Erweiterung: Signifikanz-Visualisierung ergänzt und Export sowie Themometer aktualisiert
This commit is contained in:
@ -67,3 +67,13 @@ Thermometer_ID,Stichwort,Effektstärke
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7.08,Dauer der Sommerferien,-0.09
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7.08,Dauer der Sommerferien,-0.09
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7.09,Sommerschulen,0.17
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7.09,Sommerschulen,0.17
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7.10,Schulkalender / Stundenplan,0.10
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7.10,Schulkalender / Stundenplan,0.10
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7.11,Desegration,0.23
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7.12,Ethnische Vielfalt,0.09
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7.13,Wohnheimunterbringung,0.05
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7.14,Schulgröße,0.33
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7.15,Neuordnung des Schulbezirks,0.05
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7.16,Schulwahlfreiheit,0.27
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7.17,Fördermaßnahmen im Sekundarbereich I,0.18
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7.18,Schulleitung,0.37
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7.19,Schulklima,0.53
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7.20,Schuleffekte,0.48
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@ -30,10 +30,40 @@
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"data": [
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"data": [
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{
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{
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"Cluster": 0,
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"Cluster": 0,
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"n": 21,
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"n": 37,
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"Ø d": 0.654,
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"Ø d": 0.128,
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"Kapitelverteilung": {
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"Kapitelverteilung": {
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"5": 21
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"5": 12,
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"6": 9,
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"7": 16
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},
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"Top_Terme": [
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"aktivierend",
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"und",
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"positiv"
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]
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},
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{
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"Cluster": 1,
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"n": 7,
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"Ø d": 0.921,
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"Kapitelverteilung": {
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"5": 7
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},
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"Top_Terme": [
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"erkenntnisstufen",
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"feldunabhängigkeit",
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"neugierde"
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]
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},
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{
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"Cluster": 2,
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"n": 24,
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"Ø d": 0.483,
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"Kapitelverteilung": {
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"5": 14,
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"6": 6,
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"7": 4
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},
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},
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"Top_Terme": [
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"Top_Terme": [
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"und",
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"und",
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@ -41,43 +71,18 @@
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"beharrlichkeit"
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"beharrlichkeit"
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]
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]
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},
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},
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{
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"Cluster": 1,
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"n": 20,
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"Ø d": 0.128,
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"Kapitelverteilung": {
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"6": 20
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},
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"Top_Terme": [
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"geschieden",
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"kinderheime",
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"stipendien"
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]
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},
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{
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"Cluster": 2,
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"n": 17,
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"Ø d": -0.049,
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"Kapitelverteilung": {
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"5": 17
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},
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"Top_Terme": [
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"aktivierend",
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"negativ",
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"kognitive"
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]
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},
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{
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{
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"Cluster": 3,
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"Cluster": 3,
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"n": 10,
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"n": 10,
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"Ø d": 0.127,
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"Ø d": -0.346,
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"Kapitelverteilung": {
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"Kapitelverteilung": {
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"7": 10
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"5": 5,
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"6": 5
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},
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},
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"Top_Terme": [
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"Top_Terme": [
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"konfessionsschulen",
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"negativ",
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"sommerschulen",
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"aktivierend",
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"monoedukation"
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"schulwechsel"
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]
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]
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}
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}
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]
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]
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@ -1,69 +1,79 @@
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Thermometer_ID,Stichwort,Effektstärke,Kapitel,Kapitelname,Bin,Silhouette_point,Outlier_IQR,Text_Dimension,Cluster
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Thermometer_ID,Stichwort,Effektstärke,Kapitel,Kapitelname,Bin,Silhouette_point,Outlier_IQR,Text_Dimension,Cluster
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5.01,Vorausgehende Fähigkeiten & Intelligenz,0.96,5,Lernende,hoch,0.6502913752913749,False,-0.08735409337133096,0
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5.01,Vorausgehende Fähigkeiten & Intelligenz,0.96,5,Lernende,hoch,0.6823734729493874,False,-0.08096089721466541,1
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5.02,Vorausgehendes Leistungsniveau,0.73,5,Lernende,hoch,0.7260754716981136,False,-0.07336449853523334,0
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5.02,Vorausgehendes Leistungsniveau,0.73,5,Lernende,hoch,0.09764309764310186,False,-0.06690384334788037,1
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5.03,Beziehung zwischen Schul- und Berufsleistungen,0.37,5,Lernende,gering,0.2835203366058893,False,-0.17512188415193075,0
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5.03,Beziehung zwischen Schul- und Berufsleistungen,0.37,5,Lernende,gering,0.4793284161490682,False,-0.16752469249087093,2
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5.04,Beziehung zwischen Schul- und Universitätsleistungen,0.55,5,Lernende,mittel,0.680520117762513,False,-0.175121884151931,0
