Data Analysis Using SPSS

D ata A An n al ysi s U si n g  SPSS  D r. A bdul l ah Al A l -S -Swidi  widi  Othman Oth man Yeop A bd bdu u l l ah  Gr ad adu u ate Sch oo ooll of B u si n ess, UU U U M 

Tomorrow  PL S Path M odel i n g  Workshop 

Res Re sear ch Pr oce oces ss  PO

LR 

Problem Statement

D&C

TF

Hypothesis

Findings

Research Design

Data  Qua uall i ty ! ! 

Op e ra ratio tio n a l Defin De fin ition   Operationally defining a concept is basically to render 

that concept measurable.  This is achieved by looking at the behavioral dimensions, facets or properties denoted by the concept.  Measures for many concepts has already been developed by researchers. Eg. Job satisfaction, Organizational Culture, …etc.  Researchers are advised to note the measures used to measure a construct of interest when conducting the literature review.

I s th the er e a dif di f f er en ce be bett we wee en  T h e Co Con n cept and an d Th T h e  Construct??? 

Variable   A variable is something that can be observed and

measured.  Examples: Age, Exam score  Both of the Age and Exam score are well

defined and measurable.

T ype pes s of V ar arii ab abll es   There are four main types of variables:  Independent variable  Dependent variable  Moderating variable  Mediating (Intervening) variable

Mea Me a s u rem e n t Sc S c a le   The operational definition of a construct is the

way through which this construct is going to be measured.  There are four types of measurement scales, or  called sometime the level of the data:  Nominal Ordinal Interval Ratio

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Que Qu esti on onn n ai airr es an and d M an anag age eme men n t  Research  Much of the data in management and social

science research is gathered using questionnaires or interviews. The validity of the results depends on the quality of these instruments.

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Remember!!!!!  Good questionnaires are difficult to construct; bad questionnaires are difficult to analyze.

Reff l ecti ve Con Re Cons str u ct  OR  F or ma mati tiv ve Co Con n str u ct 

A f te terr col l ecti tin n g  my data data what wh at I  h ave t o do??? 

I h av ave e col olll ecte ted d my  datt a, So what?  da wh at? 

A r e you r ead ady y wi with th your stati tatis sti cal  k n owl owle edge to go f or da data ta anal an alys ysii s??? 

 I don’t like Statistics!!!! Statistics!!!!

W h at to t o do to l ear n  Statistics? 

B e D ete terr mi min n ed! ! ! 

H ave A Str tra ate teg gi c Go Goa al ! ! ! ! 

Learn!!! 

B e Patie tient! nt! ! ! ! 

D ata Sc Scr een i n g  Cle ean i n g   D ata Cl ong g da data ta en en tr y   Wr on

Data H and ndll i ng   M i ssi ng Data  MCAR   M A R   NMAR 

 Outliers 

Outlii er s   U n i var i ate Outl tiv var i ate Outl utlii er s   M ul ti

 Normality 

mall i ty   U ni var i ate N or ma tiva ar i ate N or ma mall i ty   M ul tiv

T h e Two M ai ain n Ste teps ps i n D ata A n al alys ysii s  F ir st : The Validity and Reliability of the Measurement Model

Pill ot Stu Pi tudy  dy  To ens ensu u r e th the e vali dity and r el i ab abii l i ty of of th the e i n str tru u me men n t  u sed, Pil Pi l ot Stu Study dy is i s h i ghl y re r eco comme mmen n de ded. d. I t i s als al so use used to miti mi ti gate gate th the e ef f ect of Common  M eth thod od Var Varii ance and othe oth er i ssu es r el ated ated to the  th e  ques questi onnai onn airr e des desi gn.

T h e Two M ai ain n Ste teps ps i n D ata A n al alys ysii s  Second : Hypothesi Hypothesiss Testing (Structural Model)

Stati sti ca call Test A ppr pproac oach h es  

Parr ame Pa ametr tr i c Stati tatis sti cs 



N on-par on-parame ametr tr i c Stati Statis sti tic cs 

T h e Re Rell ati ation ons sh i p betwe betwee en Two Var Varii ab abll es 

Corr r el ati tio on A n al ysi s   Co

T h e ef f ect of a se set of var arii ab abll es  on oth the er var i abl e  tip pl e L i n ear Re Reg gr essi on   M u l ti x1

x2

x3

y

Remember!!! 

T o Run Ru n Re Regr gre essi on A n al aly ysi s  Example

Parr ame Pa ametr tr i c Stati tatis sti tic cs  N on -Pa -Parr ame metr tr i c Stati tatis sti tic cs 

I n Summ Summa ar y   T t est   A N OVA Tes Test 

Val i di ty ty..  F actor A n al ysi s, Va  Cronbach’s A l pha, I nte nterr nal 

consistency   Re Regr gre essi on A n al alys ysii s   H i er ar arc ch i cal Re Regr gre essi on A An n al ysi s 

Seco con n d Gen Gen er ati ation on Stat tatii sti cal  Modeling  Path th A n al ysi s   Pa x1

x2

x3

M

y

Str tru u ctur al E quati uatio ons M odel i ng  1 e4

EP1 1

e3

e17

EP2

EP

1 e2

EP3

JS1

1

e19

JS2

e20

e21

1

1

1

JS3

1

JS4

JS5

1

1 e1

e18

1

e24

EP4

1

JS

1 1

SI 1

e 13 1

1

SI 2 e22

SI

e 14 1

SI 3

e 15 1

e23 1

OC 1 e8

1

 AC1 1

e7

 AC2

 AC

1 e6

 AC3

OC2

1

1

1 e12

1 e5

OC1

 AC4

e11

OC3 1 e10

OC4 1 e9

SI 4

e 16

SO!!!  Whe Wh en Sh oul ould d I l ear arn n abou abou t Stati Statis sti tic cal  Modeling??? 

T h an k you