For this assignment, you will identify a specific organizational problem that could be addressed through statistical applications, and you will create a business case (justification for why your problem is important and should be prioritized above other projects requiring resources) to support the need for the analysis.? For example, you might want to explore how a working team could be more efficient in their productivity or how your company could generate incremental revenue through better product design and/or advertising campaigns.? As such, ?you would want to explain the problem, why it is important, and how it could be addressed through the use of statistical applications. You can use the dataset provided for this assignment and all subsequent assignments, or you may use your own dataset.? Whichever dataset you use, it should be used throughout the course given that the assignments build upon prior assignments.?

Your business case should consist of the following components:

- Description of the problem statement
- Justification as to why solving the problem is important, which should be connected to an organizational strategic initiative
- Explanation of how statistical applications could be used to solve ?the problem (e.g., how you would descriptively analyze your data and run ?statistical tests for hypothesis testing)
- Summary

Length: 6 pages, not including title or reference pages

References: Include a minimum of 5 scholarly resources not more than 5 years old.

The completed assignment should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. The content should reflect scholarly writing and current APA 7th edition standards. Include a plagiarism report.

## Scoring Definitions

Growth Opportunity Scoring Definitions | |||||

Evaluation Criteria | Higher Attractiveness / Fit (5 Points) |
Medium Attractiveness / Fit (3 Points) |
Lower Attractiveness / Fit (1 Point) |
||

Attractiveness | Revenue Potential | 3 Year revenue potential of $1,000,000 or more | 3 Year revenue potential of $999,999 – $400,000 | 3 Year revenue potential of $399,999 or less | |

Pretax Potential | More than 40% | Between 30% – 40% | Less than 30% | ||

Strategic Alignment | Fits a key strategic growth initiative / lever and it fits our culture / business model | Fits a strategic growth initiative / lever | Unclear fit with current business strategies | ||

Client Need | Unmet need validated by potential customers; unmet need with customer request for service | Unmet need identified and confirmed (not with customer); met need with customer openess to service | Unmet need may exist but has not been confirmed; met need with customer not intersted in service | ||

Customers | Targets customer inside domain of interest, and decision maker is in a function we are very familiar with | Targets customer inside our domain of interest and the decision maker is unfamiliar with us | Targets customer outside our domain of interest | ||

Time to Revenue | Less than 6 months to initial revenue | 7- 18 months to initial revenue | Greater than 18 months to initial revenue | ||

Investment Required (non employee) |
Minor (0 – 10% of revenue potential) | Moderate (10-20% of revenue potential) | Significant (>20% revenue potential) | ||

Progressive | Cutting Edge – Viewed as progressive by the target customer | Leading Edge – Viewed as “second” to the market but considered progressive | Standard – Effective and proven but not progressive | ||

Ability to Execute / Business Fit | Capabilities – Process | Does not require any significant additions to, or enhancement of, our existing processes | Requires enhancement of existing processes, but does not require new processes | Depends on process that do not exist in the business today | |

Capabilities – Technology Tools | Does not require any significant additions or upgrades to current tools | Requires substantial upgrades to existing tools, but no new tools | Requires new technology tools | ||

Capabilities – Skillsets | Only requires existing leadership, management, and operational skillsets | Requires new skillsets / talent from a leadership/management or an operational perspective (not both) | Requires the addition or new skillsets / talent from both a leadership/management and an operational perspective | ||

Competitors | Competitive set is limited or does not exist (less than 2) | Competitive set is moderate (2-6) | Competitive set is is very robust for our currents offering(s) (7+) | ||

Pricing Model | Pricing terms and mechanics are consistent with current offerings and familiar to the target customer set | Pricing terms and mechanics are different from current offerings or unfamiliar to the target customer set (not both) | Pricing terms and mechanics are different from current offerings and will be unfamiliar to the target customer set | ||

## Template

Growth Opportunity Scoring Sheet | ||||||||

Score Confidence | ||||||||

Growth Opportunity Name: | ||||||||

Instructions: For each of the evaluation criteria listed, please provide a score in the ‘Score’ column based on the criteria provided in the ‘Scoring Definitions’ tab | ||||||||

as well as a brief rationale for why you entered each score | ||||||||

Evaluation Criteria | Weight | Score (1,3,5) |
Weighted Score | Rationale for Score | Score (10/6/2) |
Weighted Score | ||