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5.04,Beziehung zwischen Schul- und Universitätsleistungen,0.55,5,Lernende,mittel,0.7120401337792641,False,-0.16752469249087146,2
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5.05,Erkenntnisstufen,1.28,5,Lernende,hoch,0.5057964601769913,True,-0.05914975460796303,0
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5.05,Erkenntnisstufen,1.28,5,Lernende,hoch,0.4754440961337511,True,-0.054732267395592116,1
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5.06,Exekutive Funktionen,0.62,5,Lernende,mittel,0.7281195079086115,False,-0.05914975460796313,0
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5.06,Exekutive Funktionen,0.62,5,Lernende,mittel,0.5038120750051506,False,-0.054732267395592345,2
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5.07,Stärke des Arbeitsgedächtnisses,0.63,5,Lernende,mittel,0.7313852813852812,False,-0.07892435655637323,0
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5.07,Stärke des Arbeitsgedächtnisses,0.63,5,Lernende,mittel,0.4599317988064778,False,-0.07588868190734946,2
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5.08,Vorschulische nicht-kognitive Fähigkeiten,0.2,5,Lernende,gering,0.4001572327044027,False,-0.09398809922530565,2
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5.08,Vorschulische nicht-kognitive Fähigkeiten,0.2,5,Lernende,gering,0.6627335299901671,False,-0.08422827613027246,0
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5.09,Gekreuzte Lateralität,-0.03,5,Lernende,negativ,0.6501826722338218,False,-0.07336449853523311,2
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5.09,Gekreuzte Lateralität,-0.03,5,Lernende,negativ,0.4743319268635724,False,-0.06690384334787969,0
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5.1,Feldunabhängigkeit,0.94,5,Lernende,hoch,0.6583828775267535,False,-0.05914975460796317,0
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5.1,Feldunabhängigkeit,0.94,5,Lernende,hoch,0.6757741347905253,False,-0.05473226739559237,1
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5.11,Beurteilung der eigenen Leistungsfähigkeit,0.96,5,Lernende,hoch,0.6502913752913749,False,-0.08382750975171666,0
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5.11,Beurteilung der eigenen Leistungsfähigkeit,0.96,5,Lernende,hoch,0.6823734729493874,False,-0.07570327260842737,1
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5.12,Kreativität und Lernleistung in Beziehung setzen,0.4,5,Lernende,mittel,0.3880890052355992,False,-0.14757153731358338,0
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5.12,Kreativität und Lernleistung in Beziehung setzen,0.4,5,Lernende,mittel,0.6006217348128319,False,-0.1591048977238036,2
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5.13,Kritisches Denken,0.84,5,Lernende,hoch,0.6930555555555555,False,-0.07336449853523312,0
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5.13,Kritisches Denken,0.84,5,Lernende,hoch,0.5384615384615383,False,-0.06690384334787972,1
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5.14,Beharrlichkeit und Zuversicht (Mindset),0.19,5,Lernende,gering,0.42788461538461536,False,-0.33543250302056493,2
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5.14,Beharrlichkeit und Zuversicht (Mindset),0.19,5,Lernende,gering,0.6866096866096867,False,-0.3314224947892216,0
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5.15,Beharrlichkeit und Zuversicht (Achtsamkeit),0.26,5,Lernende,gering,0.16621376811594119,False,-0.33543250302056515,2
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5.15,Beharrlichkeit und Zuversicht (Achtsamkeit),0.26,5,Lernende,gering,0.38451935081148536,False,-0.385175633205326,0
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5.16,Beharrlichkeit und Zuversicht (Durchhaltevermögen),0.35,5,Lernende,gering,0.200073637702503,False,-0.33543250302056504,0
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5.16,Beharrlichkeit und Zuversicht (Durchhaltevermögen),0.35,5,Lernende,gering,0.36200148101131857,False,-0.3314224947892214,2
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5.17,Beharrlichkeit und Zuversicht (Konzentration / Ausdauer und Engagement),0.41,5,Lernende,mittel,0.4177336747759273,False,-0.3218185254749976,0
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5.17,Beharrlichkeit und Zuversicht (Konzentration / Ausdauer und Engagement),0.41,5,Lernende,mittel,0.6271031151091123,False,-0.31723823624074776,2
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5.18,Beharrlichkeit und Zuversicht (Selbstwirksamkeitserwartung),0.64,5,Lernende,mittel,0.7331058020477826,False,-0.3354325030205649,0
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5.18,Beharrlichkeit und Zuversicht (Selbstwirksamkeitserwartung),0.64,5,Lernende,mittel,0.40984330169940336,False,-0.33142249478922153,2
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5.19,Beharrlichkeit und Zuversicht (Positives Selbstbild),0.51,5,Lernende,mittel,0.6344374342797038,False,-0.3021008406853567,0
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5.19,Beharrlichkeit und Zuversicht (Positives Selbstbild),0.51,5,Lernende,mittel,0.754258655679233,False,-0.29756562936567754,2
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5.2,Beharrlichkeit und Zuversicht (Selbstkontrolle),0.66,5,Lernende,mittel,0.7335820895522391,False,-0.3354325030205649,0
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5.2,Beharrlichkeit und Zuversicht (Selbstkontrolle),0.66,5,Lernende,mittel,0.2915181753385596,False,-0.3314224947892215,2
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5.21,Schülerpersönlichkeit,0.18,5,Lernende,gering,0.45180722891566244,False,-0.07336449853523312,2
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5.21,Schülerpersönlichkeit,0.18,5,Lernende,gering,0.7052341597796145,False,-0.06690384334787969,0
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5.22,Perfektionismus,-0.03,5,Lernende,negativ,0.6501826722338218,False,-0.07336449853523314,2
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5.22,Perfektionismus,-0.03,5,Lernende,negativ,0.4743319268635724,False,-0.06690384334787969,0
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5.23,Emotionen,0.61,5,Lernende,mittel,0.7232381801962534,False,-0.1013942427833901,0
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5.23,Emotionen,0.61,5,Lernende,mittel,0.5420821699242117,False,-0.09469066931400565,2
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5.24,Emotionen (Emotionale Intelligenz),0.5,5,Lernende,mittel,0.6195931477516049,False,-0.1010290138898771,0
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5.24,Emotionen (Emotionale Intelligenz),0.5,5,Lernende,mittel,0.7523286287139656,False,-0.09749403000680268,2
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5.25,Emotionen (Wohlbefinden),0.08,5,Lernende,gering,0.5842661691542289,False,-0.09745012386374446,2
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5.25,Emotionen (Wohlbefinden),0.08,5,Lernende,gering,0.7632850241545894,False,-0.09014704327625096,0
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5.26,Positiv-aktivierend (Freude),0.5,5,Lernende,mittel,0.6195931477516049,False,0.7827951695376463,0
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5.26,Positiv-aktivierend (Freude),0.5,5,Lernende,mittel,0.7523286287139656,False,0.7596274860802354,2
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5.27,Positiv-aktivierend (Hoffnung),0.2,5,Lernende,gering,0.4001572327044027,False,0.7827951695376463,2
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5.27,Positiv-aktivierend (Hoffnung),0.2,5,Lernende,gering,0.6627335299901671,False,0.7596274860802354,0
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5.28,Positiv-aktivierend (Neugierde),0.74,5,Lernende,hoch,0.7238450074515644,False,0.5855547077500068,0
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5.28,Positiv-aktivierend (Neugierde),0.74,5,Lernende,hoch,0.1650485436893238,False,0.5687106778355705,1
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5.29,Positiv-aktivierend (Glücklichsein),0.54,5,Lernende,mittel,0.6708582834331347,False,0.7827951695376463,0
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5.29,Positiv-aktivierend (Glücklichsein),0.54,5,Lernende,mittel,0.7309884383650871,False,0.7596274860802354,2
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5.3,Positiv-aktivierend (Entspannung),0.16,5,Lernende,gering,0.48663294797687695,False,0.7827951695376463,2
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5.3,Positiv-aktivierend (Entspannung),0.16,5,Lernende,gering,0.7269595176571921,False,0.7596274860802354,0
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5.31,negativ-aktivierend (Angst),-0.4,5,Lernende,negativ,0.608739837398373,False,0.4425504899470148,2
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5.31,negativ-aktivierend (Angst),-0.4,5,Lernende,negativ,0.7516072139728055,False,0.4899153315315725,3
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5.32,negativ-aktivierend (Depressionen),-0.3,5,Lernende,negativ,0.6266841317365268,False,0.4425504899470148,2
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5.32,negativ-aktivierend (Depressionen),-0.3,5,Lernende,negativ,0.6883554432512108,False,0.4899153315315726,3
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5.33,negativ-aktivierend (Wut),-0.65,5,Lernende,negativ,0.510747535596933,True,0.44255048994701485,2
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5.33,negativ-aktivierend (Wut),-0.65,5,Lernende,negativ,0.5657478187012586,True,0.48991533153157263,3
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5.34,negativ-aktivierend (Frustration),-0.04,5,Lernende,negativ,0.6507201646090555,False,0.6056986180050421,2
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5.34,negativ-aktivierend (Frustration),-0.04,5,Lernende,negativ,0.4271968046477851,False,0.665618594201167,0
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5.35,negativ-aktivierend (Aggression und Gewalt),0.03,5,Lernende,gering,0.6225686498855841,False,0.30890529874855743,2
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5.35,negativ-aktivierend (Aggression und Gewalt),0.03,5,Lernende,gering,0.6867612293144207,False,0.3512922425196881,0
