Analytics Grundlagen: Durchführung statistischer Untersuchungen

Statistik

Prof. Dr. Jan Kirenz

Durchführung statistischer Untersuchungen

Photo Credit: Unsplash, Roman Mager

ANALYTICS Grundlagen


“All people decisions should be informed bydata and analytics.” ~Google People Analytics motto

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Weshalb ist Statistik wichtig?

Die Statistik soll insbesondere dabei unterstützen, optimale Entscheidungen zu treffen.

Sie unterstützt uns dabei bspw. mit Methoden zur Auswertung von Daten

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Übergeordnete Zielsetzung



Werbung von Parship:

Alle 11 Minuten verliebt sich ein Single über PARSHIP.

Frage: sind das gute Nachrichten für Singles bei Parship, wenn dort ca. 5 Mio. Mitglieder angemeldet sind?

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Korrekte Interpretation von Informationen



Antwort:

Eher nicht!

Die Erfolgswahrscheinlichkeit beträgt in einem Jahr nur 2%!

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Parship hatte zum Zeitpunkt der Werbung ca. 5 Mio. Mitglieder.

Wenn sich sogar alle 10 Minuten zwei davon ineinander verlieben, damit aus dem Sucherpool ausscheiden und durch zwei neue Singles ersetzt werden, beträgt für ein zufällig ausgewähltes Mitglied die Wahrscheinlichkeit einer neuen Liebe pro Jahr kaum mehr als 2%. Mit 98% Wahrscheinlichkeit findet man also keinen Partner!

Erklärung:Wenn sich alle 10 Minuten zwei Singles verlieben, dann passiert das 6-mal in der Stunde, 144-mal am Tag oder 52.560-mal im Jahr.

Die Wahrscheinlichkeit, an einem beliebigen dieser 52.560 Zeitspannen von 10 Minuten Erfolg zu haben, beträgt 2 zu 5.000.000 (die anderen 4.999.998 suchen weiter).

Damit ist die Wahrscheinlichkeit für Misserfolg an jedem beliebigen Zeitpunkt 4.999.998 zu 5.000.000, oder über das Jahr gerechnet (4.999.998/5.000.000)^52.560 = 0,979.

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Quelle: Bauer, T. (2015). Liebestrunken – Vermittlungsbörse schießt statistisches Eigentor. Eingesehen am 25.07.2016 https://www.harding-center.mpg.de/de/unstatistik/liebestrunken-vermittlungsboerse-schiesst-statistisches-eigentor



Annähernd 7,3 Milliarden Euro haben die Deutschen im vergangenen Jahr für 6 aus 49, Glücksspirale oder Super 6 ausgegeben, etwa 90 Euro pro Kopf.

Frage: Wie wahrscheinlich ist es, den Jackpot zu „knacken“?

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Quelle: Süddeutsche Zeitung (2016). Die Deutschen und ihr Lotto - eine gestörte Beziehung. Zugriff am 27.07.2016 unter http://www.sueddeutsche.de/wirtschaft/die-deutschen-und-ihr-lotto-ohne-gewaehr-1.2922068

Bildquelle: picture alliance / dpa



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Quelle: Lotto (2016). Gewinnwahrscheinlichkeiten. Zugriff am 17.07.16 unter https://www.lotto.de/de/informationen/lotto-6aus49/gewinnwahrscheinlichkeit.html

In der Gewinnklasse 1 beträgt die Chance rund 1:140 Millionen

D.h. die Chance zu gewinnen beträgt hier 0.000000714286%

Viel Glück!!


Was ist Statistik?

Statistik ist die Lehre von Methoden zum Umgang mit quantitativen Informationen (Daten).

Methode: Standardisiertes Verfahren zur Gewinnung von Erkenntnissen

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Allgemeine Definition

Quelle: Rinne, H. (2008). Taschenbuch der Statistik (4. Auflage). Frankfurt am Main: Harri Deutsch Verlag,


Was ist Statistik?

Statistik unterstützt uns bei der systematischen Gewinnung und Analyse von Daten

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Zentrale Inhalte der Statistik


Was ist Statistik?

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Vorgehensweise bei Google:


Was ist Statistik?

