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ARM B application lecture factor analysis notes
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Lecture notes from the application lecture on factor analysis. Written in 2024, mostly in English.
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Advanced Research Methods part B – Application Lecture Factor AnalysisPPT1
Topics
1. Start:
a. Research question
b. Analyses strategy: univariate/bivariate analysis, multivariate analysis
2. Basis:
a. Univariate/bivariate ( SPSS: descriptive statistics frequencies
statistics)
3. Factor Analyses 1-5 ( SPSS: data reduction factor)
a. Items: PC/PAF, communalities, extraction
b. Factors: number, rotation, interpretation
4. Reliability analysis ( SPSS: scale reliability analysis)
5. Conclusions:
a. Implications
b. Issues
Research question
To analyze the dimensional structure of loyalty:
- Theoretical model of Customer loyalty
- Focus on core dimensions of a concept (i.e. data summarization / reduction)
- Managerial implications for the Belgian bank
Analyses strategy following univariate analysis, Hair’s decision diagram:
- Research design: the objectives, assumptions of the analysis techniques
- Items / the rules of thumb, respondents
- Factor method, rotation, interpretation, factor scores
- Validation
Univariate Analysis (based on N = 410)
Checks:
- Metric indicators / items, normal distributions
- Mean een gemiddelde van 4.7 op een 7-punts Likert schaal betekent dat de items
hoger dan gemiddeld worden beoordeeld
- SD de SD’s van de items moeten allemaal een beetje hetzelfde zijn. Als dit niet zo
is, is het item niet duidelijk geformuleerd voor de respondent
- Skewness = linkerkant/rechterkant. 2x de SD hiervan toont de rechterkant aan van
het interval
o De skewness moet binnen -3 en +3 liggen
- Kurtosis = piek/dal
- Valid values
o Defining a missing value in SPSS
- In the data editor
- Using SPSS-commands
SPSS Descriptive statistics frequencies statistics
Missing values in Multivariate Analysis
- 13 variables each with 10 cases having missing values can result in 130 missing
cases in the analysis. Probeer te begrijpen waarom er missings zijn. Misschien zijn de
vragen niet goed geformuleerd? Misschien is de vraag niet toepasbaar in een
bepaalde context?
- < 10% missings: assume representativity
1
, Factor Analysis I
Correlation matrix = je kunt hier de correlaties zien tussen de items
KMO = vergelijkt de correlaties uit de correlatiematrix met de partiële correlaties.
Factoranalyse geschikt indien waarde > 0.50
Barlett’s test of sphericity = nulhypothese: alle correlaties zijn 0 (net zoals de identity matrix).
Factoranalyse geschikt indien H0 verworpen kan worden, ofwel een significante waarde voor
X2
Some basics of explorative FA, the factor matrix
Factor loadings = hoe een bepaald item op een factor laadt
Communality = squared loadings voor alle factors (het liefst hoog)
Eigenvalue = squared loadings voor alle items (het liefst hoog)
Explained variance = het liefst boven .50/.60
Factor Analysis: EXTRACTION. Principal Component Analysis
SPSS doet automatisch principal component analysis. Factor * factor loadings. Het gaat
ervan uit dat er geen sprake is van een measurement error.
Factor Analysis: EXTRACTION. Principal Axis Factoring
Common item variance = de variantie die gedeeld is met andere items. Zonder de specifieke
variantie per item. Soms is dit goed. Soms is dit niet goed, omdat je de specifieke meaning
van de items verwijdert.
2
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