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Spss history

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17
1 INTRODUCTION TO SPSS
Transcript

1

INTRODUCTION TO

SPSS

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SPSSSPSSSStatistical tatistical PPackage for ackage for SSocial ocial SSciencesciences

► In 1968, Norman H. In 1968, Norman H. NieNie, C. Hadlai (Tex) , C. Hadlai (Tex) HullHull and Dale H. and Dale H. BentBent, three young men from , three young men from disparate professional backgrounds, disparate professional backgrounds, developed a software system based on the developed a software system based on the idea of using statistics to turn raw data into idea of using statistics to turn raw data into information information

► The initial work on SPSS was done at The initial work on SPSS was done at Stanford University with the intention to Stanford University with the intention to make it available only for local consumption make it available only for local consumption and not international distribution and not international distribution

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► NieNie, a social scientist and Stanford , a social scientist and Stanford doctoral candidate, represented the doctoral candidate, represented the target audience and set the target audience and set the requirementsrequirements

►BentBent, a Stanford University doctoral , a Stanford University doctoral candidate in operations research, had candidate in operations research, had the analysis expertise and designed the analysis expertise and designed the SPSS system file structurethe SPSS system file structure

►HullHull, who had recently graduated from , who had recently graduated from Stanford with a master of business Stanford with a master of business administration degree, programmed.administration degree, programmed.

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►McGraw-Hill published the first SPSS McGraw-Hill published the first SPSS user's manual in 1970 user's manual in 1970

► Nie, Bent, and Hull received a royalty Nie, Bent, and Hull received a royalty from sales of the manual but nothing from sales of the manual but nothing from distribution of the program. In from distribution of the program. In Nie's words, "Nie's words, "It was like Gillette selling It was like Gillette selling razors at cost and getting its profits razors at cost and getting its profits from the bladesfrom the blades."."

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Windows In SPSSWindows In SPSS►Data ViewData View►Variable ViewVariable View►OutputOutput

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DATA VIEW windowDATA VIEW window

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Variable View WindowVariable View Window

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Output windowOutput window

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Name

Age

Sex Male Female

Blood Group

City

Family Background Rural Urban

Family Income

Previous Degree BA Bcom Bsc Others

Current Degree MBA(Reg) MBIT MBA(EXE)

Current Residential Status Boarder Non Boarder

Father’s Education Illiterate Literate Primary Middle

Matric Inter Graduation Master

Others

Mother’s Education Illiterate Literate Primary Middle

Matric Inter Graduation Master

Others

Father’s Occupation Govt. job Private Job Business Others

Mother’s Occupation Govt. job Private Job Business House Job Others

Graduation Marks

Marks in FA

Marks in HRM

Marks in Math & Stat

Facilities InterNet Cell Conv.

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Measurement ScalesData can be classified according to

levels of measurement. The level of measurement of the data often dictates the calculations that can be done to summarize and present the data. It will also determine the statistical tests that should be performed.

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Types of Measurement

Scales

Nominal Interval RatioOrdinal

Data may onlybe classified

Data areranked

MeaningfulDifference

Between values

Meaningful Zeropoint and RatioBetween values

Eye colour,Religion,Sex, etc.

Cricket teamsstandings inICC ranking

Students’ grades, etc

Temperature,Shoe Size,IQ Scores

Bank Balance,Weight,

Height, etc.

Measurement Scales

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Defining variablesDefining variablesVariable Name:-Variable Name:-

Each measured Each measured character must be character must be identified with a identified with a unique NAME unique NAME

Space & hyphen is not Space & hyphen is not allowed in variable allowed in variable namename

► First character must First character must be alphabetbe alphabet

► Under score is allowedUnder score is allowed

EXAMPLE:

• REGD NO (Wrong)

• REGD-NO (Wrong)

• 2005_REGD (Wrong)

• _REGD (Wrong)

•RegistrationNo (Wrong)

•REGD_NO (correct)

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Defining the attributes of Defining the attributes of variablesvariables

► TypeType (or data type). (or data type). Data can be of several Data can be of several types, including numeric, text, currency, and types, including numeric, text, currency, and othersothers

TYPE EXAMPLE

Numeric 1000.05

Comma 1,000.005

Scientific 1 * e3(the number means 1 multiplied by

10 raised to the power 3, i.e. (1)*(103)

Dollar $1,000.00

String Ali shah

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Defining the attributes of Defining the attributes of variablesvariables

Variable labelVariable label.. Defining a label for a Defining a label for a variable makes variable makes output easier to read output easier to read but does not have but does not have any effect on the any effect on the actual analysis For actual analysis For example, the label example, the label "Family Identification "Family Identification Number" is easier to Number" is easier to understand than the understand than the name of the variable, name of the variable, fam_idfam_id..

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Defining the attributes of Defining the attributes of variablesvariables

►Column formatColumn format:-:- allows you to choose allows you to choose the width of the the width of the column as displayed column as displayed on screen and to on screen and to choose how the text choose how the text is aligned in the is aligned in the column (left, center, column (left, center, or right aligned). or right aligned).

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Defining the attributes of Defining the attributes of variablesvariables

Value labels:Value labels:

Enable the use of labels Enable the use of labels instead of values for instead of values for specific values of a specific values of a variable, thereby variable, thereby improving the quality improving the quality of output. For of output. For example, for the example, for the variable variable gendergender, the , the labels "Male" and labels "Male" and "Female" are easier to "Female" are easier to understand than "0" or understand than "0" or "1.” "1.”

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DATADATA


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