1. Definition „welche Frage soll beantwortet werden?“

2. Design „Von welcher Personengruppe benötigen wir welche Daten?“

3. Datenerhebung „Wie sollen die Daten erhoben werden?“

4. Datenanalyse “Welche Analysen werden benötigt und welche Ergebnisdarstellung ist geeignet)?“

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Vorgehen bei der Durchführung von statistischen Untersuchungen (z.B. auch bei einer empirischen Bachelorarbeit)


Durchführung einer statistischen Untersuchung




4. Datenanalyse “Welche Analysen und Visualisierungen werden benötigt?“

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Definition, Design, Datenerhebung, Datenanalyse



Schritt 1: Beschreibung einer konkreten Fragestellung (d.h. Forschungsfrage), die beantwortet werden soll.

Beispiele:Wie können YouTuber die Anzahl ihrer Follower erhöhen?Welche Form der Arbeit motiviert die Generation Y in westlichen Ländern?Was macht Menschen in Deutschland zufrieden?...

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Definition


Example

Research question: Why do some work groups thrive and others falter?

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Duhigg, C. (2016). What Google learned from its Quest to Build the Perfect Team. New York Times, http://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html?_r=0

Picture Copyright: Charles Graham



Schritt 2: Recherche des aktuellen(wissenschaftlichen) Wissensstands zu der vorliegenden Fragestellung.

Hinweise:Sie benötigen hierfür typischerweise Ergebnisse und Modelle von bereits durchgeführten Studien bzw. Forschungsarbeiten. Nutzen Sie beispielsweise folgende Recherchemöglichkeiten, um relevante Arbeiten ausfindig zu machen:Google Scholar: https://scholar.google.de

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Definition



Hinweis: JournalrankingsJournals werden nach unterschiedlichen Kriterien bewertet. Die Bewertung soll Aufschluss über die wissenschaftliche Qualität der darin veröffentlichen Beiträge liefern. Beispiele für Rankings:SJR : Scientific Journal Rankings : http://www.scimagojr.com/journalrank.php?area=1400

VHB-JOURQUAL3: http://vhbonline.org/vhb4you/jourqual/vhb-jourqual-3/teilrating-abwl/

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Definition


Journalranking

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Journalranking

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What Google LearnedFrom Its Quest toBuild the Perfect Team

Google’s researchers began by reviewing a half-century ofacademic studies looking at how teams worked. Were the best teams made up of people with similar interests? Or did it matter more whether everyone was motivated by thesame kinds of rewards?

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New research reveals surprising truths aboutwhy some work groups thrive and others falter

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‘‘We have used the statistical approach they developed for individual intelligence tosystematically measure the intelligence of groups.’’ Put differently, the researchers wanted to know if there is a collective I. Q. thatemerges within a team that is distinct from the smarts of any single member.

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Woolley, A. W. et al. (2010). Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science, 330, 668

(7): People who dowell on onemental task tend todo well on most others, despite large variations inthe tests’ contents and methods of administration(4). In principle, performance on cognitive taskscould be largely uncorrelated, as one might expectif each relied on a specific set of capacities thatwas not used by other tasks (8). It could even benegatively correlated, if practicing to improve onetask caused neglect of others (9). The empiricalfact of general cognitive ability as first demon-strated by Spearman is now, arguably, the mostreplicated result in all of psychology (4).

Evidence of general intelligence comes fromthe observation that the average correlation amongindividuals’ performance scores on a relativelydiverse set of cognitive tasks is positive, the firstfactor extracted in a factor analysis of these scoresgenerally accounts for 30 to 50% of the variance,and subsequent factors extracted account forsubstantially less variance. This first factor extractedin an analysis of individual intelligence tests isreferred to as general cognitive ability, or g, and itis the main factor that intelligence tests measure.What makes intelligence tests of substantial prac-tical (not just theoretical) importance is that in-telligence can be measured in an hour or less,and is a reliable predictor of a very wide rangeof important life outcomes over a long span oftime, including grades in school, success inmanyoccupations, and even life expectancy (4).

By analogy with individual intelligence, wedefine a group’s collective intelligence (c) as thegeneral ability of the group to perform a widevariety of tasks. Empirically, collective intelligenceis the inference one draws when the ability of agroup to perform one task is correlated with thatgroup’s ability to perform a wide range of othertasks. This kind of collective intelligence is a prop-erty of the group itself, not just the individuals in it.Unlike previous work that examined the effect ongroup performance of the average intelligence ofindividual groupmembers (10), one of our goals isto determine whether the collective intelligence ofthe group as a whole has predictive power aboveand beyond what can be explained by knowingthe abilities of the individual group members.

