1
vote
1answer
254 views

How to identify outliers for PCA

I want to conduct a PCA in SPSS. One assumption for PCA is that there are no significant outliers. How can I identify outliers in SPSS?
1
vote
2answers
145 views

Help translating the following PCA results into layman's terms?

As a non-maths doctoral candidate, I would be absolutely thrilled to bits if anyone here might be willing to take a stab at translating the following PCA results into layman's terms. There are only ...
0
votes
0answers
108 views

more variables than cases in PCA/factor analysis

I'm trying to run a PCA on 15 variables for which I have a sample size of 10 (i.e. 15 columns x 10 rows, all continuous data). I have two problems: 1) more variables than cases and 2) some highly ...
0
votes
0answers
93 views

Factor analysis

I did a principal component factor analysis with varimax rotation in SPSS. 69 variables were reduced to 12 components, but 2 components are blank and without any variable. Namely: all of my variables ...
3
votes
1answer
1k views

Categorical Principal Component Analysis - using Count, Continuous, Ordinal variables together

I have some variables and I want to reduce their number for further analysis. I initially thought of combining them using factor analysis. But since the variables are of all kinds (rating, count, ...
1
vote
1answer
938 views

About the usage of CATPCA

I have some questions regarding the usage of CATPCA in SPSS. I hope the answers to these questions will be helpful for others too who are planning to work on it. 1) In my data (on this data I want to ...
0
votes
0answers
117 views

Need help trying to do PCA and then Gower Similarity

I have a dataset and i have reduced it using PCA, but once i reduce the dimension of the dataset, i lose all my uniqe columns in the data. Which i think is what should happen. But i want to run gower ...
3
votes
1answer
1k views

Can I somehow compute variance explained by PC after Oblique rotation in PCA?

Let´s say that my PCA analysis extracted 2 components, which explain 80% of the variance before rotation. The components were then rotated using oblique (Direct Oblimin) rotation, so SPSS cannot ...
1
vote
1answer
256 views

Problem with Developing a Model for an Exploratory Study (PCA EFA)

*Background:* I am doing an academic study that is more exploratory in nature (my advisor did not want me to develop any hypothesis). So I read books and conducted interviews to try and gather a large ...
2
votes
2answers
1k views

Where can I find information about using SPSS for EFA and CFA? Is PCA (two samples) and reliability sufficient for scale development?

Context: I am in the process of developing a scale for my thesis. My advisor has guided me to using SPSS PCA to complete my analyses. Initially we reduced my scale to 3 factors (her insistence), ...
6
votes
1answer
6k views

How to perform principal components analysis on binary data (using SPSS)?

I have a dataset with a large number of Yes/No responses. Can I use principal components (PCA) or any other data reduction analyses (such as factor analysis) for this type of data? Please advise how I ...
4
votes
1answer
1k views

Calculating principal component scores after PC analysis

I am carrying out a study to find out meteorological patterns using daily met observations including around 30 met parameters (each day is a case with 30 variables). My methodology includes carrying ...
1
vote
2answers
554 views

How to understand optimal Scaling in R: The Package homals for novices

Does anyone know of a step-by-step guide for the practical implementation of Gifi Methods for Optimal Scaling in R: The Package homals? Although I have an OK theoretical understanding (thanks chl for ...
9
votes
4answers
3k views

is psych::principal function still PCA when using rotation?

I have tried to reproduce some research (using PCA) from SPSS in R. In my experience, principal() function from package psych ...