1 orcom 155 introduction (1)

8/2/2019 1 OrCom 155 Introduction (1)

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Introduction


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Statistics is the art and science of collection,presentation, analysis, and interpretation of data.

Statistics are numerical facts that are systematically

collected or analyzed. Think of it as sheep, which can be both singularand plural.


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Condenses large quantities ofinformation into a few simplefigures or statements

Aids in decision-making Gives basis for comparison Justifies a claim or assertion Helps in finding a relationship Predicts future outcome


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Sports Research Health

Predictions

Statistics

is forYOU!!!


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Descriptive and Inferential Statistics Descriptive statistics consists of the collection,

organization, summarization, and presentation of data.Inferential statistics, on the other hand, uses probability. It

also generalizes from samples to populations, performshypothesis testing, and determines relationships amongvariables.


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Population (Totality) and Sample (Sub-group)

Quantitative (Numerical) and Qualitative (Categorized) Discrete (Countable) and Continuous Variables Parameter (from the population) and Statistic (from the

sample)


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If, in a US study, it isfound that lightninghits more men (376)

than women (63) how might this

information be used byan insurance company?


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Imagine that there is a study thatseeks to know how many men wantto know the gender of their wivesunborn children.

Lets say that 25% of the men want toknow, and the remaining 75% do notwant to.

How may we define the population?

How may we define the sample?


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Nominal

Mutually exclusive (non-overlapping)

Exhaustive

No order or ranking canbe imposed

Best and easiestexamples: Gender andCourse

Ordinal

Classifies data intocategories that can be

ranked

Precise differences donot exist between the

ranks though.

Examples: Letter grades,attitude scales, peoples

builds


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Interval

Ranks data

Precise differences exist

No meaningful zero

Examples: IQ Tests,Celsius and Fahrenheittemperature scales

Ratio

Has all the properties ofthe interval level, but

also has a meaningfulzero (where zero signifies

total absence).

True ratios exist between

different units ofmeasure

Weight, Length, andIncome


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Intelligence QuotientLapsed time

Eye color

CourseTournament Ranking

UPCAT scoreNationality

HeightGold medals won

ZIP code


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Validity

The extent to which atest measures what

we actually want tomeasure

The degree to which

they accomplish thepurpose for whichthey are being used.


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Reliability

The accuracy andprecision of a

measurementprocedure

The extent to which

an experiment, test,or any measuringprocedure yields thesame result on

repeated trials.


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Availability Internal External

Source Primary Secondary

Series

Cross-Sectional

Cohort

Panel


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Census/Survey

Personal Interview

Telephone Interview Self-administered Questionnaire

Experiment

Naturalistic Observation No manipulation of variables is done


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Probability Sampling

Simple RandomSampling

Systematic Sampling

Stratified Sampling

Cluster Sampling

Multi-Stage Sampling

Non-ProbabilitySampling

Convenience

Purposive

Quota

Snowball


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This is the reduction of a wide variety ofidiosyncratic information to a more limited set ofattributes composing a variable.

Coding must be done in somewhat more detail thanwhat you plan to use in the analysis.

Keep in mind: code categories must be exhaustiveand mutually exclusive.


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This is a document used as the primary guide in thecoding process.

It helps you locate variables and interpret codes

during the analysis stage. There are certain requisites:

Variables should be identified by an abbreviation.

The full definition of the variable should be in thecodebook.

The exact wordings of questions must be contained.


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This is the end product of the coding process: theconversion of data items into numerical codes.

We can use SPSS and MS Excel. Later you will see a demonstration of encoding in

both programs.


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Dirty data will almost always produce dirtyfindings.

We can clean data using two methods: Possible code cleaning

SPSS: Variable definition

MS Excel: Validation

Contingency cleaning Logic and common sense. For example, if you see height

listed as 220cm, you may want to go back to theoriginals.

1 orcom 155 introduction (1)

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