# Summary Discovering statistics using SPSS (and sex and drugs and rock'n'roll)

ISBN-10 1847879071 ISBN-13 9781847879073
331 Flashcards & Notes
70 Students

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• ## 1.4 Generating theories and testing them

• When is a hypothesis not a hypothesis?

A good theory should allow us to make statements about the state of the world. Scientific statements (hypothesis) are ones that can be verified with reference to empirical evidence, non-scientific statements can't be empirically tested. Good theories should produce hypotheses that are scientific statements.

• ## 1.5.1 Variables

• Independent variable: a variable that probably is known as a cause

• Dependent variable: a variable that is an effect, because it depends on the cause

• Predictor variable: a variable thought to predict an outcome variable. This is basically another term for independent variable.

• Outcome variable: a variable thought to change as a function of changes in predictor variable. This term could be called dependent variable.

• Categorial variables (entities are divided into distinct categories):

• Binary variable: There are only two categories (yes/no; female/male)
• Nominal variable: There are more than two categories
• Ordinal variable: The same as a nominal variable but the categories have a logical order
• Continuous variables (entities get a distinct score):

• Interval variable: equal intervals on the variable represent equal differences in the property being measured
• Ratio variable: The same as an interval variable, but the ratios of scores on the scale must also make sense and have a meaningful zero point
• Continuous variables: can be measured to any level of precision (0,01), but also discrete (1,2,3)

• ## 1.5.2 Measurement error

• Measurement error: discrepancy between the numbers we use to represent the thing we're measuring and the actual value of the thing we're measuring

• ## 1.5.3 Validity and reliability

• Validity: measures what it sets out to measure -> Test: Content validity; heb je gemeten wat je wilde meten?

Reliability: an instrument can be interpreted consistently across different situations -> Test: Test-retest reliability

• ## 1.6.1 Correlational Research Methods

• Correlational/ cross-sectional research:  a snapshot of many variables at a single point in time. It provides a very natural view of the question we're researching because we're not influencing what happens and the measures of the variables should not be biased by the researcher being there. Important aspect of ecological validity.

• Kritiek op correlational research:

• Het meten van meerdere variabelen tegelijk geeft geen informatie over de betrokkenheid tot elkaar.
• Tertium quid (a third person or thing of indeterminate character): het vergeten van een belangrijke factor een zogenaamde 'confounding variable'.
• Alle andere verklaringen voor het oorzaak-effect worden uitgesloten
• In correlational research we observe the co-occerence of variables.

• ## 1.6.2 Experimental research methods

• Experimental research: manipulating one variable to see its effect on another

• In experiments we manipulate the causal variable systematically to see its effect on an outcome (the effect).

• ## 1.6.2.1 Two methods of data collection

• Methode: Manipulate the independent variable using different participants. Experimental condition: between-groups, between-subjects, independent design.

• Methode: manipulate the independent variable using the same participants. Experimental condition: within-subject, repeated-measures design.

• ## 1.6.2.2 Two types of variation

• Systematic variation: This variation is due to the experimenter doing something to all of the participants in one condition but not in the other condition.

• Unsystematic variation: This variation results from random factors that exist between the experimental conditions.

• In a repeated-measures design, differences between two conditions can be caused by only two things:

1. manipulation that was carried out on the participants
2. any other factor that might affect the way in which a person performs from one time to the next
• In an independent design, differences between the two conditions can be caused by two things:

1. manipulation that was carried out on the participants
2. differences between the characteristics of the people allocated to each of the groups
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Wat is cronbach's alfa?
Om dat er meerdere manier zijn om data in tween op te splitsen is er kans dat de score een product is van de manier waarop deze gesplitst wordt.

Correlatie coefficient berekenen voor elke split. De gemiddelde van deze waarde is de cronbach alfa.
Wat is de makkelijkste methode om betrouwbaarheid van een test te meten?
Split-half methode->Splitst de schaal op in twee random geselecteerde sets van items hierop wordt een score per persoon berekend.
Welke test kan je gebruiken bij meer dan twee onafhankelijke groepen?
Kruskal wallis, test de hypothese dat meerdere onafhankelijke groepen van verschillende populaties komen.
Wanneer gebruik je Friedman's ANOVA?
Bij een repeated test
Waar moet je opletten bij interpreteren van kruskal wallis pairwise comparisons test?
Corrigeren voor het aantal testen wat je doet omdat alles met elkaar vergeleken wordt, staat in de tabel een gecorrigeerde p waarde.
Met welke test vergelijk je twee aan elkaar gerelateerde condities?
Wilcoxon signed rank-test.
Hoe rapporteer je de mann whitney test?
De u waarde(de uitkomst) en de significantie
Hoe bereken je de effectgrootte vanuit de z-score die spss berekent?
r=z/sqrt(N)
Wat is de asymptotische berekening? En de exacte methode?
Een benadering dat in grote samples een goed antwoord is. Maar bij kleine samples is deze niet voldoende en moet de exacte gebruikt worden.
Met welke twee testen kan je twee onafhankelijke condities vergelijken?
The wilcoxon rank-sum test
Mann-whitney test.

Dit zijn de non-parametrische tegenhangers van de t-test.