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5.36,negativ-aktivierend (Langeweile),-0.46,5,Lernende,negativ,0.5871794871794872,False,0.4425504899470148,2
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5.36,negativ-aktivierend (Langeweile),-0.46,5,Lernende,negativ,0.7126947637292466,False,0.665618594201167,3
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5.37,kognitive Dispositionen (Morgentypus vs. Abendtypus),0.18,5,Lernende,gering,0.45180722891566244,False,-0.09227886985139404,2
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5.37,kognitive Dispositionen (Morgentypus vs. Abendtypus),0.18,5,Lernende,gering,0.7052341597796145,False,-0.08265613474581207,0
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5.38,kognitive Dispositionen (Prokrastination),-0.41,5,Lernende,negativ,0.6059563758389256,False,-0.08917102326885956,2
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5.38,kognitive Dispositionen (Prokrastination),-0.41,5,Lernende,negativ,0.7479620323841432,False,-0.08006329810038103,3
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6.01,Sozioökonomischer Status,0.56,6,Elternhaus und Familie,mittel,0.6827943498774371,False,-0.07336449853523314,1
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6.01,Sozioökonomischer Status,0.56,6,Elternhaus und Familie,mittel,0.6896373947413648,False,-0.06690384334787969,2
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6.02,Bezug staatlicher Transferleistungen,-0.12,6,Elternhaus und Familie,negativ,0.772215227319828,False,-0.07336449853523314,1
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6.02,Bezug staatlicher Transferleistungen,-0.12,6,Elternhaus und Familie,negativ,-0.013035159052858219,False,-0.06690384334787969,3
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6.03,Erwerbstätigkeit der Mutter,0.05,6,Elternhaus und Familie,gering,0.8306537734304565,False,-0.08585665050056006,1
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6.03,Erwerbstätigkeit der Mutter,0.05,6,Elternhaus und Familie,gering,0.7362514029180696,False,-0.07741252027576469,0
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6.04,Stipendien,0.03,6,Elternhaus und Familie,gering,0.82487518707422,False,-0.07336449853523311,1
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6.04,Stipendien,0.03,6,Elternhaus und Familie,gering,0.6867612293144207,False,-0.06690384334787969,0
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6.05,Einwanderungsstatus,0.05,6,Elternhaus und Familie,gering,0.8306537734304565,False,-0.07336449853523312,1
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6.05,Einwanderungsstatus,0.05,6,Elternhaus und Familie,gering,0.7362514029180696,False,-0.06690384334787969,0
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6.06,Familienstruktur,0.14,6,Elternhaus und Familie,gering,0.8378335891817927,False,-0.059149754607963165,1
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6.06,Familienstruktur,0.14,6,Elternhaus und Familie,gering,0.7412814274128143,False,-0.05473226739559239,0
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6.07,Geschieden,-0.26,6,Elternhaus und Familie,negativ,0.7061030196788244,False,-0.08431062057732165,1
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6.07,Geschieden,-0.26,6,Elternhaus und Familie,negativ,0.6103755323267518,False,-0.07597551679303022,3
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6.08,nicht-geschieden vs. wiederverheiratet,0.24,6,Elternhaus und Familie,gering,0.8324161165454884,False,-0.09341002740120666,1
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6.08,nicht-geschieden vs. wiederverheiratet,0.24,6,Elternhaus und Familie,gering,0.506300114547537,False,-0.08358940952566345,0
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6.09,Adoption,0.21,6,Elternhaus und Familie,gering,0.8381014976799916,False,-0.0733644985352332,1
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6.09,Adoption,0.21,6,Elternhaus und Familie,gering,0.6330275229357801,False,-0.06690384334787898,0
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6.1,Kinderheime,0.33,6,Elternhaus und Familie,gering,0.8071221370551951,False,-0.07336449853523312,1
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6.1,Kinderheime,0.33,6,Elternhaus und Familie,gering,0.2128574750058127,False,-0.05473226739559239,2
|
||||||
6.11,Häusliches Anregungsniveau,0.4,6,Elternhaus und Familie,mittel,0.7824192735239991,False,-0.07336449853523314,1
|
6.11,Häusliches Anregungsniveau,0.4,6,Elternhaus und Familie,mittel,0.6006217348128319,False,-0.06690384334787969,2
|
||||||
6.12,Elternunterstützung beim Lernen,0.41,6,Elternhaus und Familie,mittel,0.77761147335638,False,-0.07336449853523314,1
|
6.12,Elternunterstützung beim Lernen,0.41,6,Elternhaus und Familie,mittel,0.6271031151091123,False,-0.06690384334787967,2
|
||||||
6.13,Elterliche Autonomieunterstützung (Familienhilfe),0.06,6,Elternhaus und Familie,gering,0.8320762909326382,False,-0.07336449853523312,1
|
6.13,Elterliche Autonomieunterstützung (Familienhilfe),0.06,6,Elternhaus und Familie,gering,0.7516420361247947,False,-0.06690384334787967,0
|
||||||
6.14,Elternerwartungen,0.49,6,Elternhaus und Familie,mittel,0.7301343867848403,False,-0.07336449853523314,1
|
6.14,Elternerwartungen,0.49,6,Elternhaus und Familie,mittel,0.7454899415963664,False,-0.06690384334787967,2
|
||||||
6.15,Körperliche Züchtigung,-0.33,6,Elternhaus und Familie,negativ,0.668243380062615,False,-0.07336449853523312,1
|
6.15,Körperliche Züchtigung,-0.33,6,Elternhaus und Familie,negativ,0.7233372687918145,False,-0.05473226739559239,3
|
||||||
6.16,Väter,0.21,6,Elternhaus und Familie,gering,0.8381014976799916,False,-0.07336449853523312,1
|
6.16,Väter,0.21,6,Elternhaus und Familie,gering,0.6330275229357801,False,-0.06690384334787969,0
|
||||||
6.17,Schulwechsel,-0.38,6,Elternhaus und Familie,negativ,0.6384645645869302,False,-0.07336449853523312,1
|
6.17,Schulwechsel,-0.38,6,Elternhaus und Familie,negativ,0.7505765478091184,False,-0.06690384334787967,3
|
||||||
6.18,Fernsehen,-0.15,6,Elternhaus und Familie,negativ,0.7598477443369885,False,-0.059149754607963165,1
|
6.18,Fernsehen,-0.15,6,Elternhaus und Familie,negativ,0.19217466493731084,False,-0.05473226739559239,3
|
||||||
6.19,Programme für Eltern,0.39,6,Elternhaus und Familie,gering,0.7865197915104075,False,-0.07336449853523314,1
|
6.19,Programme für Eltern,0.39,6,Elternhaus und Familie,gering,0.5654863290004483,False,-0.06690384334787967,2
|
||||||
6.2,Hausbesuche durch Lehrpersonen,0.22,6,Elternhaus und Familie,gering,0.8366902739695423,False,-0.07336449853523314,1
|
6.2,Hausbesuche durch Lehrpersonen,0.22,6,Elternhaus und Familie,gering,0.5968253968253969,False,-0.06690384334787967,0
|
||||||
7.01,Finanzielle Ausstattung,0.19,7,Schule und Gesellschaft,gering,0.9251410228772312,False,-0.07336449853523312,3
|
7.01,Finanzielle Ausstattung,0.19,7,Schule und Gesellschaft,gering,0.6866096866096867,False,-0.06690384334787969,0
|
||||||
7.02,Accountability / Rechenschaftspflicht,0.27,7,Schule und Gesellschaft,gering,0.8900556973256574,False,-0.0733644985352329,3
|
7.02,Accountability / Rechenschaftspflicht,0.27,7,Schule und Gesellschaft,gering,0.3124183006535946,False,-0.06690384334787969,0
|
||||||
7.03,Leistungsbezogene Bezahlung,0.05,7,Schule und Gesellschaft,gering,0.9174717855887603,False,-0.07336449853523314,3
|
7.03,Leistungsbezogene Bezahlung,0.05,7,Schule und Gesellschaft,gering,0.7362514029180696,False,-0.06690384334787969,0
|
||||||
7.04,Qualität des Schulgebäudes,0.24,7,Schule und Gesellschaft,gering,0.9083468761774898,False,-0.07892435655637318,3
|
7.04,Qualität des Schulgebäudes,0.24,7,Schule und Gesellschaft,gering,0.506300114547537,False,-0.07588868190734947,0
|
||||||
7.05,Vertragsschulen / Charter-Schulen,0.04,7,Schule und Gesellschaft,gering,0.9128781474571108,False,-0.07336449853523315,3
|
7.05,Vertragsschulen / Charter-Schulen,0.04,7,Schule und Gesellschaft,gering,0.71286701208981,False,-0.06690384334787967,0
|
||||||
7.06,Konfessionsschulen,0.23,7,Schule und Gesellschaft,gering,0.9129250266162217,False,-0.07336449853523314,3
|
7.06,Konfessionsschulen,0.23,7,Schule und Gesellschaft,gering,0.5555555555555554,False,-0.05473226739559239,0
|
||||||
7.07,Monoedukation,0.07,7,Schule und Gesellschaft,gering,0.9235929535014257,False,-0.07336449853523312,3
|
7.07,Monoedukation,0.07,7,Schule und Gesellschaft,gering,0.762962962962963,False,-0.06690384334787967,0
|
||||||
7.08,Dauer der Sommerferien,-0.09,7,Schule und Gesellschaft,negativ,0.8327092370446383,False,-0.08477035593782843,3
|
7.08,Dauer der Sommerferien,-0.09,7,Schule und Gesellschaft,negativ,0.12543402777777787,False,-0.07649636029329497,0
|
||||||
7.09,Sommerschulen,0.17,7,Schule und Gesellschaft,gering,0.9281937118300768,False,-0.07336449853523315,3
|
7.09,Sommerschulen,0.17,7,Schule und Gesellschaft,gering,0.7173333333333332,False,-0.06690384334787969,0
|
||||||
7.1,Schulkalender / Stundenplan,0.1,7,Schule und Gesellschaft,gering,0.9281794453692277,False,-0.07336449853523314,3
|
7.1,Schulkalender / Stundenplan,0.1,7,Schule und Gesellschaft,gering,0.7596223674655048,False,-0.06690384334787969,0
|
||||||
|
7.11,Desegration,0.23,7,Schule und Gesellschaft,gering,0.5555555555555554,False,-0.06690384334787969,0
|
||||||
|
7.12,Ethnische Vielfalt,0.09,7,Schule und Gesellschaft,gering,0.762208067940552,False,-0.06690384334787969,0
|
||||||
|
7.13,Wohnheimunterbringung,0.05,7,Schule und Gesellschaft,gering,0.7362514029180696,False,-0.06690384334787969,0
|
||||||
|
7.14,Schulgröße,0.33,7,Schule und Gesellschaft,gering,0.2128574750058127,False,-0.06690384334787969,2
|
||||||
|
7.15,Neuordnung des Schulbezirks,0.05,7,Schule und Gesellschaft,gering,0.7362514029180696,False,-0.07588868190734946,0
|
||||||
|
7.16,Schulwahlfreiheit,0.27,7,Schule und Gesellschaft,gering,0.3124183006535946,False,-0.06690384334787969,0
|
||||||
|
7.17,Fördermaßnahmen im Sekundarbereich I,0.18,7,Schule und Gesellschaft,gering,0.7052341597796145,False,-0.06690384334787967,0
|
||||||
|
7.18,Schulleitung,0.37,7,Schule und Gesellschaft,gering,0.4793284161490682,False,-0.06690384334787969,2
|
||||||
|
7.19,Schulklima,0.53,7,Schule und Gesellschaft,mittel,0.7467470644239921,False,-0.06690384334787967,2
|
||||||
|
7.2,Schuleffekte,0.48,7,Schule und Gesellschaft,mittel,0.7357936534418898,False,-0.06690384334787969,2
|
||||||
|
|||||||
|
@ -1,5 +1,5 @@
|
