnými hodnotami ze vzorku jste provedl korelační analýzu. Výsledky sice prokázaly statistickou významnost korelace čistého příjmu a hrubého skóru Dotazníku, nicméně tato korelace se ukázala být velmi nízká. Díky indikátoru víte, že průměrný plat byl doplněn do 25% případů. Momentálně se vám tedy podařilo vyvrátit mylný předpoklad teorie, otázkou ale zůstává, jak moc oněch 25% doplněných hodnot tento výsledek zkresluje. [[Zapracovat tyto výsledky do článku->35 IA Shrnutí výsledků B]] [[Vyzkoušet ještě jiný postup->31 - Řešení missingů]]

(set: $vysledkyB to 2) (if:$tabulka is 1) [ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, jste potvrdil dosavadně platné teoretické předpoklady (text-style:"italic")["Začínám se těšit, až ten článek publikuju. Tohle bude výsledek, o kterém se bude ve vědeckých kruzích určitě mluvit."] Teď je čas přistoupit k poslední zadané výzkumné otázce. Znovu jste sedl k datovému souboru. <img src="data:image/png; 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"/> ] (if:$tabulka is 2) [ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, jste potvrdil dosavadně platné teoretické předpoklady (text-style:"italic")["Začínám se těšit, až ten článek publikuju. Tohle bude výsledek, o kterém se bude ve vědeckých kruzích určitě mluvit."] Teď je čas přistoupit k poslední zadané výzkumné otázce. Znovu jste sedl k datovému souboru. <img src="data:image/png; 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"/> ] (if:$tabulka is 3) [ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, jste potvrdil dosavadně platné teoretické předpoklady (text-style:"italic")["Začínám se těšit, až ten článek publikuju. Tohle bude výsledek, o kterém se bude ve vědeckých kruzích určitě mluvit."] Teď je čas přistoupit k poslední zadané výzkumné otázce. Znovu jste sedl k datovému souboru. <img src="data:image/png; 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"/> ] (if:$tabulka is 4) [ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, jste potvrdil dosavadně platné teoretické předpoklady (text-style:"italic")["Začínám se těšit, až ten článek publikuju. Tohle bude výsledek, o kterém se bude ve vědeckých kruzích určitě mluvit."] Teď je čas přistoupit k poslední zadané výzkumné otázce. Znovu jste sedl k datovému souboru. <img src="data:image/png; 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"/> ] [[Pokračovat na další výzkumnou otázku->36 - Úvod hypotéza C]]

(set: $vysledkyB to 3) (if:$tabulka is 1) [ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, pokud připustíme korekci síly korelace, která je zkreslená 25% doplněných průměrných hodnot, tak by ta korelace mohla být dostatečné silná, aby to stálo za zmínku. V podstatě to teorii podporuje, i když je zde jistý stín pochybnosti. Přesto se ale začínáte se těšit, až to celé publikujete. Proto není nač čekat a zdržovat se. Lepší bude hned přistoupit k poslední zadané výzkumné otázce. <img src="data:image/png; 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"/> ] (if:$tabulka is 2) [ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, pokud připustíme korekci síly korelace, která je zkreslená 25% doplněných průměrných hodnot, tak by ta korelace mohla být dostatečné silná, aby to stálo za zmínku. V podstatě to teorii podporuje, i když je zde jistý stín pochybnosti. Přesto se ale začínáte se těšit, až to celé publikujete. Proto není nač čekat a zdržovat se. Lepší bude hned přistoupit k poslední zadané výzkumné otázce. <img src="data:image/png; 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"/> ] (if:$tabulka is 3) [ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, pokud připustíme korekci síly korelace, která je zkreslená 25% doplněných průměrných hodnot, tak by ta korelace mohla být dostatečné silná, aby to stálo za zmínku. V podstatě to teorii podporuje, i když je zde jistý stín pochybnosti. Přesto se ale začínáte se těšit, až to celé publikujete. Proto není nač čekat a zdržovat se. Lepší bude hned přistoupit k poslední zadané výzkumné otázce. <img src="data:image/png; 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"/> ] (if:$tabulka is 4) [ I druhou ze tří částí výzkumu máte úspěšně za sebou. Článek uspokojivě narůstá na objemu a zdá se, že přináší zajímavé poznatky. V případě druhé hypotézy, pokud připustíme korekci síly korelace, která je zkreslená 25% doplněných průměrných hodnot, tak by ta korelace mohla být dostatečné silná, aby to stálo za zmínku. V podstatě to teorii podporuje, i když je zde jistý stín pochybnosti. Přesto se ale začínáte se těšit, až to celé publikujete. Proto není nač čekat a zdržovat se. Lepší bude hned přistoupit k poslední zadané výzkumné otázce. <img src="data:image/png; 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"/> ] [[Pokračovat na další výzkumnou otázku->36 - Úvod hypotéza C]]