Economic Fit / Attractiveness | Revenue Potential | 10% | 0.0 | 0 | 0.0 | |||

Pretax Potential | 10% | 0.0 | 0 | 0.0 | ||||

Strategic Alignment | 10% | 0.0 | 0 | 0.0 | ||||

Client Need | 10% | 0.0 | 0 | 0.0 | ||||

Customers | 10% | 0.0 | 0 | 0.0 | ||||

Time to Revenue | 5% | 0.0 | 0 | 0.0 | ||||

Investment Required | 5% | 0.0 | 0 | 0.0 | ||||

Progressive | 10% | 0.0 | 0 | 0.0 | ||||

Total | 70% | 0.0 | 0.0 | 0.0 | ||||

Ability to Execute / Business Fit | Capabilities – Process | 5% | 0.0 | 0 | 0.0 | |||

Capabilities – Technology | 5% | 0.0 | 0 | 0.0 | ||||

Capabilities – Skillsets | 10% | 0.0 | 0 | 0.0 | ||||

Competitors | 5% | 0.0 | 0 | 0.0 | ||||

Pricing Model | 5% | 0.0 | 0 | 0.0 | ||||

Total | 30% | 0.0 | 0.0 | 0.0 | ||||

Total Score | 100% | 0.0 | 0.0 |

## Master Scoring Summary

ID | Initiative Name | Score | ||

Economic Fit/ Attractiveness (70) | Ability To Execute / Business Fit (30) | Confidence Rating | ||

1 | Initiative 1 | 38 | 22 | 90 |

2 | Initiative 2 | 44 | 14 | 55 |

3 | Initiative 3 | 52 | 28 | 80 |

4 | Initiative 4 | 44 | 10 | 75 |

5 | Initiative 5 | 60 | 18 | 80 |

6 | Initiative 6 | 38 | 28 | 75 |

7 | Initiative 7 | 50 | 12 | 65 |

8 | Initiative 8 | 50 | 12 | 65 |

9 | Initiative 9 | 52 | 28 | 80 |

10 | Initiative 10 | 48 | 26 | 65 |

11 | Initiative 11 | 48 | 22 | 60 |

12 | Initiative 12 | 48 | 22 | 60 |

13 | Initiative 13 | 50 | 28 | 75 |

14 | Initiative 14 | 52 | 28 | 70 |

15 | Initiative 15 | 58 | 26 | 85 |

16 | Initiative 16 | 42 | 24 | 90 |

17 | Initiative 17 | 58 | 28 | 90 |

18 | Initiative 18 | 54 | 28 | 95 |

19 | Initiative 19 | 54 | 28 | 95 |

20 | Initiative 20 | 54 | 28 | 100 |

21 | Initiative 21 | 50 | 26 | 100 |

22 | Initiative 22 | 46 | 26 | 80 |

23 | Initiative 23 | 58 | 28 | 100 |

24 | ||||

25 |

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

[CELLRANGE]

38 44 52 44 60 38 50 50 52 48 48 48 50 52 58 42 58 54 54 54 50 46 58 22 14 28 10 18 28 12 12 28 26 22 22 28 28 26 24 28 28 28 28 26 26 28 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Economic Fit/Attractiveness