The first question we examined was whethercollective intelligence—in this sense—even exists.Is there a single factor for groups, a c factor, thatfunctions in the same way for groups as generalintelligence does for individuals? Or does groupperformance, instead, have some other correla-tional structure, such as several equally importantbut independent factors, as is typically found inresearch on individual personality (11)?

To answer this question, we randomly as-signed individuals to groups and asked them toperform a variety of different tasks (12). In Study1, 40 three-person groups worked together for upto 5 hours on a diverse set of simple group tasksplus a more complex criterion task. To guide ourtask sampling, we drew tasks from all quadrantsof the McGrath Task Circumplex (6, 12), a well-established taxonomy of group tasks based on thecoordination processes they require. Tasks in-cluded solving visual puzzles, brainstorming,making collective moral judgments, and negoti-ating over limited resources. At the beginning ofeach session, we measured team members’ indi-vidual intelligence. And, as a criterion task at theend of each session, each group played checkersagainst a standardized computer opponent.

The results support the hypothesis that ageneral collective intelligence factor (c) exists ingroups. First, the average inter-item correlationfor group scores on different tasks is positive (r =0.28) (Table 1). Next, factor analysis of teamscores yielded one factor with an initial eigen-value accounting for more than 43% of thevariance (in the middle of the 30 to 50% rangetypical in individual intelligence tests), whereasthe next factor accounted for only 18%. Confir-matory factor analysis supported the fit of asingle latent factor model with the data [c2 =1.66, P = 0.89, df = 5; comparative fit index(CFI) =.99, root mean square error of approxi-mation (RMSEA) = 0.01]. Furthermore, whenthe factor loadings for different tasks on the firstgeneral factor are used to calculate a c score foreach group, this score strongly predicts perform-ance on the criterion task (r = 0.52, P = 0.01).Finally, the average and maximum intelligence

scores of individual group members are notsignificantly correlated with c [r = 0.19, notsignificant (ns); r = 0.27, ns, respectively] andnot predictive of criterion task performance (r =0.18, ns; r = 0.13, ns, respectively). In a regres-sion using both individual intelligence and c topredict performance on the criterion task, c hasa significant effect (b = 0.51, P = 0.001), butaverage individual intelligence (b = 0.08, ns) andmaximum individual intelligence (b =.01, ns) donot (Fig. 1).

In Study 2, we used 152 groups ranging fromtwo to five members. Our goal was to replicatethese findings in groups of different sizes, using abroader sample of tasks and an alternative mea-sure of individual intelligence. As expected, thisstudy replicated the findings of Study 1, yieldinga first factor explaining 44% of the variance and asecond factor explaining only 20%. In addition, aconfirmatory factor analysis suggests an excel-lent fit of the single-factor model with the data(c2 = 5.85, P = 0.32, df = 5; CFI = 0.98, NFI =0.89, RMSEA = 0.03).

In addition, for a subset of the groups in Study2, we included five additional tasks, for a total often. The results from analyses incorporating allten tasks were also consistent with the hypothesisthat a general c factor exists (see Fig. 2). Thescree test (13) clearly suggests that a one-factormodel is the best fit for the data from both studies[Akaike Information Criterion (AIC) = 0.00 forsingle-factor solution]. Furthermore, parallel anal-ysis (13) suggests that only factors with an eigen-value above 1.38 should be retained, and there isonly one such factor in each sample. These conclu-sions are supported by examining the eigenvaluesboth before and after principal axis extraction,which yields a first factor explaining 31% of

Table 1. Correlations among group tasks and descriptive statistics for Study 1. n = 40 groups; *P ≤0.05; **P ≤ 0.001.