(text-style:"italic")[„No, výsledky jsou výsledky. Navíc, tak velký vzorek by měl být poměrně reprezentativní. Jsou holt více a méně agresivní muži a agresivnější a méně agresivní ženy. V průměru to bude asi jedno.“] Veden takovou úvahou se rozhodnete ušetřit čas na dalších analýzách a vynaložit ho k dokončení článku. [[Zapracovat výsledky do článku->40 - OI Výsledky C]] (if:(history: where its name contains "31 - Řešení outlierů")'s length >= 1)[ [[Vyzkoušet ještě jiný postup->43 - Řešení outlierů]] ]

(text-style:"italic")["Je pravděpodobné, že extrémní hodnoty jsou výsledkem nějakých chyb. Bude lepší je z dalších analýz vyřadit.“] Veden touto myšlenkou provedete analýzu znovu na vzorku očištěném od outlierů. Skutečně docházíte k jiným výsledkům. V tomto případě se ukázal jak věcně tak statisticky významný rozdíl. Napovídá, že muži jsou výrazně náchylnější k agresivitě vůči vládě Státu. To vlastně potvrzuje, co se tak nějak předpokládalo. [[Zapracovat výsledky do článku ->41 - OE Výsledky C]] (if:(history: where its name contains "43 - Řešení outlierů")'s length >= 1)[ [[Vyzkoušet ještě jiný postup->43 - Řešení outlierů]] ]