Ability to Execute/Business Fit

## TIM-7101_Video_Game_Data

Date | Visits | VisitTime | TotalTime | Game | Advertising |

Friday | 0 | 0 | 0 | Police | Yes |

Saturday | 1 | 0.76 | 0.76 | Police | Yes |

Sunday | 0 | 0 | 0 | Police | Yes |

Monday | 0 | 0 | 0 | Police | No |

Tuesday | 0 | 0 | 0 | Police | No |

Wednesday | 0 | 0 | 0 | Police | No |

Thursday | 0 | 0 | 0 | Police | No |

Friday | 0 | 0 | 0 | Police | No |

Saturday | 0 | 0 | 0 | Police | No |

Sunday | 0 | 0 | 0 | Police | No |

Monday | 6 | 1.33 | 7.95 | Police | Yes |

Tuesday | 5 | 2.98 | 14.9 | Police | Yes |

Wednesday | 0 | 0 | 0 | Police | Yes |

Thursday | 7 | 2.4 | 16.83 | Police | Yes |

Friday | 0 | 0 | 0 | Police | Yes |

Saturday | 0 | 0 | 0 | Police | Yes |

Sunday | 1 | 0.82 | 0.82 | Police | Yes |

Monday | 8 | 1.93 | 15.45 | Police | Yes |

Tuesday | 3 | 1.33 | 3.99 | Police | No |

Wednesday | 0 | 0 | 0 | Police | No |

Thursday | 0 | 0 | 0 | Police | No |

Friday | 0 | 0 | 0 | Police | No |

Friday | 1 | 1.68 | 1.68 | Theif | Yes |

Saturday | 1 | 0.67 | 0.67 | Theif | Yes |

Sunday | 0 | 0 | 0 | Theif | Yes |

Monday | 1 | 1.16 | 1.16 | Theif | No |

Tuesday | 0 | 0 | 0 | Theif | No |

Wednesday | 1 | 2.88 | 2.88 | Theif | No |

Thursday | 0 | 0 | 0 | Theif | No |

Friday | 0 | 0 | 0 | Theif | No |

Saturday | 0 | 0 | 0 | Theif | No |

Sunday | 0 | 0 | 0 | Theif | No |

Monday | 8 | 1 | 7.97 | Theif | Yes |

Tuesday | 3 | 1.41 | 4.22 | Theif | Yes |

Wednesday | 0 | 0 | 0 | Theif | Yes |

Thursday | 10 | 2.85 | 28.45 | Theif | Yes |

Friday | 0 | 0 | 0 | Theif | Yes |

Saturday | 1 | 4.44 | 4.44 | Theif | Yes |

Sunday | 1 | 1.23 | 1.23 | Theif | Yes |

Monday | 6 | 2.15 | 12.89 | Theif | Yes |

Tuesday | 0 | 0 | 0 | Theif | No |

Wednesday | 0 | 0 | 0 | Theif | No |

Thursday | 0 | 0 | 0 | Theif | No |

Friday | 0 | 0 | 0 | Theif | No |

Evidence?Based?Library?and?Information?Practice?2007,?2:1?

32

Evidence?Based?Library?and?Information?Practice

?

?

Feature?Article?

?

A?Statistical?Primer:?Understanding?Descriptive?and?Inferential?Statistics?

?

?

Gillian?Byrne?

Information?Services?Librarian?

Queen?Elizabeth?II?Library?

Memorial?University?of?Newfoundland?

St.?John?s,?NL?,?Canada?

Email:?[email?protected]?

?

?

Received:?13?December?2006? ? Accepted:?08?February?2007?

?

?

??2007?Byrne.?This?is?an?Open?Access?article?distributed?under?the?terms?of?the?Creative?Commons?

Attribution?License?(http://creativecommons.org/licenses/by/2.0),?which?permits?unrestricted?use,?

distribution,?and?reproduction?in?any?medium,?provided?the?original?work?is?properly?cited.?

?

Abstract?

?

As?libraries?and?librarians?move?more?towards?evidence-based?decision?making,?the?data?

being?generated?in?libraries?is?growing.?Understanding?the?basics?of?statistical?analysis?is?

crucial?for?evidence-based?practice?(EBP),?in?order?to?correctly?design?and?analyze?research?

as?well?as?to?evaluate?the?research?of?others.?This?article?covers?the?fundamentals?of?

descriptive?and?inferential?statistics,?from?hypothesis?construction?to?sampling?to?common?

statistical?techniques?including?chi-square,?correlation,?and?analysis?of?variance?(ANOVA).?

?

?

Introduction?

Much?of?the?research?done?by?librarians,?

from?bibliometrics?to?surveys?to?usability?

testing,?requires?the?measurement?of?certain?

factors.??This?measurement?results?in?

numbers,?or?data,?being?collected,?which?

must?then?be?analyzed?using?quantitative?

research?methods.?A?basic?understanding?of?

statistical?techniques?is?essential?to?properly?

designing?research,?as?well?as?accurately?

evaluating?the?research?of?others.??