1 2 3 4 5 6 7 8 91 Collective intelligence (c)2 Brainstorming 0.38*3 Group matrix reasoning 0.86** 0.30*4 Group moral reasoning 0.42* 0.12 0.275 Plan shopping trip 0.66** 0.21 0.38* 0.186 Group typing 0.80** 0.13 0.50** 0.25* 0.43*7 Avg member intelligence 0.19 0.11 0.19 0.12 –0.06 0.228 Max member intelligence 0.27 0.09 0.33* 0.05 –0.04 0.28 0.73**9 Video game 0.52* 0.17 0.38* 0.37* 0.39* 0.44* 0.18 0.13Minimum –2.67 9 2 32 –10.80 148 4.00 8.00 26Maximum 1.56 55 17 81 82.40 1169 12.67 15.67 96Mean 0 28.33 11.05 57.35 46.92 596.13 8.92 11.67 61.80SD 1.00 11.36 3.02 10.96 19.64 263.74 1.82 1.69 17.56

Fig. 1. Standardized regression coefficients forcollective intelligence (c) and average individualmember intelligence when both are regressed to-gether on criterion task performance in Studies1 and 2 (controlling for group size in Study 2).Coefficient for maximum member intelligence isalso shown for comparison, calculated in a separateregression because it is too highly correlated withindividual member intelligence to incorporate bothin a single analysis (r = 0.73 and 0.62 in Studies1 and 2, respectively). Error bars, mean T SE.

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In their literature research, the Google People Analytics team discovered this studyabout collective intelligence


l This paper presents a model of team learning and tests it in a multimethod fieldstudy.

l It introduces the construct of team psychological safety:- a shared belief held by members of a team that the team is safe for

interpersonal risktaking and models the effects of team psychological safetyand team efficacy together on learning and performance in organizationalwork teams.

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Source: Edmondson, A. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, Vol. 44, No. 2, pp. 350-383 Link to study: http://web.mit.edu/curhan/www/docs/Articles/15341_Readings/Group_Performance/Edmondson%20Psychological%20safety.pdf

...the Google People Analytics team also discovered thisstudy aboutpsychologicalsafety...

Psychological Safety

Team psychological safety thus serves as a mechanism translating structural features into behavioral outcomes: Hypothesis 7 (H7): Team psychological safety mediates between the antecedents of team leader coaching and context support and the outcome of team learning behavior. Context support and team leader coaching should also affect team efficacy. Effective coaching is likely to contribute to members' confidence in the team's ability to do its job, as is a supportive context, which reduces obstacles to progress and allows team members to feel confident about their chances of success. If coaching and context support pro- mote team efficacy and team efficacy promotes team learning, this suggests that team efficacy also functions as a me- diator: Hypothesis 8 (H8): Team efficacy mediates between the antecedents of team leader coaching and context support and the outcome of team learning behavior.

Team Type

Organizations use a variety of types of teams. Team type varies across several dimensions, including cross-functional versus single-function, time-limited versus enduring, and manager-led versus self-led. These dimensions combine to form different types of teams, such as a time-limited new product development team or an ongoing self-directed production team. The team learning model should be applicable across multiple types of teams, because the social psychological mechanism at the core of the model concerns people taking action in the presence of others, and the salience of interpersonal threat should hold across settings. Therefore, although the utility of learning behavior may vary across types of teams, the association between team psychological safety and team learning behavior should apply across different team types. Thus, the effects of team type on learning behavior should be insignificant when assessed together with the other variables in the team learning model, shown in figure 1. For example, new product development teams might be expected to exhibit more learning behavior than production teams because of the nature of their task; none- theless, mean differences in learning behavior that might be observed across types of teams should be explained by team psychological safety and team efficacy, as shown in figure 1, rather than by team type.

Figure 1. A model of work-team learning.

Antecedent Tea m Team Conditions Beliefs Behaviors Outcomes

TEAM LEARNING TEAM BEHAVIOR

STRUCTURES TEAM PERFORMANCE

TEAM SAFETY Seeking feedback, Context support TEAM EFFICACY discussing errors,

seeking information Satisfies customer needs Team leader and feedback from and expectations

coaching customers and others

357/ASQ, June 1999


l Results of a study of 51 work teams in a manufacturing company, measuringantecedent, process, and outcome variables, show that team psychologicalsafety is associated with learning behavior.

l The results support an integrative perspective in which both team structures, such as context support and team leader coaching, and shared beliefs shape teamoutcomes.