(text-style:"bold","shadow")[Pokročilé postupy řešení statistických problémů] (text-style:"italic")[Práce s outliery] (text-style:"underline")[Předmluva] Mluvím-li o outlierech, mám na mysli pozorované hodnoty měření, které se výrazně liší od ostatních hodnot zjištěných ve vzorku. Jestliže Vám, vážený čtenáři, tato definice připadá vágní pak vězte, že je tomu opravdu tak. Na to, co je to vlastně outlier, jak ho poznat a jak pak s takovým záznamem naložit existují desítky různých názorů. A to na každou ze zmíněných tří věcí. Následující stránky pokrývají část možných variant. Přidržíme se předpokladu, že outlier znamená nepřiměřeně vysokou, či nepřiměřeně nízkou hodnotu konkrétního parametru ve srovnání s ostatními hodnotami stejné proměnné napříč datovým souborem. Pro další uvažování je důležité určit, co takovou nepřiměřenou hodnotu parametru způsobilo. První, nejběžnější a nejlépe řešitelná varianta je, že tato hodna je způsobená prostou chybou v přepisu dat. K tomu může snadno docházet při manuálním přepisu fyzických dat to elektronické podoby. Řešením je samozřejmě kontrola původních dat a korekce takových chybových hodnot. Druhým typem chyby, která vede k přítomnosti outlierů v datech jsou chyby na straně respondenta, které mohou plynout z nepochopení zadání, špatné interpretaci odpověďové škály a tak podobně. V tomto případě nezbývá, než takové hodnoty vyloučit z analýzy, protože jejich zachování by vedlo ke zkreslení výsledků. Třetí zdroj outlierů lze shrnout jako netypické projevy. Zde již narážíme na všudypřítomný problém „sto lidí sto názorů.“ Netypické projevy samozřejmě zahrnují některé jednotlivce, kteří ve srovnání s ostatními jednoduše dosahují v konkrétní proměnné extrémních hodnot. Je však popisována řada definic outlierů, některé odvozené od konkrétních statistických postupů. Od toho se potom odvíjí jak způsoby identifikace takových případů tak způsoby nakládání s nimi. Samozřejmě, existuje také přístup, který nenakládá s outliery jako s problémem, který musí být nutně vyřešen, ale jako se zdrojem zajímavého druhu informací. Takový zdroj může v některých případech mnoho napovědět. Dalo by se říct, že studiem výjimek se nejvíce dovídáme o pravidlech. Tento způsob práce samozřejmě vyžaduje individuální přístup a studium různých příčin u každého jednotlivého respondenta klade vysoké nároky jak na čas, tak na úsilí. Je nicméně pravda, že i korekce chybových dat vyžaduje zvýšené úsilí a individuální přístup ke každé hodnotě, kterou lze považovat za outlier. Může se zdát, že v případě outliery zatížených dat máme k dispozici dvě možnosti. První možností je investovat mnoho času a úsilí k individuálnímu posouzení každé takové hodnoty, což pravděpodobně povede k jejich redukci, či k tomu, že se z těchto hodnot dozvíme něco, co z celkového obrazu není patrné. Druhá možnost znamená plošné řešení outlierových hodnot. Zde buď zahrneme takové hodnoty do analýz nebo je z nich vyloučíme. V prvním případě riskujeme značně zkreslené výsledky, zatímco v druhém případě riskujeme ztrátu důležitých informací a nereprezentativnost takových výsledků. Také riskujeme nařčení ze strany oponentů, že manipulujeme s daty záměrně takovým způsobem, abychom získali výsledky, které se nám, abych tak řekl, hodí do krámu. V každém případě jsou tyto možnosti značně omezující. Proto někteří výzkumníci aplikují třetí možnost. Tou třetí možností je publikování dvojích výsledků, jak včetně outlierů tak bez nich. Takový postup lze označit za sebeprotektivní, protože přesouvá část zodpovědnosti za interpretaci na čtenáře. Je to však způsob, jak se vyhnout náročné individuální analýze i problémům plynoucím z plošných řešení. To je jen plošné shrnutí toho, co lze říct o práci s outliery. V tomto ohledu důrazně doporučuji nahlédnout do článku autorů Hermana Aguinise, Ryana K. Gottfredsona a Harryho Joo, publikovaného v roce 2013, který shrnuje 14 definic otlierů, 39 způsobů jejich identifikace a 20 postupů nakládání s nimi. Některé jsou poměrně komplikované, takže se i já v tomto místě zbavím části interpretační zodpovědnosti. [[Zavřít knihu->43 - Řešení outlierů]] [[Seznam literatury-> 42 - Seznam literatury C]]

(set: $vysledkyC to 1) Analýza dat včetně outlierů tedy neprokázala žádný rozdíl mezi muži a ženami. To do značné míry zpochybňuje teoretické znalosti, které jsou do této chvíle považovány za platné a předpokládají, že muži projevují výrazně více agrese než ženy. [[Zapracovat tyto výsledky do článku->50 - Článek dokončen + Interakce]]

Kniha vás ani v tomto případě nezklamala. Díky jejím informacím se rozšířily vaše možnosti, jak z celou záležitostí naložit [[Použít výsledky včetně outlierů->37 - OI Výsledky hypotéza c)]] [[Vyřadit outliery a zkusti analýzy znovu->38 - OE Výsledky hypotéza c)]] [[Použít obojí výsledky->44 - Obojí výsledky C]] [[Investovat čas a energii do individuální ananlýzy->45 CS - Výsledky C]]