This?paper?will?introduce?basic?statistical?

principles,?such?as?hypothesis?construction?

and?sampling,?as?well?as?descriptive?and?

inferential?statistical?techniques.?Descriptive?

statistics?describe,?or?summarize,?data,?while?

inferential?statistics?use?methods?to?infer?

conclusions?about?a?population?from?a?

sample.?

?

In?order?to?illustrate?the?techniques?being?

Evidence?Based?Library?and?Information?Practice?2007,?2:1?

33

? ? ? ? ? ? ???Great?Job? ? ? ???Lousy?Job?

? ? ? ? ? ? ? ? ? ? ? ? ?

If?you?accept?the?job? ? Have?a?great?experience? Waste?time?&?effort?

?

If?you?decline?the?job? Waste?an?opportunity? Avoid?wasting?time?&?effort?

?

?

Figure?1.?Illustration?of?Type?I?&?II?errors.?

?

?

described?here,?an?example?of?a?fictional?

article?will?be?used.??Entitled?Perceptions?of?

Evidence-Based?Practice:?A?Survey?of?Canadian?

Librarians,?this?article?uses?various?

quantitative?methods?to?determine?how?

Canadian?librarians?feel?about?Evidence-

based?Practice?(EBP).??It?is?important?to?note?

that?this?article,?and?the?statistics?derived?

from?it,?is?entirely?fictional.??

?

Hypothesis?

Hypotheses?can?be?defined?as??untested?

statements?that?specify?a?relationship?

between?two?or?more?variables??(Nardi?36).?

In?social?sciences?research,?hypotheses?are?

often?phrased?as?research?questions.?In?plain?

language,?hypotheses?are?statements?of?

what?you?want?to?prove?(or?disprove)?in?

your?study.??Many?hypotheses?can?be?

constructed?for?a?single?research?study,?as?

you?can?see?from?the?example?in?Fig.?1.?

?

In?research,?two?hypotheses?are?constructed?

for?each?research?question.?The?first?is?the?

null?hypothesis.??The?null?hypothesis?

(represented?as?H0)?assumes?no?relationship?

between?variables;?thus?it?is?usually?phrased?

as??this?has?no?affect?on?this?.??The?

alternative?hypothesis?(represented?as?H1)?is?

simply?stating?the?opposite,?that??this?has?an?

affect?on?this.??The?null?hypothesis?is?

generally?the?one?constructed?for?scientific?

research.?

?

Type?I?&?II?Errors?

Anytime?you?make?a?decision?in?life,?there?is?

a?possibility?of?two?things?going?wrong.??

Take?the?example?of?a?job?offer.?If?you?

decide?to?take?the?job?and?it?turned?out?to?be?

lousy,?you?would?have?wasted?a?lot?of?time?

and?energy.?However,?if?you?decided?to?pass?

on?the?job?and?it?was?great,?you?would?have?

wasted?an?opportunity.??It?s?best?illustrated?

by?a?two?by?two?box?(Fig.?1).?

?

?It?is?obvious?that,?despite?thorough?research?

about?the?position?(speaking?to?people?that?

work?there,?interview?process,?etc.),?it?is?

possible?to?come?to?the?wrong?conclusion?

about?the?job.??The?same?possibility?occurs?in?

research.?If?your?research?concludes?that?

there?is?a?relationship?between?variables?

when?in?fact?there?is?no?relationship?(i.e.,?

you?ve?incorrectly?assumed?the?alterative?

hypothesis?is?proven),?this?is?a?Type?I?error.?

If?your?research?concludes?that?there?is?no?

relationship?between?the?variables?when?in?

fact?there?is?(i.e.,?you?ve?incorrectly?assumed?

the?null?hypothesis?is?proven),?this?is?a?Type??

II?error.?Another?way?to?think?of?Type?I?&?II?

errors?is?as?false?positives?and?false?

negatives.?Type?I?error?is?a?false?positive,?

like?concluding?the?job?is?great?when?it?s?

lousy.??A?Type?II?error?is?a?false?negative;?

concluding?the?job?is?lousy?when?it?s?great.??

?