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Source: Edmondson, A. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, Vol. 44, No. 2, pp. 350-383 Link to study: http://web.mit.edu/curhan/www/docs/Articles/15341_Readings/Group_Performance/Edmondson%20Psychological%20safety.pdf

...the Google People Analytics team also discovered thisstudy aboutpsychologicalsafety...

Psychological Safety

Team psychological safety thus serves as a mechanism translating structural features into behavioral outcomes: Hypothesis 7 (H7): Team psychological safety mediates between the antecedents of team leader coaching and context support and the outcome of team learning behavior. Context support and team leader coaching should also affect team efficacy. Effective coaching is likely to contribute to members' confidence in the team's ability to do its job, as is a supportive context, which reduces obstacles to progress and allows team members to feel confident about their chances of success. If coaching and context support pro- mote team efficacy and team efficacy promotes team learning, this suggests that team efficacy also functions as a me- diator: Hypothesis 8 (H8): Team efficacy mediates between the antecedents of team leader coaching and context support and the outcome of team learning behavior.

Team Type

Organizations use a variety of types of teams. Team type varies across several dimensions, including cross-functional versus single-function, time-limited versus enduring, and manager-led versus self-led. These dimensions combine to form different types of teams, such as a time-limited new product development team or an ongoing self-directed production team. The team learning model should be applicable across multiple types of teams, because the social psychological mechanism at the core of the model concerns people taking action in the presence of others, and the salience of interpersonal threat should hold across settings. Therefore, although the utility of learning behavior may vary across types of teams, the association between team psychological safety and team learning behavior should apply across different team types. Thus, the effects of team type on learning behavior should be insignificant when assessed together with the other variables in the team learning model, shown in figure 1. For example, new product development teams might be expected to exhibit more learning behavior than production teams because of the nature of their task; none- theless, mean differences in learning behavior that might be observed across types of teams should be explained by team psychological safety and team efficacy, as shown in figure 1, rather than by team type.

Figure 1. A model of work-team learning.

Antecedent Tea m Team Conditions Beliefs Behaviors Outcomes

TEAM LEARNING TEAM BEHAVIOR

STRUCTURES TEAM PERFORMANCE

TEAM SAFETY Seeking feedback, Context support TEAM EFFICACY discussing errors,

seeking information Satisfies customer needs Team leader and feedback from and expectations

coaching customers and others

357/ASQ, June 1999



Schritt 3: Definition von Hypothesen, die zur Beantwortung der Fragestellung beitragen sollen

Hypothesen sind Aussagen, die einen Zusammenhang zwischen mindestens zwei Merkmalen (Variablen) herstellen.

Beispiele:- Variable A hat einen positiven Einfluss auf Variable B.- Je größer Variable A, desto kleiner Variable B- ...

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Definition



Based on different studies, the researchers formulatedhypotheses about different factors...

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Hypothesen

Fiktive Beispiele:- Empathische Führungskräfte erhöhen die

Gruppenintelligenz- Je sicherer sich die Mitarbeiter in einem Team

fühlen, desto höher ist die Gruppenintelligenz...

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Definition







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Schritt 1: Ausgehend von den Hypothesen werden die benötigten Merkmale (Variablen) und Merkmalsausprägungen definiert, die zum Testen der Hypothesen notwendig sind (Operationalisierung).

Üblicherweise wird ein Fragebogen erstellt, der alle relevanten Merkmale enthält (dies wird auch Operationalisierung der Hypothesen genannt). Geeignete Messinstrumente können oftmals von bereits publizierten Arbeiten übernommen werden.

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Design



Operationalisierung

- Wir benötigen geeignete „Messinstrumente“ für:- „Psychological Safety“ - Gruppenintelligenz - Erfolg/Misserfolg eines Teams- ...

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Design



Schritt 2: Definition der Personengruppe (Grundgesamtheit), auf welche sich die Hypothesen beziehen.

Unter Grundgesamtheit versteht man die Gesamtmenge aller Personen, über die eine Aussage getroffen werden soll.

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Design



Grundgesamtheit

- Alle Teams, die bei Google arbeiten

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Design



Schritt 3: Auswahl einer Stichprobe (mit Hilfe eines geeigneten Auswahlverfahrens), die stellvertretend für die Grundgesamtheit untersucht werden soll.

