BR Solution Set 2013

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BUSINESS RESEARCH MS 108

MBA II, JUNE 2013

Q1 STEPS IN RESEARCH PROCESS

Scientific research involves a systematic process that focuses on being objective and gathering a

multitude of information for analysis so that the researcher can come to a conclusion. This

process is used in all research and evaluation projects, regardless of the research method

(scientific method of inquiry, evaluation research, or action research). The process focuses on

testing hunches or ideas in a park and recreation setting through a systematic process. In this

process, the study is documented in such a ay that another individual can conduct the same

study again. This is referred to as replicating the study. !ny research done ithout documenting

the study so that others can revie the process and results is not an investigation using the

scientific research process. The scientific research process is a multiple"step process here the

steps are interlinked ith the other steps in the process. If changes are made in one step of theprocess, the researcher must revie all the other steps to ensure that the changes are reflected

throughout the process. #arks and recreation professionals are often involved in conducting

research or evaluation projects ithin the agency. These professionals need to understand the

eight steps of the research process as they apply to conducting a study.

Step 1$ Identift!eP"#$%e&

The first step in the process is to identify a problem or develop a research question. The research

problem may be something the agency identifies as a problem, some knoledge or information

that is needed by the agency, or the desire to identify a recreation trend nationally. %et us

consider a problem that the agency has identified is childhood obesity, hich is a local problem

and concern ithin the community. This serves as the focus of the study.

Step2'Re(ie)t!e*ite"+t"e

&o that the problem has been identified, the researcher must learn more about the topic under

investigation. To do this, the researcher must revie the literature related to the research

problem. This step provides foundational knoledge about the problem area. The revie of

literature also educates the researcher about hat studies have been conducted in the past, ho

these studies ere conducted, and the conclusions in the problem area. In the obesity study, the

revie of literature enables the programmer to discover horrifying statistics related to the long"term effects of childhood obesity in terms of health issues, death rates, and projected medical

costs. In addition, the programmer finds several articles and information from the 'enters for

isease 'ontrol and #revention that describe the benefits of alking *,*** steps a day. The

information discovered during this step helps the programmer fully understand the magnitude of

the problem, recogni+e the future consequences of obesity, and identify a strategy to combat

obesity (i.e., alking).


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Step3' C%+"if t!e P"#$%e&

any times the initial problem identified in the first step of the process is too large or broad in

scope. In step - of the process, the researcher clarifies the problem and narros the scope of the

study. This can only be done after the literature has been revieed. The knoledge gained

through the revie of literature guides the researcher in clarifying and narroing the researchproject. In the eample, the programmer has identified childhood obesity as the problem and the

purpose of the study. This topic is very broad and could be studied based on genetics, family

environment, diet, eercise, self"confidence, leisure activities, or health issues. !ll of these areas

cannot be investigated in a single study/ therefore, the problem and purpose of the study must be

more clearly defined. The programmer has decided that the purpose of the study is to determine

if alking *,*** steps a day for three days a eek ill improve the individual0s health. This

purpose is more narroly focused and researchable than the original problem.

Step -' C%e+"% .efine Te"&/ +nd C#nept/

Terms and concepts are ords or phrases used in the purpose statement of the study or the

description of the study. These items need to be specifically defined as they apply to the study.

Terms or concepts often have different definitions depending on ho is reading the study. To

minimi+e confusion about hat the terms and phrases mean, the researcher must specifically

define them for the study. In the obesity study, the concept of 1individual0s health2 can be

defined in hundreds of ays, such as physical, mental, emotional, or spiritual health. 3or this

study, the individual0s health is defined as physical health. The concept of physical health may

also be defined and measured in many ays. In this case, the programmer decides to more

narroly define 1individual health2 to refer to the areas of eight, percentage of body fat, and

cholesterol. 4y defining the terms or concepts more narroly, the scope of the study is moremanageable for the programmer, making it easier to collect the necessary data for the study. This

also makes the concepts more understandable to the reader.