Type?I?errors?are?considered?by?researchers?

to?be?more?dangerous.??This?is?because?

concluding?there?is?a?relationship?between?

variables?when?there?is?not?can?lead?to?more?

extreme?consequences.??A?drug?trial?

illustrates?this?well.??Concluding?falsely?that?

a?drug?can?help?could?lead?to?the?drug?being?

put?on?the?market?without?being?beneficial?

to?the?public.??A?Type?II?error?would?lead?to?

a?promising?drug?being?left?off?the?market,?

Evidence?Based?Library?and?Information?Practice?2007,?2:1?

34

which?while?serious,?isn?t?considered?as?dire.?

To?help?remember?this,?think?of?the?

conservative?nature?of?science.?Inaction?(and?

possibly?more?testing)?is?less?dangerous?

than?action.??Thus,?disproving?the?null?

hypothesis,?which?supposes?no?relationship,?

is?preferred?to?proving?the?alternative?

hypnosis.?

?

There?are?many?safety?features?built?in?to?

research?methodology?which?help?minimize?

the?possibility?of?committing?both?errors,?

including?sampling?techniques?and?

statistical?significance,?both?of?which?you?

will?learn?about?later.?

?

Dependent?and?Independent?Variables?

Understanding?hypotheses?help?you?

determine?which?variables?are?dependent?

and?which?are?independent?(why?this?is?

important?will?be?revealed?a?bit?later).??

Essentially?it?works?like?this:??the?dependent?

variable?(DV)?is?what?you?are?measuring,?

while?the?independent?variable?(IV)?is?the?

cause,?or?predictor,?of?what?is?being?

measured.?

?

In?experimental?research?(research?done?in?

controlled?conditions?like?a?lab),?there?is?

usually?only?one?hypothesis,?and?

determining?the?variables?are?relatively?

simple.?For?example,?in?drug?trials,?the?

dosage?is?the?independent?variable?(what?

the?researcher?is?manipulating)?while?the?

effects?are?dependent?variables?(what?the?

researcher?is?measuring).?

?

In?non-experimental?research?(research?

which?takes?place?in?the??real?world?,?such?as?

survey?research),?determining?your?

dependent?variable(s)?is?less?straightforward.??

The?same?variable?can?be?considered?

independent?for?one?hypothesis?while?

dependent?for?another.?An?example???you?

might?hypothesize?that?hours?spent?in?the?

library?(independent?variable)?are?a?

predictor?of?grade?point?average?(dependent?

variable).?You?might?also?hypothesize?that?

major?(independent?variable)?affects?how?

much?time?students?spend?in?the?library?

(dependent?variable).?Thus,?your?hypothesis?

construction?dictates?your?dependent?and?

independent?variables.?

?

A?final?variable?to?be?aware?of?in?

quantitative?research?is?the?confounding?

variable?(CV).??Also?know?as?lurking?

variables,?a?confounding?variable?is?an?

unacknowledged?factor?in?an?experiment?

which?might?affect?the?relationship?between?

the?other?variables.??The?classic?example?of?a?

confounding?example?affecting?an?

assumption?of?a?relationship?is?that?murder?

rates?and?ice?cream?purchased?are?highly?

correlated?(when?murder?rates?go?up,?so?

does?the?purchase?of?ice?cream?).?What?is?

the?relationship???There?isn?t?one;?both?

variables?are?affected?by?a?third,?

unacknowledged?variable:?hot?weather.??

?

Population,?Samples?&?Sampling?

Although?it?is?possible?to?study?an?entire?

population?(censuses?are?examples?of?this),?

in?research?samples?are?normally?drawn?

from?the?population?to?make?experiments?

feasible.?The?results?of?the?study?are?then?

generalized?to?the?population.??Obviously,?it?

is?important?to?choose?your?sample?wisely!?

?

Population?

This?might?seem?obvious,?but?the?first?step?is?

to?carefully?determine?the?characteristics?of?

the?population?about?which?you?wish?to?

learn.??For?example,?if?your?research?

involves?your?university,?it?is?worthwhile?to?

investigate?the?basic?demographic?features?

of?the?institution;?i.e.,?what?is?the?percentage?

of?undergraduate?students?vs.?graduate?

students???Males?vs.?females???If?you?think?

these?are?groups?you?would?like?to?compare?

in?your?study,?you?must?ensure?they?are?

properly?represented?in?your?sample.?

?

Sampling?Techniques?