|||||||
{
|
{
|
||||||
"n_rows": 68,
|
"n_rows": 78,
|
||||||
"duplicate_ids": {},
|
"duplicate_ids": {},
|
||||||
"n_duplicates": 0,
|
"n_duplicates": 0,
|
||||||
"invalid_kapitel_entries": [],
|
"invalid_kapitel_entries": [],
|
||||||
|
|||||||
@ -1,5 +1,5 @@
|
|||||||
,n,mean,std,min,q1,median,q3,max,skew,kurtosis
|
,n,mean,std,min,q1,median,q3,max,skew,kurtosis
|
||||||
Gesamt,68,0.2458823529411765,0.37290403428819446,-0.65,0.0475,0.21,0.5,1.28,0.12925197443518738,0.2800914374662091
|
Gesamt,78,0.2474358974358975,0.352742266006083,-0.65,0.05,0.215,0.4875,1.28,0.12462357658528317,0.5621417918900056
|
||||||
Kapitel 5,38,0.3394736842105263,0.43649059535035,-0.65,0.1,0.385,0.6275,1.28,-0.29469572711835434,-0.11719052918009742
|
Kapitel 5,38,0.3394736842105263,0.43649059535035,-0.65,0.1,0.385,0.6275,1.28,-0.29469572711835434,-0.11719052918009742
|
||||||
Kapitel 6,20,0.1275,0.27132957236929645,-0.38,-0.0075,0.175,0.34500000000000003,0.56,-0.3580457406718272,-0.6941765728667146
|
Kapitel 6,20,0.1275,0.27132957236929645,-0.38,-0.0075,0.175,0.34500000000000003,0.56,-0.3580457406718272,-0.6941765728667146
|
||||||
Kapitel 7,10,0.127,0.11264990013311152,-0.09,0.05500000000000001,0.135,0.22,0.27,-0.5585413007473659,-0.22749527826960758
|
Kapitel 7,20,0.1925,0.15606594422669035,-0.09,0.065,0.185,0.27,0.53,0.5084512016471165,0.08880591241093594
|
||||||
|
|||||||
|
@ -1,69 +1,79 @@
|
|||||||
Thermometer_ID,Stichwort,Effektstärke,Kapitel,Kapitelname,Bin,Silhouette_point,Outlier_IQR,Text_Dimension,abs_d,SignifikanzScore,Rank_abs,Rank_score,Impact_Label
|
Thermometer_ID,Stichwort,Effektstärke,Kapitel,Kapitelname,Bin,Silhouette_point,Outlier_IQR,Text_Dimension,abs_d,SignifikanzScore,Rank_abs,Rank_score,Impact_Label
|
||||||
5.05,Erkenntnisstufen,1.28,5,Lernende,hoch,0.5057964601769913,True,-0.05914975460796303,1.28,0.7782633229981433,1,1,hoch+
|
5.05,Erkenntnisstufen,1.28,5,Lernende,hoch,0.4754440961337511,True,-0.054732267395592116,1.28,0.8491567794928772,1,1,hoch+
|
||||||
5.01,Vorausgehende Fähigkeiten & Intelligenz,0.96,5,Lernende,hoch,0.6502913752913749,False,-0.08735409337133096,0.96,0.7005156686176717,2,2,hoch+
|
5.01,Vorausgehende Fähigkeiten & Intelligenz,0.96,5,Lernende,hoch,0.6823734729493874,False,-0.08096089721466541,0.96,0.8039982503810714,2,2,hoch+
|
||||||
5.11,Beurteilung der eigenen Leistungsfähigkeit,0.96,5,Lernende,hoch,0.6502913752913749,False,-0.08382750975171666,0.96,0.7005156686176717,2,2,hoch+
|
5.11,Beurteilung der eigenen Leistungsfähigkeit,0.96,5,Lernende,hoch,0.6823734729493874,False,-0.07570327260842737,0.96,0.8039982503810714,2,2,hoch+
|
||||||
5.1,Feldunabhängigkeit,0.94,5,Lernende,hoch,0.6583828775267535,False,-0.05914975460796317,0.94,0.6951632881526102,4,4,hoch+
|
5.1,Feldunabhängigkeit,0.94,5,Lernende,hoch,0.6757741347905253,False,-0.05473226739559237,0.94,0.7909398630421234,4,4,hoch+
|
||||||
5.13,Kritisches Denken,0.84,5,Lernende,hoch,0.6930555555555555,False,-0.07336449853523312,0.84,0.6653646480780675,5,5,hoch+
|
5.13,Kritisches Denken,0.84,5,Lernende,hoch,0.5384615384615383,False,-0.06690384334787972,0.84,0.6709811100292111,5,5,hoch+
|
||||||
5.28,Positiv-aktivierend (Neugierde),0.74,5,Lernende,hoch,0.7238450074515644,False,0.5855547077500068,0.74,0.6335275154343559,6,6,hoch+
|
7.19,Schulklima,0.53,7,Schule und Gesellschaft,mittel,0.7467470644239921,False,-0.06690384334787967,0.53,0.6313332717361173,17,6,mittel+
|
||||||
5.02,Vorausgehendes Leistungsniveau,0.73,5,Lernende,hoch,0.7260754716981136,False,-0.07336449853523334,0.73,0.6298983936260716,7,7,hoch+
|
5.29,Positiv-aktivierend (Glücklichsein),0.54,5,Lernende,mittel,0.7309884383650871,False,0.7596274860802354,0.54,0.6278749537761342,16,7,mittel+
|
||||||
5.2,Beharrlichkeit und Zuversicht (Selbstkontrolle),0.66,5,Lernende,mittel,0.7335820895522391,False,-0.3354325030205649,0.66,0.6002389791577778,8,8,mittel+
|
5.19,Beharrlichkeit und Zuversicht (Positives Selbstbild),0.51,5,Lernende,mittel,0.754258655679233,False,-0.29756562936567754,0.51,0.6256697258875981,18,8,mittel+
|
||||||
5.18,Beharrlichkeit und Zuversicht (Selbstwirksamkeitserwartung),0.64,5,Lernende,mittel,0.7331058020477826,False,-0.3354325030205649,0.64,0.5903889528895614,10,9,mittel+
|
5.04,Beziehung zwischen Schul- und Universitätsleistungen,0.55,5,Lernende,mittel,0.7120401337792641,False,-0.16752469249087146,0.55,0.6227450827653359,15,9,mittel+
|
||||||
5.07,Stärke des Arbeitsgedächtnisses,0.63,5,Lernende,mittel,0.7313852813852812,False,-0.07892435655637323,0.63,0.5846857686644674,11,10,mittel+
|
5.26,Positiv-aktivierend (Freude),0.5,5,Lernende,mittel,0.7523286287139656,False,0.7596274860802354,0.5,0.6198582940349135,19,10,mittel+
|
||||||
5.06,Exekutive Funktionen,0.62,5,Lernende,mittel,0.7281195079086115,False,-0.05914975460796313,0.62,0.5781714067558055,12,11,mittel+
|
5.24,Emotionen (Emotionale Intelligenz),0.5,5,Lernende,mittel,0.7523286287139656,False,-0.09749403000680268,0.5,0.6198582940349135,19,10,mittel+
|
||||||
5.23,Emotionen,0.61,5,Lernende,mittel,0.7232381801962534,False,-0.1013942427833901,0.61,0.570808962558881,13,12,mittel+
|
6.01,Sozioökonomischer Status,0.56,6,Elternhaus und Familie,mittel,0.6896373947413648,False,-0.06690384334787969,0.56,0.6158049131935759,14,12,mittel+
|
||||||
6.01,Sozioökonomischer Status,0.56,6,Elternhaus und Familie,mittel,0.6827943498774371,False,-0.07336449853523314,0.56,0.5255780466260127,14,13,mittel+
|
6.14,Elternerwartungen,0.49,6,Elternhaus und Familie,mittel,0.7454899415963664,False,-0.06690384334787967,0.49,0.6114744757226528,21,13,mittel+
|
||||||
5.04,Beziehung zwischen Schul- und Universitätsleistungen,0.55,5,Lernende,mittel,0.680520117762513,False,-0.175121884151931,0.55,0.5195841925705693,15,14,mittel+
|
7.2,Schuleffekte,0.48,7,Schule und Gesellschaft,mittel,0.7357936534418898,False,-0.06690384334787969,0.48,0.6015931297784081,22,14,mittel+
|
||||||
6.14,Elternerwartungen,0.49,6,Elternhaus und Familie,mittel,0.7301343867848403,False,-0.07336449853523314,0.49,0.5168291137953939,20,15,mittel+
|
5.33,negativ-aktivierend (Wut),-0.65,5,Lernende,negativ,0.5657478187012586,True,0.48991533153157263,0.65,-0.5940805024586342,9,15,mittel−
|
||||||
5.29,Positiv-aktivierend (Glücklichsein),0.54,5,Lernende,mittel,0.6708582834331347,False,0.7827951695376463,0.54,0.5097122300292544,16,16,mittel+
|
5.36,negativ-aktivierend (Langeweile),-0.46,5,Lernende,negativ,0.7126947637292466,False,0.665618594201167,0.46,-0.579888142004947,23,16,mittel−
|
||||||
6.12,Elternunterstützung beim Lernen,0.41,6,Elternhaus und Familie,mittel,0.77761147335638,False,-0.07336449853523314,0.41,0.5033521249392942,22,17,mittel+
|
5.38,kognitive Dispositionen (Prokrastination),-0.41,5,Lernende,negativ,0.7479620323841432,False,-0.08006329810038103,0.41,-0.5743699764646016,24,17,mittel−
|
||||||
6.11,Häusliches Anregungsniveau,0.4,6,Elternhaus und Familie,mittel,0.7824192735239991,False,-0.07336449853523314,0.4,0.5010759709839467,25,18,mittel+
|
5.31,negativ-aktivierend (Angst),-0.4,5,Lernende,negativ,0.7516072139728055,False,0.4899153315315725,0.4,-0.571480236183217,27,18,mittel−
|
||||||
6.19,Programme für Eltern,0.39,6,Elternhaus und Familie,gering,0.7865197915104075,False,-0.07336449853523314,0.39,0.4984285305205778,28,19,gering+
|
5.23,Emotionen,0.61,5,Lernende,mittel,0.5420821699242117,False,-0.09469066931400565,0.61,0.5624785042387641,13,19,mittel+
|
||||||
7.02,Accountability / Rechenschaftspflicht,0.27,7,Schule und Gesellschaft,gering,0.8900556973256574,False,-0.0733644985352329,0.27,0.49517951792876724,35,20,gering+
|
6.17,Schulwechsel,-0.38,6,Elternhaus und Familie,negativ,0.7505765478091184,False,-0.06690384334787967,0.38,-0.5613401149278164,31,20,gering−
|
||||||
7.04,Qualität des Schulgebäudes,0.24,7,Schule und Gesellschaft,gering,0.9083468761774898,False,-0.07892435655637318,0.24,0.4903814393455832,38,21,gering+
|
5.06,Exekutive Funktionen,0.62,5,Lernende,mittel,0.5038120750051506,False,-0.054732267395592345,0.62,0.547223036774851,12,21,mittel+
|
||||||
7.06,Konfessionsschulen,0.23,7,Schule und Gesellschaft,gering,0.9129250266162217,False,-0.07336449853523314,0.23,0.4879847311783926,40,22,gering+
|
5.07,Stärke des Arbeitsgedächtnisses,0.63,5,Lernende,mittel,0.4599317988064778,False,-0.07588868190734946,0.63,0.5290275502606099,11,22,mittel+
|
||||||
6.1,Kinderheime,0.33,6,Elternhaus und Familie,gering,0.8071221370551951,False,-0.07336449853523312,0.33,0.4804436947330976,32,23,gering+
|
6.15,Körperliche Züchtigung,-0.33,6,Elternhaus und Familie,negativ,0.7233372687918145,False,-0.05473226739559239,0.33,-0.5230653535187484,35,23,gering−
|
||||||
5.33,negativ-aktivierend (Wut),-0.65,5,Lernende,negativ,0.510747535596933,True,0.44255048994701485,0.65,-0.47846238112862827,9,24,mittel−
|
5.17,Beharrlichkeit und Zuversicht (Konzentration / Ausdauer und Engagement),0.41,5,Lernende,mittel,0.6271031151091123,False,-0.31723823624074776,0.41,0.511033784373636,24,24,mittel+
|
||||||
5.19,Beharrlichkeit und Zuversicht (Positives Selbstbild),0.51,5,Lernende,mittel,0.6344374342797038,False,-0.3021008406853567,0.51,0.476193170818376,17,25,mittel+
|
6.12,Elternunterstützung beim Lernen,0.41,6,Elternhaus und Familie,mittel,0.6271031151091123,False,-0.06690384334787967,0.41,0.511033784373636,24,24,mittel+
|
||||||
7.01,Finanzielle Ausstattung,0.19,7,Schule und Gesellschaft,gering,0.9251410228772312,False,-0.07336449853523312,0.19,0.4751974964285984,46,26,gering+
|
5.18,Beharrlichkeit und Zuversicht (Selbstwirksamkeitserwartung),0.64,5,Lernende,mittel,0.40984330169940336,False,-0.33142249478922153,0.64,0.5075786416500672,10,26,mittel+
|
||||||
7.09,Sommerschulen,0.17,7,Schule und Gesellschaft,gering,0.9281937118300768,False,-0.07336449853523315,0.17,0.46720000000000006,50,27,gering+
|
5.12,Kreativität und Lernleistung in Beziehung setzen,0.4,5,Lernende,mittel,0.6006217348128319,False,-0.1591048977238036,0.4,0.492356200269003,27,27,mittel+
|
||||||
5.24,Emotionen (Emotionale Intelligenz),0.5,5,Lernende,mittel,0.6195931477516049,False,-0.1010290138898771,0.5,0.4636006893230006,18,28,mittel+
|
6.11,Häusliches Anregungsniveau,0.4,6,Elternhaus und Familie,mittel,0.6006217348128319,False,-0.06690384334787969,0.4,0.492356200269003,27,27,mittel+
|
||||||
5.26,Positiv-aktivierend (Freude),0.5,5,Lernende,mittel,0.6195931477516049,False,0.7827951695376463,0.5,0.4636006893230006,18,28,mittel+