Step ' .efine t!e P#p%+ti#n

5esearch projects can focus on a specific group of people, facilities, park development,

employee evaluations, programs, financial status, marketing efforts, or the integration of

technology into the operations. 3or eample, if a researcher ants to eamine a specific group of

people in the community, the study could eamine a specific age group, males or females, people

living in a specific geographic area, or a specific ethnic group. %iterally thousands of options areavailable to the researcher to specifically identify the group to study. The research problem and

the purpose of the study assist the researcher in identifying the group to involve in the study. In

research terms, the group to involve in the study is alays called the population. efining the

population assists the researcher in several ays. 3irst, it narros the scope of the study from a

very large population to one that is manageable. Second, the population identifies the group that

the researcher0s efforts ill be focused on ithin the study. This helps ensure that the researcher


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stays on the right path during the study. 3inally, by defining the population, the researcher

identifies the group that the results ill apply to at the conclusion of the study. 5esearcher has

identified the population of the study as children ages * to 6 years. This narroer population

makes the study more manageable in terms of time and resources.

Step ' .e(e%#p t!e In/t"&ent+ti#n P%+n

The plan for the study is referred to as the instrumentation plan. The instrumentation plan serves

as the road map for the entire study, specifying ho ill participate in the study/ ho, hen, and

here data ill be collected/ and the content of the program. In the obesity study, the researcher

has decided to have the children participate in a alking program for si months. The group of

participants is called the sample, hich is a smaller group selected from the population specified

for the study. The study cannot possibly include every * to 6"year"old child in the community,

so a smaller group is used to represent the population. The researcher develops the plan for the

alking program, indicating hat data ill be collected, hen and ho the data ill be

collected, ho ill collect the data, and ho the data ill be analy+ed. The instrumentation planspecifies all the steps that must be completed for the study. This ensures that the programmer has

carefully thought through all these decisions and that she provides a step"by"step plan to be

folloed in the study.

Step ' C#%%et .+t+

7nce the instrumentation plan is completed, the actual study begins ith the collection of data.

The collection of data is a critical step in providing the information needed to anser the

research question. 8very study includes the collection of some type of data9hether it is from

the literature or from subjects9to anser the research question. ata can be collected in theform of ords on a survey, ith a questionnaire, through observations, or from the literature. In

the obesity study, the programmers ill be collecting data on the defined variables$ eight,

percentage of body fat, cholesterol levels, and the number of days the person alked a total of

*,*** steps during the class. The researcher collects these data at the first session and at the last

session of the program. These to sets of data are necessary to determine the effect of the

alking program on eight, body fat, and cholesterol level. 7nce the data are collected on the

variables, the researcher is ready to move to the final step of the process, hich is the data

analysis.

Step 8' An+%4e t!e .+t+

!ll the time, effort, and resources dedicated to steps through : of the research process

culminate in this final step. The researcher finally has data to analy+e so that the research

question can be ansered. In the instrumentation plan, the researcher specified ho the data ill

be analy+ed. The researcher no analy+es the data according to the plan. The results of this

analysis are then revieed and summari+ed in a manner directly related to the research

questions. In the obesity study, the researcher compares the measurements of eight, percentage


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of body fat, and cholesterol that ere taken at the first meeting of the subjects to the

measurements of the same variables at the final program session. These to sets of data ill be

analy+ed to determine if there as a difference beteen the first measurement and the second

measurement for each individual in the program. Then, the data ill be analy+ed to determine if

the differences are statistically significant. If the differences are statistically significant, the study

validates the theory that as the focus of the study. The results of the study also provide valuable

information about one strategy to combat childhood obesity in the community.

!s you have probably concluded, conducting studies using the eight steps of the scientific

research process requires you to dedicate time and effort to the planning process. 5esearcher

cannot conduct a study using the scientific research process hen time is limited or the study is

done at the last minute. 5esearchers ho do this conduct studies that result in either false

conclusions or conclusions that are not of any value to the organi+ation.

;6a) Qe/ti#nn+i"e' ! questionnaire is a researchinstrument consisting of a series

of questionsand other prompts for the purpose of gathering information from respondents.