Probability?Sampling?

Evidence?Based?Library?and?Information?Practice?2007,?2:1?

35

Probability?sampling?means?that?each?

member?of?the?population?has?an?equal?

chance?of?being?selected?for?the?survey.??

There?are?several?flavors?of?probability?

sampling;?the?common?characteristic?being?

that?in?order?to?perform?probability?

sampling?you?must?be?able?to?identify?all?

members?of?your?population??

?

Random?sampling?is?the?most?basic?form?of?

probability?sampling.?It?involves?identifying?

every?member?of?a?population?(often?by?

assigning?each?a?number),?and?then?

selecting?sample?subjects?by?randomly?

choosing?numbers.?This?is?often?done?by?

computer?programs.?

?

Stratified?random?sampling?ensures?the?

sample?matches?the?population?on?

characteristics?important?to?a?study.?Using?

the?example?of?a?university,?you?might?

separate?your?population?into?graduate?

students?and?undergraduate?students,?and?

then?randomly?sample?each?group?

separately.?This?will?ensure?that?if?your?

university?has?70%?undergraduates?and?30%?

graduates,?your?sample?will?have?a?similar?

ratio.?

?

Cluster?sampling?is?used?when?a?population?

is?spread?over?a?large?geographic?region.??

For?example,?if?you?are?studying?librarians?

who?work?at?public?libraries?in?Canada,?you?

might?randomly?sample?50?libraries,?and?

then?randomly?sample?the?librarians?within?

those?libraries.?

?

Non-probability?Sampling?

Simply?put,?this?is?any?sampling?technique?

that?does?not?involve?random?sampling.??

Often?samples?are?not?random?because?in?

some?research?it?is?easier?to?perform?

convenience?sampling?(surveying?those?who?

volunteer,?for?example).?Also,?sometimes?the?

population?from?which?the?sample?is?to?be?

taken?cannot?be?easily?identified.??A?

common?strategy?employed?by?libraries?is?to?

use?patron?records?to?derive?random?

samples.?This?is?probability?sampling?only?if?

the?population?is?library?users;?if?the?

population?is?an?entire?institution?or?city,?it?

is?no?longer?random.?With?non-probability?

samples,?you?can?only?generalize?to?those?

who?participated,?not?to?a?population.?

?

Sample?Size?

Sample?size?is?also?extremely?important?to?

be?able?to?accurately?generalize?to?a?

population.?Generally,?the?bigger?the?sample,?

the?better.?The?Central?Limit?Theorem?states?

that?the?larger?the?sample,?the?more?likely?

the?distribution?of?the?means?will?be?normal,?

and?therefore?population?characteristics?can?

more?accurately?be?predicted.??Some?other?

things?to?keep?in?mind:?

?

? If?you?want?to?compare?groups?with?

each?other?(for?example,?majors),?

you?will?need?at?least?5?subjects?in?

each?group?to?do?many?statistical?

analyses.?

?

? Poor?response?rate?can?severely?

compromise?a?study,?if?surveys?are?

involved.??Depending?on?the?

distribution?method,?response?rate?

can?be?as?low?as?10%?(ideally?you?

want?a?response?rate?over?70%)?

(Weisberg?119).Ensure?your?sample?

size?is?large?enough?to?still?provide?

accurate?results?with?a?poor?

response?rate.?

?

There?is?no?magic?formula?to?determine?the?

proper?sample?size???it?depends?on?the?

complexity?of?your?research,?how?

homogenous?the?population?is,?and?time?

and?human?resources?you?have?available?to?

compile?and?analyze?data.?

?

Descriptive?Statistics?

Once?you?have?performed?your?research?

and?gathered?data,?you?need?to?perform?

Evidence?Based?Library?and?Information?Practice?2007,?2:1?

36

?

Table?1.?Examples?of?hypotheses.?

?

?

data?analysis.?Choosing?the?appropriate?

statistical?method?for?the?data?is?crucial.??The?

bad?news?is,?this?means?you?have?to?know?a?

whole?lot?about?your?data???is?it?nominal,?

ordinal?or?ratio??Is?it?normally?distributed??

Let?s?start?from?the?very?beginning.?