(set: $vysledkyC to 2) Varianta včetně outlierů neprokázala žádný rozdíl mezi muži a ženami, což zpochybňuje současně platnou teorii. Analýza očištěná od otlierů ukázala jak věcně tak statisticky významný rozdíl. Napovídá, že muži jsou výrazně náchylnější k agresivitě vůči vládě Státu. To vlastně součastnou teorii potvrzuje. (text-style:"italic")["Tyto výsledky jsou značně matoucí. Ale pokud bych se rozhodl publikovat pouze jednu nebo pouze druhou variantu, hrozí, že bych se dopustil zkreslení výsledků a bylo by to považováno za neprofesionální vědecký postup. Bude tedy lepší publikovat všechny informace včetně informací o množství a povaze outlierů, ať si s tím vědecká obec poradí po svém."] [[Zapracovat výsledky do článku->50 - Článek dokončen + Interakce]] [[Vyzkoušet ještě jiný postup->43 - Řešení outlierů]]

(set: $vysledkyC to 4) (text-style:"Italic")[„Asi bude nejlepší investovat ještě nějaký čas a úsilí do podrobnější analýzy. Ty výsledky zatím nejsou vlastně nijak vypovídající.“] Veden touto myšlenkou se pouštíte do detailního rozboru všech outierů. Od Institutu jste si vyžádal původní data včetně odpovědí na jednotlivé položky všech metod a upravoval, třídil a interpretoval jste jako divý. Úsilí se ovšem vyplatilo. Nápravou několika chybně zadaných hodnot a vyřazením několika lidí, jejichž odpovědi byly dostatečně podivné na to, aby se dalo usuzovat na nepochopení, jste docílil toho, že následná analýza prokázala rozdíl mezi muži a ženami, jehož věcná významnost není zanedbatelná. Navíc se zdá, že prostředí, v jakém se lidé běžně nachází a jejich politická aktivita či neaktivita může radikalizovat či umírňovat agresivní projevy. A to jak u žen tak u mužů. Tyto analýzy si však vyžádaly takové množství času, že už nezbyl na to, abyste se tím dál zabýval, je nutné dokončit článek. [[Zapracovat výsledky do článku->51 - Článek dokončen bez interakce]]

Zapracování těchto výsledků do článku byl článek dokončen! (if:(history: where its name contains "9 - Postupovat rozvážně")'s length >= 1)[ (if:(history: where its name contains "51 - Interakce")'s length is not 1)[ (text-style:"italic")["Vzhledem k rozsahu dat a tomu, že do termínu odevzdání výsledků zbývá ještě nějaký čas, bych se mohl chvíli věnovat ještě nějakým dalším analýzám. Například vlivu interakce politické aktivity a věku na míru projevované agrese. Nebo ten čas věnuji kvalitní přípravě textu článku, i když v menším rozsahu."] [[Zabývat se otázkou intrakce->51 - Interakce]] ] ] [[Publikovat!->00 Výsledky]]

(set: $interakce to 1) Váš zájem o to, jak interakce politické aktivity a věku ovlivňuje míru projevované agresivity proti vládě Státu vyústil v závěr, že politicky aktivní lidé jsou agresivnější než ti politicky neaktivní. Nejvýraznější je to pak u věkové skupiny od dvacet tří let [[Publikovat s výsledky interakce->00 Výsledky]] [[Publikovat bez výsledků interakce->52 - Článek dokončen interakce zatajena]]

(set: $vysledkyC to 2) Analýza dat očištěná od outlierů tedy prokázala, že muži jsou skutečně více náchylní k projevování agrese vůči Státu. Tento výsledek tedy podporuje dosavadně platnou teorii. Nicméně rozdíl nebyl příliš velký, čili věcná významnost tohoto zjištění trochu pokulhává. [[Zapracovat tyto výsledky do článku->50 - Článek dokončen + Interakce]]

Zapracování těchto výsledků do článku byl článek dokončen! (if:(history: where its name contains "10 - AD kreativita")'s length >= 1)[ Rád byste se ještě zabýval doplňkovým výzkumem, abyste maximalizoval užitek z obdržených dat, nicméně podrobná analýza outlierů si vyžádala příliš mnoho času. Už je na čase článek publikovat. ] [[Publikovat!->00 Výsledky]]