!lthough they are often designed forstatisticalanalysis of the responses, this is not alays the

case. ;uestionnaires have advantages over some other types of surveysin that they are cheap, do

not require as much effort from the questioner as verbal or telephone surveys, and often have

standardi+ed ansers that make it simple to compile data. ! distinction can be made beteen

questionnaires ith questions that measure separate variables, and questionnaires ith questions

that are aggregated into either a scale or inde. ;uestionnaires ithin the former category are

commonly part of surveys, hereas questionnaires in the latter category are commonly part of

tests.

;uestionnaires ith questions that measure separate variables could for instance include

questions on$

preferences (e.g. political party)

behaviors (e.g. food consumption)

facts (e.g. gender)

;uestionnaires ith questions that are aggregated into either a scale or inde, include for

instance questions that measure$

latent traits (e.g. personality traits such as etroversion)

attitudes (e.g. toards immigration)

an inde (e.g. Social 8conomic Status)
http://en.wikipedia.org/wiki/Researchhttp://en.wikipedia.org/wiki/Questionhttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Statistical_surveyhttp://en.wikipedia.org/wiki/Questionhttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Statistical_surveyhttp://en.wikipedia.org/wiki/Research


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;6b)O$/e"(+ti#n'7bservation is ay of gathering data by atching behavior, events, or noting

physical characteristics in their natural setting. 7bservations can be overt (everyone knos they

are being observed) or covert (no one knos they are being observed and the observer is

concealed). The benefit of covert observation is that people are more likely to behave naturally if

they do not kno they are being observed. hen data collection from individuals is not a realistic option. If respondents are unilling or

unable to provide data through questionnaires or intervies, observation is a method that

requires little from the individuals from here data need to be collected.

;- T5PES O6 SAMP*IN7 .ESI7N

The sampling design can be broadly grouped on to basis vi+., representation and element

selection. 5epresentation refers to the selection of members on a probability or by other means.

8lement selection refers to the manner in hich the elements are selected individually anddirectly from the population. If each element is dran individually from the population at large,

it is an unrestricted sample. 5estricted sampling is here additional controls are imposed, in

other ords it covers all other forms of sampling. The classification of sampling design on the

basis of representation and element selection is shon belo$

E%e&ent Rep"e/ent+ti#n B+/i/


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Se%eti#n P"#$+$i%it N#np"#$+$i%it

Un"e/t"ited Si&p%e "+nd#& C#n(eniene

5estricted

'omple random

Systematic

Stratified

'luster

ouble

#urposive

?udgment

;uota

Snoball

PROBABI*IT5 SAMP*IN7

#robability sampling is here each sampling unit in the defined target population has a knon

non+ero probability of being selected in the sample. The actual probability of selection for each

sampling unit may or may not be equal depending on the type of probability sampling design

used. Specific rules for selecting members from the operational population are made to ensure

unbiased selection of the sampling units and proper sample representation of the defined target

population. The results obtained by using probability"sampling designs can be generali+ed to the

target population ithin a specified margin of error. The different types of probability sampling

designs are discussed belo/

Un"e/t"ited #" Si&p%e R+nd#& S+&p%in9

In the unrestricted probability sampling design every element in the population has a knon,

equal non+ero chance of being selected as a subject. 3or eample, if * employees (n @ *) are to

be selected from -* employees (& @ -*), the researcher can rite the name of each employee in

a piece of paper and select them on a random basis. 8ach employee ill have an equal knon

probability of selection for a sample. The same is epressed in terms of the folloing formula/

#robability of selection @ Si+e of sample

""""""""""""""""""""""""""

Si+e of population

8ach employee ould have a *A-* or .--- chance of being randomly selected in a dran

sample. >hen the defined target population consists of a larger number of sampling units, a more

sophisticated method can be used to randomly dra the necessary sample. ! table of random

numbers can be used for this purpose. The table of random numbers contains a list of randomly

generated numbers. The numbers can be randomly generated through the computer programsalso. Bsing the random numbers the sample can be selected.