A?clear?understanding?of?librarians??perceptions?of?EBP?is?necessary?to?inform?the?development?of?

systems?to?support?EBP?in?librarianship.?

?

The?following?research?questions?were?posed:?

1. What?are?the?perceptions?of?librarians?of?EBP??

2. Does?institution?type?the?librarian?works?at?affect?perception??

3. Does?length?of?service?of?the?librarian?affect?perception??

?

What?are?the?hypotheses??

?

There?are?three?being?provided.?Here?is?a?rephrasing?of?number?3:?

?

H0?=??Length?of?service?of?librarians?has?no?affect?on?the?perception?of?EBP??

H1?=??Length?of?service?of?librarians?affects?the?perception?of?EBP??

?

What?are?the?Type?I?&?II?error?possibilities??

?

? ?

????The?real?situation?(in?the?population)?

?

?????????H0?is?true?????????????????????H1?is?true?

?

No?error?

?

?

?

Type?II?error?

?

?

?

Result?of?

Research?????????????????

(from?sample):???????

H0?is?proven?(length?

of?service?doesn?t?

affect?perception)?

?

?

H1?is?proven?(length?

of?service?does?affect?

perception)?

?

Type?I?error?

?

?

?

No?error?

?

What?are?the?dependent?and?independent?variables??

?

The?researchers?are?attempting?to?determine?whether?length?of?service?can?predict?perception?of?EBP,?

or?to?rephrase,?is?perception?of?EBP?dependant?on?length?of?service.?Therefore:?

?

Dependent?variables:?perception?of?EBP?

Independent?variable:?length?of?service?

Evidence?Based?Library?and?Information?Practice?2007,?2:1?

37

Levels?of?Measurement?

Nominal?variables?are?measured?at?the?most?

basic?level.??They?are?discrete?levels?of?

measurement?where?a?number?represents?a?

category?(i.e.,?1?=?male;?2?=?female),?but?these?

numbers?do?not?imply?order?and?

mathematical?calculations?cannot?be?

performed?on?them.??You?could?just?as?easily?

say,?1?=?male?and?36,000?=?female?-?this?

doesn?t?mean?that?females?are?35,?999?times?

bigger?or?better?than?males!?Nominal?

variables?are?of?the?least?use?statistically.?

?

Ordinal?variables?are?also?discrete?categories,?

but?there?is?an?order?to?the?categories;?they?

increase?and?decrease?at?regular?intervals.??A?

good?example?is?a?Likert?scale:??1?=?very?

poor;?2?=?poor;?3?=?average,?etc.?In?this?

example,?you?can?state?1?is??less??or??smaller??

or??worse??than?2.??The?disadvantage?of?

ordinal?variables?is?that?you?cannot?measure?

in?between?the?values.??You?do?not?know?

how?much?worse?1?is?than?2.?

?

Ratio?(sometimes?known?as?scale,?

continuous?or?interval)?variables?are?the?

most?robust,?statistically,?of?variable?types.??

Ratio?variables?have?natural?order,?and?the?

distance?between?the?points?in?the?same.?

Think?of?pounds?on?a?scale.??You?know?that?

?

?

Table?2.?Examples?of?sampling.?

?

The?sampling?frame?was?the?database?of?all?librarians?(defined?as?those?who?hold?an?MLS)?

who?were?members?of?the?Canadian?Library?Association?in?March?2005.??A?total?of?5,683?

librarians?were?on?the?list.?The?list?was?divided?up?by?type?of?library?worked?at?(academic,?

public,?school,?special,?and?other?/?not?stated).?A?proportional?random?sample?of?210?was?then?

selected.?This?ensured?that?even?at?a?return?rate?of?40%?a?final?sample?size?of?150?would?be?

achieved.?

?

Is?this?a?random?sample??

On?first?glance,?yes.??However,?this?is?only?a?true?random?sample?if?all?librarians?in?Canada?

belonged?to?the?Canadian?Library?Association.??The?design?of?this?study?means?that?the?results?

can?only?be?generalized?to?Canadian?Library?Association?members,?not?to?Canadian?librarians.?

?

What?sampling?technique?is?used??

This?survey?used?stratified?random?sampling?to?ensure?that?all?types?of?librarians?would?be?

represented,?as?illustrated?in?the?chart?below.??Pleas