Beispiele:Eine Stichprobe ist eine Auswahl an Personen, die stellvertretend für eine Grundgesamtheit Auskunft gibt.Eine Stichprobenbefragung (Teilerhebung) ist nur dann zulässig, wenn die Struktur der Stichprobe (z.B. prozentualer Anteil an Personen mit einem bestimmten: Geschlecht, Bildungsabschluss, Alter, Einkommen …) die Struktur der Grundgesamtheit widerspiegelt. Falls die Strukturen identisch sind, spricht man von einer repräsentativen Stichprobe.

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Design



Stichprobe

- Auswahl von insgesamt 180 verschiedenen Teams aus unterschiedlichen Bereichen des Unternehmens

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Design



Schritt 4: Festlegung, ob eine Längsschnittstudie(Erhebung zu mehreren Zeitpunkten) oder Querschnittstudie (Erhebung an nur einem Zeitpunkt) durchgeführt werden soll

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Design



Schritt 5: Insgesamt muss sichergestellt werden, dass die Untersuchung den Gütekriterienquantitativer Untersuchungen genügt (Validität, Reliabilität und Objektivität)

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Design







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Schritt 1: Die Erhebung der Daten kann auf verschiedenen Arten erfolgen:

Primärerhebung: Durchführung einer neuen Befragung. Beispielsweise schriftlich (offline oder online), telefonisch oder in einem persönlichen Interview

Sekundärerhebung: Nutzung von bereits vorhandenen Daten. Beispielsweise Download von frei zugänglichen Daten aus dem World Wide Web

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Datenerhebung



Datenerhebung

- Insbesondere Durchführung von quantitativen Befragungen mit Fragebögen (Primärerhebung)

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Design



Wegen der relativ unkomplizierten Erhebung wird bei Primärerhebungen als Erhebungsverfahren häufig ein Onlinefragebogen eingesetzt

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Datenerhebung



Schritt 2: Bei der Erhebung muss beachtet werden, dass die Stichprobe ein repräsentatives Abbildder Grundgesamtheit darstellt.

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Datenerhebung







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Schritt 1: Die Analyse der Daten beginnt zunächst mit der Datenbereinigung.

Dabei werden zunächst unter anderem fehlerhafte und unvollständige Daten in den Datensätzen identifiziert und ggf. korrigiert

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Datenanalyse



Schritt 2: Die Daten aus der Stichprobe werden mit Hilfe der deskriptiven Statistik (Beschreibung der Stichprobenergebnisse) analysiert.

Hierbei werden zentrale Tendenzen (Durchschnittswerte) und die Streuung (d.h. Verteilung) der Daten ermittelt.

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Datenanalyse



Schritt 3: Untersuchung, ob Befunde (zu Hypothesen) aus der Stichprobe auf die zugehörige Grundgesamtheit übertragen werden können (auch Inferenzstatistik genannt)

Dependenzanalysen: In Abhängigkeit von den Hypothesen können Zusammenhänge (z.B. Korrelation, Regression) und/oder Unterschiede (z.B. t-Test, U-Test, …) untersucht werden.Interdependenzanalysen: Strukturen in Daten (z.B. Conjoint-Analyse, Clusteranalyse,…) untersuchten.

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Datenanalyse



Schritt 4: Die Datenvisualisierung soll die Ergebnisse möglichst verständlich darstellen, um so die Entscheidungsfindung zu vereinfachen.

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Datenanalyse



One key-finding: ‘As long as everyone got a chance to talk, the team did well. But ifonly one person or a small group spoke all the time, the collective intelligencedeclined.

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Duhigg, C. (2016). What Google learned from ist Quest to Build the Perfect Team. New York Times, http://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html?_r=0

ICopyright: Charles Graham

Prof. Dr. Jan Kirenz 48


What Google LearnedFrom Its Quest toBuild the Perfect Teaml Google now describes „psychological safety“,a model of

teamwork in which members have a shared belief that it is safe totake risks and share a range of ideas without the fear of beinghumiliated, as the most important factor to building a successfulteam.

l In short... just be polite

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Duhigg, C. (2016). What Google learned from ist Quest to Build the Perfect Team. New York Times, http://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html?_r=0

ICopyright: Charles Graham

Analytics Grundlagen: Durchführung statistischer Untersuchungen

Data & Analytics

Transcript of Analytics Grundlagen: Durchführung statistischer Untersuchungen