Chat with us, powered by LiveChat For this assignment, you will identify a specific organ - Study Help
  

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

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