(if:$vysledkyA is 1)[Potvrzení teoretických předpokladů ohledně věkových skupin a jejich schopnosti rozlišovat spolehlivost mediálních zpráv zapadlo ve víru jiných, zajímavějších zjištění. Tímto počinem jste své kariéře příliš neprospěl. (if:(history: where its name contains "23 - MS Výsledky A+")'s length is 1)[Možná, kdybyste byl schopen rozlišit ty dva shluky, které jste viděl na scatterplotu, bylo by to celé trochu jinak.] ] (if:$vysledkyA is 2)[Korekce teoretických předpokladů ohledně věkových skupin a jejich schopnosti rozlišovat spolehlivost mediálních zpráv vyvolala ve vědeckých kruzích rozruch. Řada vědců se touto záležitostí dále zabývá a vaše vědecká popularita roste.] (if:$vysledkyB is 1)[Nepříliš průkazné výsledky, které jste poskytl ohledně korelace výše příjmů a ochoty k veřejnému vystupování vaší kariéře příliš neprospěly. Sklidil jste v tomto ohledu spíše kritické ohlasy.] (if:$vysledkyB is 2)[Zájem vyvolalo i potvrzení toho, že výše příjmů koreluje s ochotou lidí k veřejnému vystupování. Různé týmy se pustily do vámi navrhované kvalitativní analýzy mechanismu, který se zatím skrývá. Vaše vědecké renomé dále narůstá.] (if:$vysledkyB is 3)[Nepříliš průkazné výsledky, které jste poskytl ohledně korelace výše příjmů a ochoty k veřejnému vystupování vaší kariéře příliš neprospěly. Sklidil jste v tomto ohledu spíše kritické ohlasy.] (if:$vysledkyC is 1)[Neprokázání rozdílů v agresivitě mezi muži a ženami zapadlo mezi ostatními, významnějšími výsledky z poslední doby. Touto částí svého článku jste ke znovuvzkříšení své vědecké pověsti nijak nepřispěl.] (if:$vysledkyC is 2)[Důkaz, že se muži a ženy liší v úrovni projevované agresivity vůči Vládě vyvolal značný rozruch ve společnosti . Mluví se o tom jak ve vědeckých kruzích, tak ve vládních úřadech. Oba tábory zvažují, jaký bude v tomto ohledu další postup. Vaše jméno je samozřejmě v této souvislosti skloňováno velmi často. Nicméně, objevují se i takové hlasy, které tyto výsledky zpochybňují. Argumentují tím, že vyřazením outlierů z analýz jste docílil nadhodnocení významnosti celého závěru.] (if:$vysledkyC is 3)[Vaše výsledky ohledně míry agresivity vůči vládě Státu u mužů a žen vyvolaly ve vědeckých kruzích bouřlivou debatu. Vědci intenzivně porovnávají oba závěry a intenzivně se mezi sebou dohadují, který z nich je věrohodnější a co z toho celého vlastně plyne. Tento rozruch značně přispívá vašemu vědeckému renomé. ] (if:$vysledkyC is 4)[Důkaz, že se muži a ženy liší v úrovni projevované agresivity vůči Vládě vyvolal ve společnosti značný rozruch. Mluví se o tom jak ve vědeckých kruzích, tak ve vládních úřadech. Oba tábory zvažují, jaký bude v tomto ohledu další postup. Vaše jméno je samozřejmě v této souvislosti skloňováno velmi často. Vědečtí pracovníci také velmi oceňují množství práce, které jste prokázal individuální analýzou outlierů.] (if:$interakce is 2)[Zkoumání interakce bylo z vědeckého hlediska správné rozhodnutí. Někdy je ale potřeba zvážit také důsledky, jaké budou mít některá zjištění na veřejnost. Někdy je lepší některé věci raději neuveřejňovat.] (if:$interakce is 1)[Vašich zjištění vyplývajících ze studia interakce politické aktivity a věku se chytla vláda. Její způsob řešení této situace ale spočíval v omezování možností politických aktivit a policejní tlak na politicky aktivní jedince v kritickém věku od 23 let. Tato skupina je nyní výrazně omezována, aby neohrožovala pozici a vliv Vlády. Vy jste se stal mezi lidmi velmi slavným. Vešlo v obecnou známost, že výzkum, který byl následně vládnou zneužit a vedl k útlaku obyvatelstva, jste provedl vy. Z vědeckého hlediska bylo zkoumání této interakce samozřejmě správným rozhodnutím. Nicméně někdy vedou k důsledkům, které jsme nemohli předpokládat. Nyní se musíte vypořádat s etickými aspekty těchto důsledků.]

