1. Using the rotated component matrix, answer the following questions.

a. How many factors (dimensions) did this analysis indicate for the concept of self-esteem?

b. What items should be in each one of the two dimensions of self-esteem?

2. What is the most important thing you learned in this class this semester? Why?

Factor Analysis

Communalities
		Initial		Extraction
Esteem1		1.000		.602
Esteem2		1.000		.692
Esteem3		1.000		.718
Esteem4		1.000		.644
Esteem5		1.000		.505
Esteem6		1.000		.710
Esteem7		1.000		.574
Esteem8		1.000		.687
Esteem9		1.000		.594
Esteem10		1.000		.587
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component	Initial Eigenvalues						Extraction Sums of Squared Loadings
	Total		% of Variance		Cumulative %		Total	% of Variance	Cumulative %
1	4.807		48.066		48.066		4.807	48.066	48.066
2	1.507		15.067		63.133		1.507	15.067	63.133
3	.760		7.605		70.737
4	.642		6.423		77.160
5	.514		5.136		82.296
6	.443		4.430		86.726
7	.380		3.798		90.524
8	.360		3.599		94.123
9	.318		3.178		97.301
10	.270		2.699		100.000
Extraction Method: Principal Component Analysis.

Component Matrixa
	Component
	1	2
Esteem1	.698	.338
Esteem2	.684	-.474
Esteem3	.589	.609
Esteem4	.601	.532
Esteem5	.704	-.098
Esteem6	.751	-.383
Esteem7	.726	.218
Esteem8	.718	-.414
Esteem9	.698	-.327
Esteem10	.745	.179
Extraction Method: Principal Component Analysis.
a. 2 components extracted.

Rotated Component Matrixa
	Component
	1	2
Esteem1	.298	.716
Esteem2	.826	.099
Esteem3	.037	.846
Esteem4	.097	.797
Esteem5	.591	.394
Esteem6	.816	.212
Esteem7	.399	.644
Esteem8	.812	.166
Esteem9	.739	.218
Esteem10	.439	.628
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.