|
5.32,negativ-aktivierend (Depressionen),-0.3,5,Lernende,negativ,0.6883554432512108,False,0.4899153315315726,0.3,-0.49033310570379907,38,29,gering−
|
||||||
6.08,nicht-geschieden vs. wiederverheiratet,0.24,6,Elternhaus und Familie,gering,0.8324161165454884,False,-0.09341002740120666,0.24,0.4505217237410541,38,30,gering+
|
6.19,Programme für Eltern,0.39,6,Elternhaus und Familie,gering,0.5654863290004483,False,-0.06690384334787967,0.39,0.4691434686154248,30,30,gering+
|
||||||
6.2,Hausbesuche durch Lehrpersonen,0.22,6,Elternhaus und Familie,gering,0.8366902739695423,False,-0.07336449853523314,0.22,0.44316543498795136,41,31,gering+
|
5.2,Beharrlichkeit und Zuversicht (Selbstkontrolle),0.66,5,Lernende,mittel,0.2915181753385596,False,-0.3314224947892215,0.66,0.4551702842913464,8,31,mittel+
|
||||||
6.09,Adoption,0.21,6,Elternhaus und Familie,gering,0.8381014976799916,False,-0.0733644985352332,0.21,0.4391062543347549,42,32,gering+
|
5.3,Positiv-aktivierend (Entspannung),0.16,5,Lernende,gering,0.7269595176571921,False,0.7596274860802354,0.16,0.4433635953292121,58,32,gering+
|
||||||
6.16,Väter,0.21,6,Elternhaus und Familie,gering,0.8381014976799916,False,-0.07336449853523312,0.21,0.4391062543347549,42,32,gering+
|
7.09,Sommerschulen,0.17,7,Schule und Gesellschaft,gering,0.7173333333333332,False,-0.06690384334787969,0.17,0.44311898734177213,57,33,gering+
|
||||||
7.1,Schulkalender / Stundenplan,0.1,7,Schule und Gesellschaft,gering,0.9281794453692277,False,-0.07336449853523314,0.1,0.4335925108470548,55,34,gering+
|
5.21,Schülerpersönlichkeit,0.18,5,Lernende,gering,0.7052341597796145,False,-0.06690384334787969,0.18,0.441578407783241,54,34,gering+
|
||||||
5.36,negativ-aktivierend (Langeweile),-0.46,5,Lernende,negativ,0.5871794871794872,False,0.4425504899470148,0.46,-0.42738519654552776,21,35,mittel−
|
5.37,kognitive Dispositionen (Morgentypus vs. Abendtypus),0.18,5,Lernende,gering,0.7052341597796145,False,-0.08265613474581207,0.18,0.441578407783241,54,34,gering+
|
||||||
7.07,Monoedukation,0.07,7,Schule und Gesellschaft,gering,0.9235929535014257,False,-0.07336449853523312,0.07,0.41678484019606843,58,36,gering+
|
7.17,Fördermaßnahmen im Sekundarbereich I,0.18,7,Schule und Gesellschaft,gering,0.7052341597796145,False,-0.06690384334787967,0.18,0.441578407783241,54,34,gering+
|
||||||
6.17,Schulwechsel,-0.38,6,Elternhaus und Familie,negativ,0.6384645645869302,False,-0.07336449853523312,0.38,-0.4159072056249074,29,37,gering−
|
6.06,Familienstruktur,0.14,6,Elternhaus und Familie,gering,0.7412814274128143,False,-0.05473226739559239,0.14,0.44126900120114576,60,37,gering+
|
||||||
5.38,kognitive Dispositionen (Prokrastination),-0.41,5,Lernende,negativ,0.6059563758389256,False,-0.08917102326885956,0.41,-0.4132420904516396,22,38,mittel−
|
5.14,Beharrlichkeit und Zuversicht (Mindset),0.19,5,Lernende,gering,0.6866096866096867,False,-0.3314224947892216,0.19,0.43661824083089906,52,38,gering+
|
||||||
5.31,negativ-aktivierend (Angst),-0.4,5,Lernende,negativ,0.608739837398373,False,0.4425504899470148,0.4,-0.4099032635874468,25,39,mittel−
|
7.01,Finanzielle Ausstattung,0.19,7,Schule und Gesellschaft,gering,0.6866096866096867,False,-0.06690384334787969,0.19,0.43661824083089906,52,38,gering+
|
||||||
6.15,Körperliche Züchtigung,-0.33,6,Elternhaus und Familie,negativ,0.668243380062615,False,-0.07336449853523312,0.33,-0.40753954121134467,32,40,gering−
|
7.1,Schulkalender / Stundenplan,0.1,7,Schule und Gesellschaft,gering,0.7596223674655048,False,-0.06690384334787969,0.1,0.43168058244394814,62,40,gering+
|
||||||
6.06,Familienstruktur,0.14,6,Elternhaus und Familie,gering,0.8378335891817927,False,-0.059149754607963165,0.14,0.4053656162508216,53,41,gering+
|
6.07,Geschieden,-0.26,6,Elternhaus und Familie,negativ,0.6103755323267518,False,-0.07597551679303022,0.26,-0.4302676840294623,41,41,gering−
|
||||||
7.03,Leistungsbezogene Bezahlung,0.05,7,Schule und Gesellschaft,gering,0.9174717855887603,False,-0.07336449853523314,0.05,0.40397154411752395,60,42,gering+
|
5.27,Positiv-aktivierend (Hoffnung),0.2,5,Lernende,gering,0.6627335299901671,False,0.7596274860802354,0.2,0.4289059258429483,50,42,gering+
|
||||||
7.05,Vertragsschulen / Charter-Schulen,0.04,7,Schule und Gesellschaft,gering,0.9128781474571108,False,-0.07336449853523315,0.04,0.396760122048193,63,43,gering+
|
5.08,Vorschulische nicht-kognitive Fähigkeiten,0.2,5,Lernende,gering,0.6627335299901671,False,-0.08422827613027246,0.2,0.4289059258429483,50,42,gering+
|
||||||
6.07,Geschieden,-0.26,6,Elternhaus und Familie,negativ,0.7061030196788244,False,-0.08431062057732165,0.26,-0.3938138908860457,36,44,gering−
|
7.12,Ethnische Vielfalt,0.09,7,Schule und Gesellschaft,gering,0.762208067940552,False,-0.06690384334787969,0.09,0.4282356204144158,63,44,gering+
|
||||||
7.08,Dauer der Sommerferien,-0.09,7,Schule und Gesellschaft,negativ,0.8327092370446383,False,-0.08477035593782843,0.09,-0.37867559681950863,56,45,gering−
|
5.28,Positiv-aktivierend (Neugierde),0.74,5,Lernende,hoch,0.1650485436893238,False,0.5687106778355705,0.74,0.4272937937814937,6,45,hoch+
|
||||||
5.32,negativ-aktivierend (Depressionen),-0.3,5,Lernende,negativ,0.6266841317365268,False,0.4425504899470148,0.3,-0.3713230886031487,34,46,gering−
|
5.25,Emotionen (Wohlbefinden),0.08,5,Lernende,gering,0.7632850241545894,False,-0.09014704327625096,0.08,0.42400000000000004,65,46,gering+
|
||||||
6.18,Fernsehen,-0.15,6,Elternhaus und Familie,negativ,0.7598477443369885,False,-0.059149754607963165,0.15,-0.3692270873626853,52,47,gering−
|
7.07,Monoedukation,0.07,7,Schule und Gesellschaft,gering,0.762962962962963,False,-0.06690384334787967,0.07,0.419031223628692,66,47,gering+
|
||||||
6.13,Elterliche Autonomieunterstützung (Familienhilfe),0.06,6,Elternhaus und Familie,gering,0.8320762909326382,False,-0.07336449853523312,0.06,0.3639433328972249,59,48,gering+
|
6.16,Väter,0.21,6,Elternhaus und Familie,gering,0.6330275229357801,False,-0.06690384334787969,0.21,0.4181384740448265,48,48,gering+
|
||||||
6.02,Bezug staatlicher Transferleistungen,-0.12,6,Elternhaus und Familie,negativ,0.772215227319828,False,-0.07336449853523314,0.12,-0.3613193752948617,54,49,gering−
|
6.09,Adoption,0.21,6,Elternhaus und Familie,gering,0.6330275229357801,False,-0.06690384334787898,0.21,0.4181384740448265,48,48,gering+
|
||||||
6.05,Einwanderungsstatus,0.05,6,Elternhaus und Familie,gering,0.8306537734304565,False,-0.07336449853523312,0.05,0.35839658489477866,60,50,gering+
|
7.18,Schulleitung,0.37,7,Schule und Gesellschaft,gering,0.4793284161490682,False,-0.06690384334787969,0.37,0.4143923598553344,32,50,gering+
|
||||||
6.03,Erwerbstätigkeit der Mutter,0.05,6,Elternhaus und Familie,gering,0.8306537734304565,False,-0.08585665050056006,0.05,0.35839658489477866,60,50,gering+
|
5.03,Beziehung zwischen Schul- und Berufsleistungen,0.37,5,Lernende,gering,0.4793284161490682,False,-0.16752469249087093,0.37,0.4143923598553344,32,50,gering+
|
||||||
6.04,Stipendien,0.03,6,Elternhaus und Familie,gering,0.82487518707422,False,-0.07336449853523311,0.03,0.3457631263876847,65,52,gering+
|
6.13,Elterliche Autonomieunterstützung (Familienhilfe),0.06,6,Elternhaus und Familie,gering,0.7516420361247947,False,-0.06690384334787967,0.06,0.4082984847540064,67,52,gering+
|
||||||
5.17,Beharrlichkeit und Zuversicht (Konzentration / Ausdauer und Engagement),0.41,5,Lernende,mittel,0.4177336747759273,False,-0.3218185254749976,0.41,0.31443492230202125,22,53,mittel+
|
6.2,Hausbesuche durch Lehrpersonen,0.22,6,Elternhaus und Familie,gering,0.5968253968253969,False,-0.06690384334787967,0.22,0.4039667269439422,47,53,gering+
|
||||||
5.12,Kreativität und Lernleistung in Beziehung setzen,0.4,5,Lernende,mittel,0.3880890052355992,False,-0.14757153731358338,0.4,0.2940730063829064,25,54,mittel+
|
7.03,Leistungsbezogene Bezahlung,0.05,7,Schule und Gesellschaft,gering,0.7362514029180696,False,-0.06690384334787969,0.05,0.39543301368111494,68,54,gering+
|
||||||
5.34,negativ-aktivierend (Frustration),-0.04,5,Lernende,negativ,0.6507201646090555,False,0.6056986180050421,0.04,-0.25914076079822995,63,55,gering−
|
7.15,Neuordnung des Schulbezirks,0.05,7,Schule und Gesellschaft,gering,0.7362514029180696,False,-0.07588868190734946,0.05,0.39543301368111494,68,54,gering+
|
||||||
5.22,Perfektionismus,-0.03,5,Lernende,negativ,0.6501826722338218,False,-0.07336449853523314,0.03,-0.2540586051432589,65,56,gering−
|
6.05,Einwanderungsstatus,0.05,6,Elternhaus und Familie,gering,0.7362514029180696,False,-0.06690384334787969,0.05,0.39543301368111494,68,54,gering+
|
||||||
5.09,Gekreuzte Lateralität,-0.03,5,Lernende,negativ,0.6501826722338218,False,-0.07336449853523311,0.03,-0.2540586051432589,65,56,gering−
|
7.13,Wohnheimunterbringung,0.05,7,Schule und Gesellschaft,gering,0.7362514029180696,False,-0.06690384334787969,0.05,0.39543301368111494,68,54,gering+
|
||||||
5.25,Emotionen (Wohlbefinden),0.08,5,Lernende,gering,0.5842661691542289,False,-0.09745012386374446,0.08,0.24345585549171578,57,58,gering+
|
6.03,Erwerbstätigkeit der Mutter,0.05,6,Elternhaus und Familie,gering,0.7362514029180696,False,-0.07741252027576469,0.05,0.39543301368111494,68,54,gering+
|
||||||
5.35,negativ-aktivierend (Aggression und Gewalt),0.03,5,Lernende,gering,0.6225686498855841,False,0.30890529874855743,0.03,0.23956267381276325,65,59,gering+
|
5.02,Vorausgehendes Leistungsniveau,0.73,5,Lernende,hoch,0.09764309764310186,False,-0.06690384334788037,0.73,0.3871699271192964,7,59,hoch+
|
||||||
5.3,Positiv-aktivierend (Entspannung),0.16,5,Lernende,gering,0.48663294797687695,False,0.7827951695376463,0.16,0.23060347176021956,51,60,gering+
|
7.06,Konfessionsschulen,0.23,7,Schule und Gesellschaft,gering,0.5555555555555554,False,-0.05473226739559239,0.23,0.387139240506329,45,60,gering+
|
||||||
5.03,Beziehung zwischen Schul- und Berufsleistungen,0.37,5,Lernende,gering,0.2835203366058893,False,-0.17512188415193075,0.37,0.2247798719940885,30,61,gering+
|
7.11,Desegration,0.23,7,Schule und Gesellschaft,gering,0.5555555555555554,False,-0.06690384334787969,0.23,0.387139240506329,45,60,gering+
|
||||||
5.21,Schülerpersönlichkeit,0.18,5,Lernende,gering,0.45180722891566244,False,-0.07336449853523312,0.18,0.22192177322024864,48,62,gering+
|
