Date:         Fri, 3 Sep 2010 10:33:27 +0200
Reply-To:     Spousta Jan <JSpousta@CSAS.CZ>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Spousta Jan <JSpousta@CSAS.CZ>
Subject:      Re: Factor analysis
Comments: To: jacqueline london <jackieblon@yahoo.com>
In-Reply-To:  A<184910.75444.qm@web31008.mail.mud.yahoo.com>
Content-Type: multipart/alternative;

Hi Jacqueline, "E" is a common way how to write numbers in the Scientific notation (http://en.wikipedia.org/wiki/Scientific_notation). For example 3.189E-02 means 3.189 * 10 ^ -2 = 0.03189.
 
In the factor analysis, you can usually ignore these numbers as if they were zeroes - they are too small to have any meaning. You can drop them from the output by using 
 
/FORMAT BLANK(.10)
 
option in the syntax (or check the "Suppress absolute values less than..." option in the interface).
 
Best,
 
Jan

________________________________

From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of jacqueline london
Sent: Friday, September 03, 2010 10:17 AM
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: Factor analysis


Hi all, I am struggling with factor analysis that is new to me. I have a 20-item likert questionnaire and I ran an  initial solutions and the component analysis table below I cannot understand. Some items which have E, would do they mean? Would anyone help? Thanks.
 
Component Matrix

Component

1

2

3

4

5

SELBEL1

.399

.252

-.398

-.280

.105

SELBEF2

.251

-.564

.263

.167

.331

SELBEF3

.647

-.224

.129

-.139

6.780E-02

SELBEF4

.627

-.146

.190

-.263

2.454E-02

SELBEF5

.331

.469

.114

8.683E-02

.279

SELBEF6

.460

.215

.423

-4.469E-02

-.127

SELBEF7

.362

-.159

3.189E-02

-.382

-.148

SELBEF8

.634

-1.308E-02

5.161E-02

-1.292E-02

-.321

SELBEF9

.405

-1.846E-02

.135

-.321

-.369

SELBEF10

.497

-.464

6.585E-02

6.435E-02

.118

SELBEF11

.274

-1.904E-02

-.250

.713

-.242

SELBEF12

.576

.250

-.264

-1.427E-02

-.282

SELBEF13

.350

.438

.356

.142

.269

SELBEF14

.395

.214

.475

.287

-.192

SELBEF15

.464

.364

5.913E-02

.153

.374

SELBEF16

.604

-.373

1.975E-02

-1.295E-02

-1.068E-02

SELBEF17

.488

.338

-.319

-.278

.334

SELBEF18

.378

-.402

-.138

.154

.217

SELBEF19

.539

.220

-.194

.214

-.270

SELBEF20

.658

-.189

-.444

.166

.113

Extraction Method: Principal Component Analysis.

a 5 components extracted.


  
_____________

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