(set: $interakce to 2) Zapracování těchto výsledků do článku byl článek dokončen! Po zvážení možných důsledků takového zjištění jste se rozhodl raději se držet pouze předepsaného rozsahu a tyto výsledky navíc nepublikovat. [[Publikovat!->00 Výsledky]]

Stay a while, and listen, dostává se k Vám velmi netypický výukový materiál. V době nejtvrdšího lockdownu někdy na jaře 2020, když se učilo jenom online jsem se mi hrozně nechtělo přednášet prostřednictvím počítače, a to z mnoha různých důvodů. Náhodou to bylo právě v době, kdy jsem měl na nočním stolku položený gamebook "Alenčina noční můra v Říši divů" a marně jsem se ho snažil projít. A tak se zrodil nápad na to, vytvořit výukový materiál ve formátu gamebooku, i když nebude mít fyzickou podobu knihy. A protože to bylo uprostřed lockdownu, tak během pár dní vznikl první můj gamebook zaměřený na výuku statistiky. A u studentů slavil úspěch. To nás společně s ''Tomášem Proškem'' inspirovalo k tomu, že jsme se rozhodli vytvořit další gamebooky na další statistická témata. Naše snaha byla podpořena nejen Stipendijním programem na podporu využití technologií ve výuce", ale také laskavou pomocí zaměstnanců Kancelář e-learningu CIT FF MUNI. Tak vznikly tři statistické gamebooky: //Základní statistické pojmy//; //Práce s daty, missingy, outliery// a //Závěrečné shrnutí// pro účely výuky předmětu //Rozšířený úvod do inferenční statistiky// na Psychologickém ústavu Filosofické fakulty. A ten druhý právě čtete. Jednotlivé gamebooky mezi sebou mají droubou návaznost, a tak doporučuji projít nejdříve //Základní statistické pojmy//, pokud jste tak ještě neudělali. Ještě je asi dobré doplnit, že gamebooky nejsou samonosné a jsou pouze doplňkovým materiálům k přednáškám //Rozšířeného úvodu do inferenční statistiky// a snaha projít je bez informací z tohoto předmětu může být krajně frustrující. Pokud byste nevěděli, co to takový gamebook je, tak vězte, že se jedná o velmi stařičkého předchůdce videoher ve formátu knihy. Je to0 příběh, který se však různě větví na základě rozhodnutí čtenáře. Na konci každé sekce popisující nějaké události jste postaveni před volbu z několika možností, a tato volba určí pokračování příběhu Proto zde na každé straně je několik možností, jak se může hlavní postava příběhu, vědec Abrahám, zachovat. Každé rozhodnutí Vás pošle na jinou cestu. Všechny však vedou k nějakému cíli. Někdy správnému a někdy velice nehezkému cíli. Kterou cestou se vydáte, záleží na Vás. Vy budete mít možnost zvolit si, jak se Abrahám zachová. Danou možnost zvolíte kliknutím na příslušnou reakci na konci textu, a to vás přesune na odpovídající místo. Snad jen dodám, že to, co na vás na které cestě čeká nebudete vědět, dokud se jí nevydáte. A na různých místech najdete různé nápovědy. Dobře si jich všímejte. Někdy záleží na drobných rozdílech. Přeju hodně úspěchů a taky zábavy a snad i nějaký ten studijní pokrok. Lukáš Čunek [[Začít!->1 - Postava]]