7.05,Vertragsschulen / Charter-Schulen,0.04,7,Schule und Gesellschaft,gering,0.71286701208981,False,-0.06690384334787967,0.04,0.37837840886731816,73,62,gering+
|
||||||
5.37,kognitive Dispositionen (Morgentypus vs. Abendtypus),0.18,5,Lernende,gering,0.45180722891566244,False,-0.09227886985139404,0.18,0.22192177322024864,48,62,gering+
|
7.04,Qualität des Schulgebäudes,0.24,7,Schule und Gesellschaft,gering,0.506300114547537,False,-0.07588868190734947,0.24,0.3661268954717472,43,63,gering+
|
||||||
5.14,Beharrlichkeit und Zuversicht (Mindset),0.19,5,Lernende,gering,0.42788461538461536,False,-0.33543250302056493,0.19,0.21416364030433988,46,64,gering+
|
6.08,nicht-geschieden vs. wiederverheiratet,0.24,6,Elternhaus und Familie,gering,0.506300114547537,False,-0.08358940952566345,0.24,0.3661268954717472,43,63,gering+
|
||||||
5.08,Vorschulische nicht-kognitive Fähigkeiten,0.2,5,Lernende,gering,0.4001572327044027,False,-0.09398809922530565,0.2,0.20440820067160598,44,65,gering+
|
5.35,negativ-aktivierend (Aggression und Gewalt),0.03,5,Lernende,gering,0.6867612293144207,False,0.3512922425196881,0.03,0.3598976568812281,75,65,gering+
|
||||||
5.27,Positiv-aktivierend (Hoffnung),0.2,5,Lernende,gering,0.4001572327044027,False,0.7827951695376463,0.2,0.20440820067160598,44,65,gering+
|
6.04,Stipendien,0.03,6,Elternhaus und Familie,gering,0.6867612293144207,False,-0.06690384334787969,0.03,0.3598976568812281,75,65,gering+
|
||||||
5.16,Beharrlichkeit und Zuversicht (Durchhaltevermögen),0.35,5,Lernende,gering,0.200073637702503,False,-0.33543250302056504,0.35,0.17137467759664005,31,67,gering+
|
5.16,Beharrlichkeit und Zuversicht (Durchhaltevermögen),0.35,5,Lernende,gering,0.36200148101131857,False,-0.3314224947892214,0.35,0.3433071052388429,34,67,gering+
|
||||||
5.15,Beharrlichkeit und Zuversicht (Achtsamkeit),0.26,5,Lernende,gering,0.16621376811594119,False,-0.33543250302056515,0.26,0.1104,36,68,gering+
|
5.15,Beharrlichkeit und Zuversicht (Achtsamkeit),0.26,5,Lernende,gering,0.38451935081148536,False,-0.385175633205326,0.26,0.3119076091594366,41,68,gering+
|
||||||
|
7.16,Schulwahlfreiheit,0.27,7,Schule und Gesellschaft,gering,0.3124183006535946,False,-0.06690384334787969,0.27,0.2789230081906179,39,69,gering+
|
||||||
|
7.02,Accountability / Rechenschaftspflicht,0.27,7,Schule und Gesellschaft,gering,0.3124183006535946,False,-0.06690384334787969,0.27,0.2789230081906179,39,69,gering+
|
||||||
|
7.14,Schulgröße,0.33,7,Schule und Gesellschaft,gering,0.2128574750058127,False,-0.06690384334787969,0.33,0.25554809449671706,35,71,gering+
|
||||||
|
6.1,Kinderheime,0.33,6,Elternhaus und Familie,gering,0.2128574750058127,False,-0.05473226739559239,0.33,0.25554809449671706,35,71,gering+
|
||||||
|
5.22,Perfektionismus,-0.03,5,Lernende,negativ,0.4743319268635724,False,-0.06690384334787969,0.03,-0.2485739464829354,75,73,gering−
|
||||||
|
5.09,Gekreuzte Lateralität,-0.03,5,Lernende,negativ,0.4743319268635724,False,-0.06690384334787969,0.03,-0.2485739464829354,75,73,gering−
|
||||||
|
5.34,negativ-aktivierend (Frustration),-0.04,5,Lernende,negativ,0.4271968046477851,False,0.665618594201167,0.04,-0.22867275585339622,73,75,gering−
|
||||||
|
6.18,Fernsehen,-0.15,6,Elternhaus und Familie,negativ,0.19217466493731084,False,-0.05473226739559239,0.15,-0.15830925478993252,59,76,gering−
|
||||||
|
7.08,Dauer der Sommerferien,-0.09,7,Schule und Gesellschaft,negativ,0.12543402777777787,False,-0.07649636029329497,0.09,-0.09453378164556966,63,77,gering−
|
||||||
|
6.02,Bezug staatlicher Transferleistungen,-0.12,6,Elternhaus und Familie,negativ,-0.013035159052858219,False,-0.06690384334787969,0.12,-0.043199999999999995,61,78,gering−
|
||||||
|
|||||||
|
@ -1,13 +1,13 @@
|
|||||||
{
|
{
|
||||||
"silhouette_global": 0.6815565731142729,
|
"silhouette_global": 0.5859691845119998,
|
||||||
"levene_W": 6.124238885035784,
|
"levene_W": 8.053380864529343,
|
||||||
"levene_p": 0.0036581487538003225,
|
"levene_p": 0.0006786556735287001,
|
||||||
"kruskal_H": 6.095304064204115,
|
"kruskal_H": 5.717436337170795,
|
||||||
"kruskal_p": 0.04747025227129131,
|
"kruskal_p": 0.05734221623047485,
|
||||||
"kruskal_eps2": 0.06112394125677784,
|
"kruskal_eps2": 0.048278393989231096,
|
||||||
"spearman_rho_text_d": -0.27386411336988703,
|
"spearman_rho_text_d": -0.23679802495635682,
|
||||||
"spearman_p_text_d": 0.023829588725956963,
|
"spearman_p_text_d": 0.03685509517309772,
|
||||||
"chi2": 16.792602473926756,
|
"chi2": 21.11754385964912,
|
||||||
"chi2_p": 0.010076456895569883,
|
"chi2_p": 0.0017474931607551554,
|
||||||
"chi2_df": 6
|
"chi2_df": 6
|
||||||
}
|
}
|
||||||
File diff suppressed because it is too large
Load Diff
@ -18,7 +18,6 @@ import json
|
|||||||
|
|
||||||
from sklearn.preprocessing import OneHotEncoder
|
from sklearn.preprocessing import OneHotEncoder
|
||||||
from sklearn.cluster import KMeans
|
from sklearn.cluster import KMeans
|
||||||
from sklearn.metrics import silhouette_score
|
|
||||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||||
from sklearn.decomposition import PCA
|
from sklearn.decomposition import PCA
|
||||||
from sklearn.metrics import silhouette_score, silhouette_samples
|
from sklearn.metrics import silhouette_score, silhouette_samples
|
||||||
@ -101,6 +100,8 @@ def load_data(csv_path: str) -> pd.DataFrame:
|
|||||||
df["Effektstärke"].astype(str).str.replace(",", ".", regex=False).str.strip()
|
df["Effektstärke"].astype(str).str.replace(",", ".", regex=False).str.strip()
|
||||||
)
|
)
|
||||||
df["Effektstärke"] = pd.to_numeric(df["Effektstärke"], errors="coerce")
|
df["Effektstärke"] = pd.to_numeric(df["Effektstärke"], errors="coerce")
|
||||||
|
# explizit ±inf auf NaN setzen, um sie zu entfernen
|
||||||
|
df["Effektstärke"] = df["Effektstärke"].replace([np.inf, -np.inf], np.nan)
|
||||||
|
|
||||||
# Kapitel aus Thermometer_ID ableiten und Kapitelname mappen
|
# Kapitel aus Thermometer_ID ableiten und Kapitelname mappen
|
||||||
df["Kapitel"] = df["Thermometer_ID"].astype(str).str.split(".").str[0].astype(int)
|
df["Kapitel"] = df["Thermometer_ID"].astype(str).str.split(".").str[0].astype(int)
|
||||||
@ -171,7 +172,7 @@ def add_manual_bins(df: pd.DataFrame) -> pd.DataFrame:
|
|||||||
# K-Means-Clustering (Effektstärke + Kapitel)
|
# K-Means-Clustering (Effektstärke + Kapitel)
|
||||||
# -----------------------------------------
|
# -----------------------------------------
|
||||||
|
|
||||||
def encode_features(df: pd.DataFrame) -> tuple[np.ndarray, list[str]]:
|
def encode_features(df: pd.DataFrame, kapitel_weight: float = 1.0) -> tuple[np.ndarray, list[str]]:
|
||||||
"""One-Hot-Encoding des Kapitels + Effektstärke (metrisch)."""
|
"""One-Hot-Encoding des Kapitels + Effektstärke (metrisch)."""
|
||||||
try:
|
try:
|
||||||
enc = OneHotEncoder(sparse_output=False, handle_unknown="ignore") # neuere sklearn-Versionen
|
enc = OneHotEncoder(sparse_output=False, handle_unknown="ignore") # neuere sklearn-Versionen
|
||||||
@ -179,13 +180,14 @@ def encode_features(df: pd.DataFrame) -> tuple[np.ndarray, list[str]]:
|
|||||||
enc = OneHotEncoder(sparse=False, handle_unknown="ignore") # ältere sklearn-Versionen
|
enc = OneHotEncoder(sparse=False, handle_unknown="ignore") # ältere sklearn-Versionen
|
||||||
cat = df[["Kapitel"]].fillna(-1)
|
cat = df[["Kapitel"]].fillna(-1)
|
||||||
cat_ohe = enc.fit_transform(cat)
|
cat_ohe = enc.fit_transform(cat)
|
||||||
|
cat_ohe = cat_ohe * float(kapitel_weight)
|
||||||
eff = df[["Effektstärke"]].values
|
eff = df[["Effektstärke"]].values
|
||||||
X = np.hstack([eff, cat_ohe])
|
X = np.hstack([eff, cat_ohe])
|
||||||
feature_names = ["Effektstärke"] + [f"kap::{c}" for c in enc.get_feature_names_out(["Kapitel"])]
|
feature_names = ["Effektstärke"] + [f"kap::{c}" for c in enc.get_feature_names_out(["Kapitel"])]
|
||||||
return X, feature_names
|
return X, feature_names
|
||||||
|
|
||||||
|
|
||||||
def encode_features_3d(df: pd.DataFrame) -> tuple[np.ndarray, list[str]]:
|
def encode_features_3d(df: pd.DataFrame, kapitel_weight: float = 1.0) -> tuple[np.ndarray, list[str]]:
|
||||||
"""Effektstärke + Kapitel + Textdimension (TF-IDF + PCA) für 3D-Clustering."""
|
"""Effektstärke + Kapitel + Textdimension (TF-IDF + PCA) für 3D-Clustering."""
|
||||||
# Kapitel
|
# Kapitel
|
||||||
try:
|
try:
|
||||||
@ -194,6 +196,7 @@ def encode_features_3d(df: pd.DataFrame) -> tuple[np.ndarray, list[str]]:
|
|||||||
enc = OneHotEncoder(sparse=False, handle_unknown="ignore")
|
enc = OneHotEncoder(sparse=False, handle_unknown="ignore")
|
||||||
cat = df[["Kapitel"]].fillna(-1)
|
cat = df[["Kapitel"]].fillna(-1)
|
||||||
cat_ohe = enc.fit_transform(cat)
|
cat_ohe = enc.fit_transform(cat)
|
||||||
|
cat_ohe = cat_ohe * float(kapitel_weight)
|
||||||
|
|
||||||
# Effektstärke
|
# Effektstärke
|
||||||
eff = df[["Effektstärke"]].values
|
eff = df[["Effektstärke"]].values
|
||||||
@ -201,6 +204,8 @@ def encode_features_3d(df: pd.DataFrame) -> tuple[np.ndarray, list[str]]:
|
|||||||
# Textdimension über TF-IDF + PCA
|
# Textdimension über TF-IDF + PCA
|
||||||
vectorizer = TfidfVectorizer(max_features=100)
|
vectorizer = TfidfVectorizer(max_features=100)
|
||||||
X_text = vectorizer.fit_transform(df["Stichwort"].astype(str))
|
X_text = vectorizer.fit_transform(df["Stichwort"].astype(str))
|
||||||
|
# Sicherstellen, dass TF-IDF keine Inf/NaN enthält (sollte nicht vorkommen)
|
||||||
|
X_text = X_text.tocsr()
|
||||||
pca = PCA(n_components=1, random_state=42)
|
pca = PCA(n_components=1, random_state=42)
|
||||||
text_dim = pca.fit_transform(X_text.toarray())
|
text_dim = pca.fit_transform(X_text.toarray())
|
||||||
|
|
||||||
@ -212,9 +217,26 @@ def encode_features_3d(df: pd.DataFrame) -> tuple[np.ndarray, list[str]]:
|
|||||||
feature_names = ["Effektstärke"] + list(enc.get_feature_names_out(["Kapitel"])) + ["Text_Dimension"]
|
feature_names = ["Effektstärke"] + list(enc.get_feature_names_out(["Kapitel"])) + ["Text_Dimension"]
|
||||||
return X, feature_names
|
return X, feature_names
|
||||||
|
|
||||||
|
# -----------------------------------------
|
||||||
|
# Hilfsfunktion zur Sanitisierung von Feature-Matrizen
|
||||||
|
# -----------------------------------------
|
||||||
|
def _sanitize_X(X: np.ndarray, clip: float | None = None) -> np.ndarray:
|
||||||
|
"""Ersetzt NaN/Inf in Feature-Matrizen und optionales Clipping gegen numerische Ausreißer.
|
||||||
|
Gibt eine *neue* Matrix zurück.
|
||||||
|
"""
|
||||||
|
X = np.asarray(X, dtype=float).copy()
|
||||||
|
# NaN/Inf -> 0
|
||||||
|
X[~np.isfinite(X)] = 0.0
|
||||||
|
if clip is not None and clip > 0:
|
||||||
|
X = np.clip(X, -float(clip), float(clip))
|
||||||
|
return X
|
||||||
|
|
||||||
def run_kmeans(df: pd.DataFrame, k: int = 4, random_state: int = 42):
|
|
||||||
X, feature_names = encode_features(df)
|
def run_kmeans(df: pd.DataFrame, k: int = 4, random_state: int = 42, kapitel_weight: float = 1.0):
|
||||||
|
X, feature_names = encode_features(df, kapitel_weight=kapitel_weight)
|
||||||
|
X = _sanitize_X(X, clip=1e6)
|
||||||
|
if not np.isfinite(X).all():
|
||||||
|
print("Warnung: Nicht-endliche Werte in X nach Sanitisierung – werden als 0 behandelt.")
|
||||||
model = KMeans(n_clusters=k, n_init=20, random_state=random_state)
|
model = KMeans(n_clusters=k, n_init=20, random_state=random_state)
|
||||||
labels = model.fit_predict(X)
|
labels = model.fit_predict(X)
|
||||||
sil = silhouette_score(X, labels) if k > 1 and len(df) > k else np.nan
|
sil = silhouette_score(X, labels) if k > 1 and len(df) > k else np.nan
|
||||||
@ -362,14 +384,14 @@ def chi2_bins_kapitel(df: pd.DataFrame):
|
|||||||
print(f"Chi²={chi2[0]:.3f}, p={chi2[1]:.6f}, df={chi2[2]} (Unabhängigkeitstest)")
|
print(f"Chi²={chi2[0]:.3f}, p={chi2[1]:.6f}, df={chi2[2]} (Unabhängigkeitstest)")
|
||||||
return ct
|
return ct
|
||||||
|
|
||||||
def cluster_diagnostics(df: pd.DataFrame, k_min: int = 2, k_max: int = 8):
|
def cluster_diagnostics(df: pd.DataFrame, k_min: int = 2, k_max: int = 8, kapitel_weight: float = 0.0):
|
||||||
X, _ = encode_features(df)
|
X, _ = encode_features(df, kapitel_weight=kapitel_weight)
|
||||||
inertias, sils, ks = [], [], []
|
inertias, sils, ks = [], [], []
|
||||||
for k in range(k_min, k_max+1):
|
for k in range(k_min, k_max + 1):
|
||||||
km = KMeans(n_clusters=k, n_init=20, random_state=42).fit(X)
|
km = KMeans(n_clusters=k, n_init=20, random_state=42).fit(X)
|
||||||
inertias.append(km.inertia_)
|
inertias.append(km.inertia_)
|
||||||
ks.append(k)
|
ks.append(k)
|
||||||
sils.append(silhouette_score(X, km.labels_) if k>1 else np.nan)
|
sils.append(silhouette_score(X, km.labels_) if k > 1 else np.nan)
|
||||||
colors = plotly_template.get_colors()
|
colors = plotly_template.get_colors()
|
||||||
fig = go.Figure()
|
fig = go.Figure()
|
||||||
fig.add_trace(go.Scatter(x=ks, y=inertias, mode="lines+markers",
|
fig.add_trace(go.Scatter(x=ks, y=inertias, mode="lines+markers",
|
||||||
@ -423,6 +445,7 @@ def build_significance_view(df: pd.DataFrame) -> pd.DataFrame:
|
|||||||
- score_cluster = Silhouette_point (kleiner 0 -> auf 0 gesetzt), anschließend min-max-normalisiert
|
- score_cluster = Silhouette_point (kleiner 0 -> auf 0 gesetzt), anschließend min-max-normalisiert
|
||||||
- Gesamt-Score = 0.6*norm(|d|) + 0.4*norm(max(Silhouette_point, 0))
|
- Gesamt-Score = 0.6*norm(|d|) + 0.4*norm(max(Silhouette_point, 0))
|
||||||
Vorzeichen des Scores folgt dem Vorzeichen von d, damit negative Effekte unten landen.
|
Vorzeichen des Scores folgt dem Vorzeichen von d, damit negative Effekte unten landen.
|
||||||
|
Hinweis: Clustering/Score in dieser Ansicht wird kapitelunabhängig berechnet, indem Kapitel-OHE mit Gewicht 0.0 skaliert wird.
|
||||||
"""
|
"""
|
||||||
tmp = df.copy()
|
tmp = df.copy()
|
||||||
# Basisgrößen
|
# Basisgrößen
|
||||||
@ -505,9 +528,9 @@ def plot_significance_space(df_sig: pd.DataFrame):
|
|||||||
))
|
))
|
||||||
|
|
||||||
# Referenzlinien
|
# Referenzlinien
|
||||||
fig.add_hline(y=0, line=dict(color=colors["border"], width=1))
|
fig.add_hline(y=0, line=dict(color=colors.get("depthArea"), width=1))
|
||||||
for x0 in [0.0, 0.40, 0.70, -0.40, -0.70]:
|
for x0 in [0.0, 0.40, 0.70, -0.40, -0.70]:
|
||||||
fig.add_vline(x=x0, line=dict(color=colors["border"], width=1, dash="dot"))
|
fig.add_vline(x=x0, line=dict(color=colors.get("depthArea"), width=1, dash="dot"))
|
||||||
|
|
||||||
fig.update_layout(plotly_template.get_standard_layout(
|
fig.update_layout(plotly_template.get_standard_layout(
|
||||||
"Signifikanz-geführter Raum: Effektstärke × Score (kapitelunabhängig)",
|
"Signifikanz-geführter Raum: Effektstärke × Score (kapitelunabhängig)",
|
||||||
@ -543,7 +566,7 @@ def plot_heatmap_kapitel_vs_d(df: pd.DataFrame, kapitel: int | None = None, bins
|
|||||||
scale.append([float(t), f"rgb({r},{g},{b})"])
|
scale.append([float(t), f"rgb({r},{g},{b})"])
|
||||||
return scale
|
return scale
|
||||||
|
|
||||||
colorscale = _two_color_scale(colors["depthArea"], colors["brightArea"]) if "depthArea" in colors else "Viridis"
|
colorscale = _two_color_scale(colors.get("depthArea", "#444"), colors.get("brightArea", "#fff")) if "depthArea" in colors and "brightArea" in colors else colors.get("continuous", "Viridis")
|
||||||
|
|
||||||
# Histogram2d
|
# Histogram2d
|
||||||
fig = go.Figure(data=go.Histogram2d(
|
fig = go.Figure(data=go.Histogram2d(
|
||||||
@ -684,51 +707,111 @@ def plot_bins(df: pd.DataFrame, kapitel: int | None = None):
|
|||||||
export_figure(fig, "vl-bins", export_fig_visual, export_fig_png)
|
export_figure(fig, "vl-bins", export_fig_visual, export_fig_png)
|
||||||
|
|
||||||
|
|
||||||
def plot_scatter(df: pd.DataFrame, cluster_labels: np.ndarray, model: KMeans, sil: float, title_suffix: str, kapitel: int | None = None):
|
def plot_scatter(df: pd.DataFrame, cluster_labels: np.ndarray, model: KMeans, sil: float, title_suffix: str, kapitel: int | None = None, top_n: int = 5):
|
||||||
|
"""
|
||||||
|
Kapitelunabhängiger 2D-Scatter:
|
||||||
|
- x: künstlicher Index, aber so angeordnet, dass Punkte je Cluster zusammenstehen
|
||||||
|
- y: Effektstärke (Cohen d)
|
||||||
|
- Farben: Cluster
|
||||||
|
Zusätzlich:
|
||||||
|
• horizontale Linien bei den Cluster-Mitteln (Ø d)
|
||||||
|
• Labels für die Top-N nach |d|
|
||||||
|
"""
|
||||||
styles = plotly_template.get_plot_styles()
|
styles = plotly_template.get_plot_styles()
|
||||||
|
colors = plotly_template.get_colors()
|
||||||
kapitel_label = f"Kapitel {kapitel}" if kapitel else "Gesamt"
|
kapitel_label = f"Kapitel {kapitel}" if kapitel else "Gesamt"
|
||||||
|
|
||||||
tmp = df.copy()
|
tmp = df.copy()
|
||||||
tmp["Cluster"] = cluster_labels.astype(int)
|
tmp["Cluster"] = cluster_labels.astype(int)
|
||||||
|
|
||||||
# Plot-X: Kapitel als ganze Zahlen; kleine Jitter-Verschiebung, damit Punkte nicht exakt übereinander liegen
|
|
||||||
rng = np.random.default_rng(42)
|
|
||||||
tmp["_kapitel_x"] = tmp["Kapitel"].astype(int) + (rng.random(len(tmp)) - 0.5) * 0.12
|
|
||||||
|
|
||||||
# Clusterstärken (Mittelwert der Effektstärke im jeweiligen Clusterzentrum)
|
# Clusterstärken (Mittelwert der Effektstärke im jeweiligen Clusterzentrum)
|
||||||
cluster_strengths = {i: float(model.cluster_centers_[i][0]) for i in range(len(model.cluster_centers_))}
|
cluster_strengths = {i: float(model.cluster_centers_[i][0]) for i in range(len(model.cluster_centers_))}
|
||||||
tmp["Clusterstärke"] = tmp["Cluster"].map(cluster_strengths)
|
tmp["Clusterstärke"] = tmp["Cluster"].map(cluster_strengths)
|
||||||
|
|
||||||
|
# Cluster-Reihenfolge: absteigend nach Ø d
|
||||||
|
clusters_sorted = sorted(tmp["Cluster"].unique(), key=lambda c: cluster_strengths[c], reverse=True)
|
||||||
|
|
||||||
|
# Gewünschte Markerpalette (robust mit Fallbacks)
|
||||||
|
def _get_marker(*candidates):
|
||||||
|
for key in candidates:
|
||||||
|
if key in styles:
|
||||||
|
return styles[key]
|
||||||
|
return styles.get("marker_accent", {})
|
||||||
|
|
||||||
|
palette_markers = [
|
||||||
|
_get_marker("marker_positiveHighlight", "marker_brightArea", "marker_accent"),
|
||||||
|
_get_marker("marker_primaryLine", "marker_brightArea", "marker_accent"),
|
||||||
|
_get_marker("marker_secondaryLine", "marker_accent", "marker_brightArea"),
|
||||||
|
_get_marker("marker_negativeHighlight", "marker_accent", "marker_brightArea"),
|
||||||
|
]
|
||||||
|
|
||||||
|
# x-Positionen so vergeben, dass Cluster-Blöcke entstehen
|
||||||
|
tmp = tmp.reset_index(drop=True)
|
||||||
|
tmp["_x"] = np.nan
|
||||||
|
x_cursor = 0
|
||||||
|
block_bounds = {} # für Centroid-Linien (x-Min/x-Max je Cluster)
|
||||||
|
|
||||||
|
for c in clusters_sorted:
|
||||||
|
sub_idx = tmp.index[tmp["Cluster"] == c].tolist()
|
||||||
|
n = len(sub_idx)
|
||||||
|
xs = np.arange(x_cursor, x_cursor + n)
|
||||||
|
tmp.loc[sub_idx, "_x"] = xs
|
||||||
|
block_bounds[c] = (xs.min(), xs.max())
|
||||||
|
x_cursor += n + 2 # +2 als optischer Abstand zwischen Blöcken
|
||||||
|
|
||||||
hovertemplate = (
|
hovertemplate = (
|
||||||
"Thermometer: %{customdata[2]}<br>"
|
"Thermometer: %{customdata[2]}<br>"
|
||||||
"Stichwort: %{text}<br>"
|
"Stichwort: %{text}<br>"
|
||||||
"Effektstärke: %{y:.2f}<br>"
|
"Effektstärke: %{y:.2f}<br>"
|
||||||
"Kapitel: %{customdata[0]}<br>"
|
"Kapitel: %{customdata[0]}<br>"
|
||||||
"Clusterstärke: %{customdata[1]:.3f}<extra></extra>"
|
"Clusterstärke: %{customdata[1]:.2f}<extra></extra>"
|
||||||
)
|
)
|
||||||
|
|
||||||
fig = go.Figure()
|
fig = go.Figure()
|
||||||
clusters = sorted(tmp["Cluster"].unique())
|
|
||||||
palette_keys = ["positiveHighlight", "negativeHighlight", "accent", "brightArea"]
|
|
||||||
|
|
||||||
for idx, cluster in enumerate(clusters):
|
# Punkte je Cluster zeichnen
|
||||||
cluster_df = tmp[tmp["Cluster"] == cluster]
|
for idx, c in enumerate(clusters_sorted):
|
||||||
color_key = palette_keys[idx % len(palette_keys)]
|
cdf = tmp[tmp["Cluster"] == c]
|
||||||
fig.add_trace(go.Scatter(
|
fig.add_trace(go.Scatter(
|
||||||
x=cluster_df["_kapitel_x"],
|
x=cdf["_x"],
|
||||||
y=cluster_df["Effektstärke"],
|
y=cdf["Effektstärke"],
|
||||||
mode="markers",
|
mode="markers",
|
||||||
marker={**styles[f"marker_{color_key}"], "size": 10},
|
marker={**palette_markers[idx % len(palette_markers)], "size": 10},
|
||||||
name=f"Cluster: {cluster_strengths[cluster]:.2f}",
|
name=f"Cluster: {cluster_strengths[c]:.2f}",
|
||||||
text=cluster_df["Stichwort"],
|
text=cdf["Stichwort"],
|
||||||
customdata=np.stack([cluster_df["Kapitelname"], cluster_df["Clusterstärke"], cluster_df["Thermometer_ID"]], axis=-1),
|
customdata=np.stack([cdf["Kapitelname"], cdf["Clusterstärke"], cdf["Thermometer_ID"]], axis=-1),
|
||||||
hovertemplate=hovertemplate
|
hovertemplate=hovertemplate
|
||||||
))
|
))
|
||||||
|
|
||||||
fig.update_layout(plotly_template.get_standard_layout(
|
# Centroid-Linien (horizontale Ø d pro Cluster)
|
||||||
f"Effektstärke × Cluster ({title_suffix}) ({kapitel_label}) – Silhouette: {sil:.3f}", "Kapitel", "Cohen d"
|
for c in clusters_sorted:
|
||||||
|
x0, x1 = block_bounds[c]
|
||||||
|
yd = cluster_strengths[c]
|
||||||
|
centroid_color = colors.get("depthArea", "#444")
|
||||||
|
line_style = dict(styles.get("linie_secondaryLine", {"width": 2}))
|
||||||
|
line_style["color"] = centroid_color
|
||||||
|
fig.add_trace(go.Scatter(
|
||||||
|
x=[x0, x1],
|
||||||
|
y=[yd, yd],
|
||||||
|
mode="lines",
|
||||||
|
line=line_style,
|
||||||
|
name=None,
|
||||||
|
showlegend=False,
|
||||||
|
hovertemplate=f"Cluster-Mittel: {yd:.2f}<extra></extra>"
|
||||||
))
|
))
|
||||||
# Ganze Zahlen auf der x‑Achse (Kapitel)
|
|
||||||
fig.update_layout(xaxis=dict(tickmode="linear", dtick=1))
|
# Vertikale Trennlinien zwischen Cluster-Blöcken (zur Orientierung)
|
||||||
|
# (nur als dezente Linien, keine Legende)
|
||||||
|
block_edges = sorted({bounds[1] + 1 for bounds in block_bounds.values()})
|
||||||
|
for edge in block_edges[:-1]: # letzte Kante führt bereits zum Abstand
|
||||||
|
fig.add_vline(x=edge - 1, line=dict(color=colors.get("depthArea"), width=1, dash="dot"))
|
||||||
|
|
||||||
|
fig.update_layout(plotly_template.get_standard_layout(
|
||||||
|
f"Effektstärke × Cluster ({title_suffix}) ({kapitel_label}) – Silhouette: {sil:.3f}",
|
||||||
|
"Thermometer (gruppiert nach Cluster)", "Cohen d"
|
||||||
|
))
|
||||||
|
fig.update_xaxes(showticklabels=False)
|
||||||
|
|
||||||
fig.show()
|
fig.show()
|
||||||
export_figure(fig, f"vl-scatter-{title_suffix}", export_fig_visual, export_fig_png)
|
export_figure(fig, f"vl-scatter-{title_suffix}", export_fig_visual, export_fig_png)
|
||||||
|
|
||||||
@ -757,12 +840,13 @@ def plot_scatter_3d(df: pd.DataFrame, cluster_labels: np.ndarray, sil: float, ti
|
|||||||
for idx, cluster in enumerate(clusters):
|
for idx, cluster in enumerate(clusters):
|
||||||
cluster_df = tmp[tmp["Cluster"] == cluster]
|
cluster_df = tmp[tmp["Cluster"] == cluster]
|
||||||
color_key = palette_keys[idx % len(palette_keys)]
|
color_key = palette_keys[idx % len(palette_keys)]
|
||||||
|
marker_style = styles.get(f"marker_{color_key}", {})
|
||||||
fig.add_trace(go.Scatter3d(
|
fig.add_trace(go.Scatter3d(
|
||||||
x=cluster_df["Effektstärke"],
|
x=cluster_df["Effektstärke"],
|
||||||
y=cluster_df["Kapitel"],
|
y=cluster_df["Kapitel"],
|
||||||
z=cluster_df["Text_Dimension"],
|
z=cluster_df["Text_Dimension"],
|
||||||
mode="markers",
|
mode="markers",
|
||||||
marker={**styles[f"marker_{color_key}"], "size": 6},
|
marker={**marker_style, "size": 6},
|
||||||
name=f"Cluster {cluster} (Ø d = {cluster_strengths[cluster]:.2f})",
|
name=f"Cluster {cluster} (Ø d = {cluster_strengths[cluster]:.2f})",
|
||||||
text=cluster_df["Stichwort"],
|
text=cluster_df["Stichwort"],
|
||||||
customdata=np.stack([cluster_df["Kapitelname"], cluster_df["Cluster"]], axis=-1),
|
customdata=np.stack([cluster_df["Kapitelname"], cluster_df["Cluster"]], axis=-1),
|
||||||
@ -807,10 +891,12 @@ def analyse(csv_path: str = "Thermometer.csv", k: int = 4, kapitel: int | None =
|
|||||||
df = add_manual_bins(df)
|
df = add_manual_bins(df)
|
||||||
|
|
||||||
# K-Means
|
# K-Means
|
||||||
labels, sil, model = run_kmeans(df, k=k)
|
# Kapitelgewicht = 0.0 => Kapitel-OHE trägt nicht zur Distanz bei (kapitelübergreifendes Clustering)
|
||||||
|
labels, sil, model = run_kmeans(df, k=k, kapitel_weight=0.0)
|
||||||
# Silhouette je Punkt anhängen
|
# Silhouette je Punkt anhängen
|
||||||
try:
|
try:
|
||||||
X_for_sil, _ = encode_features(df)
|
X_for_sil, _ = encode_features(df, kapitel_weight=0.0)
|
||||||
|
X_for_sil = _sanitize_X(X_for_sil, clip=1e6)
|
||||||
if k > 1 and len(df) > k:
|
if k > 1 and len(df) > k:
|
||||||
df["Silhouette_point"] = silhouette_samples(X_for_sil, labels)
|
df["Silhouette_point"] = silhouette_samples(X_for_sil, labels)
|
||||||
else:
|
else:
|
||||||
@ -852,7 +938,7 @@ def analyse(csv_path: str = "Thermometer.csv", k: int = 4, kapitel: int | None =
|
|||||||
text_vs_effect(df)
|
text_vs_effect(df)
|
||||||
if kapitel is None:
|
if kapitel is None:
|
||||||
chi2_bins_kapitel(df)
|
chi2_bins_kapitel(df)
|
||||||
cluster_diagnostics(df)
|
cluster_diagnostics(df, kapitel_weight=0.0)
|
||||||
profiles_df = cluster_profiles(df, labels)
|
profiles_df = cluster_profiles(df, labels)
|
||||||
try:
|
try:
|
||||||
export_json(json.loads(profiles_df.to_json(orient="table")), "cluster_profile.json")
|
export_json(json.loads(profiles_df.to_json(orient="table")), "cluster_profile.json")
|
||||||
@ -966,7 +1052,8 @@ def analyse(csv_path: str = "Thermometer.csv", k: int = 4, kapitel: int | None =
|
|||||||
plot_scatter(df, labels, model, sil, title_suffix=f"k{k}", kapitel=kapitel)
|
plot_scatter(df, labels, model, sil, title_suffix=f"k{k}", kapitel=kapitel)
|
||||||
|
|
||||||
# 3D-Clustering
|
# 3D-Clustering
|
||||||
X3d, _ = encode_features_3d(df)
|
X3d, _ = encode_features_3d(df, kapitel_weight=0.0)
|
||||||
|
X3d = _sanitize_X(X3d, clip=1e6)
|
||||||
model3d = KMeans(n_clusters=k, n_init=20, random_state=42)
|
model3d = KMeans(n_clusters=k, n_init=20, random_state=42)
|
||||||
labels3d = model3d.fit_predict(X3d)
|
labels3d = model3d.fit_predict(X3d)
|
||||||
sil3d = silhouette_score(X3d, labels3d) if k > 1 and len(df) > k else np.nan
|
sil3d = silhouette_score(X3d, labels3d) if k > 1 and len(df) > k else np.nan
|
||||||
|
|||||||
Reference in New Issue
Block a user