Re/t"ited #" C#&p%e: P"#$+$i%it S+&p%in9

!s an alternative to the simple random sampling design, several comple probability sampling

design can be used hich are more viable and effective. 8fficiency is improved because more


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information can be obtained for a give sample si+e using some of the comple probability

sampling procedures than the simple random sampling design. The five most common comple

probability sampling designs vi+., systematic sampling, stratified random sampling, cluster

sampling, area sampling and double sampling are discussed belo$

S/te&+ti "+nd#& /+&p%in9

The systematic random sampling design is similar to simple random sampling but requires that

the defined target population should be ordered in some ay. It involves draing every nth

element in the population starting ith a randomly chosen element beteen and n. In other

ords individual sampling units are selected according their position using a skip interval. The

skip interval is determined by dividing the sample si+e into population si+e. 3or e.g. if the

researcher ants a sample of ** to be dran from a defined target population of ***, the skip

interval ould be *(***A**). 7nce the skip interval is calculated, the researcher ould

randomly select a starting point and take every * thuntil the entire target population is proceeded

thorough. The steps to be folloed in a systematic sampling method are enumerated belo/ Total number of elements in the population should be identified

The sampling ratio is to be calculated ( n @ total population si+e divided by si+e of the

desired sample)

The random start should be identified

! sample can be dran by choosing every nth entry

To important considerations in using the systematic random sampling are/

It is important that the natural order of the defined target population list be unrelated to

the characteristic being studied. Skip interval should not correspond to the systematic change in the target population.

St"+tified R+nd#& S+&p%in9

Stratified random sampling requires the separation of defined target population into different

groups called strata and the selection of sample from each stratum. Stratified random sampling is

very useful hen the divisions of target population are skeed or hen etremes are present in

the probability distribution of the target population elements of interest. The goal in stratification

is to minimi+e the variability ithin each stratum and maimi+e the difference beteen strata.

The ideal stratification ould be based on the primary variable under study. 5esearchers oftenhave several important variables about hich they ant to dra conclusion. ! reasonable

approach is to identify some basis for stratification that correlates ell ith other major

variables. It might be a single variable like age, income etc or a compound variable like on the

basis of income and gender.


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Stratification leads to segmenting the population into smaller, more homogeneous sets of

elements. In order to ensure that the sample maintains the required precision in terms of

representing the total population, representative samples must be dran from each of the smaller

population groups.

There are three reasons as to hy a researcher chooses a stratified random sample/

To increase the sample0s statistical efficiency

To provide adequate data for analy+ing various sub population

To enable different research methods and procedures to be used in different strata.

raing a stratified random sampling involves the folloing steps/

etermine the variables to use for stratification

Select proportionate or disproportionate stratification

ivide the target population into homogeneous subgroups or strata

Select random samples from each stratum

'ombine the samples from each stratum into a single sample of the target population.

There are to common methods for deriving samples from the strata vi+., proportionate and

disproportionate. In proportionate stratified sampling, each stratum is properly represented so

the sample dran from it is proportionate to the stratum0s share of the total population. The

larger strata are sampled more because they make up a larger percentage of the target population.

This approach is more popular than any other stratified sampling procedures due to the folloing

reasons/

It has higher statistical efficiency than the simple random sample It is much easier to carry out than other stratifying methods

It provides a self"eighting sample ie the population mean or proportion can be

estimated simply by calculating the mean or proportion of all sample cases.

In disproportionate stratified sampling, the sample si+e selected from each stratum is

independent of that stratum0s proportion of the total defined target population. This approach is

used hen stratification of the target population produces sample si+es that contradict their

relative importance to the study.

!n alternative of disproportionate stratified method is optimal allocation. In this method,

consideration is given to the relative si+e of the stratum as ell as the variability ithin the

stratum to determine the necessary sample si+e of each stratum. The logic underlying the optimal

allocation is that the greater the homogeneity of the prospective sampling units ithin a

particular stratum, the feer the units that ould have to be selected to estimate the true

population parameter accurately for that subgroup. This method is also opted for in situation


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In stratified sampling the population is divided into a fe subgroups, each ith many

elements in it and the subgroups are selected according to some criterion that is related to

the variables under the study. In cluster sampling the population is divided into many

subgroups each ith a fe elements in it. The subgroups are selected according to some

criterion of ease or availability in data collection.

Stratified sampling should secure homogeneity ithin the subgroups and heterogeneity

beteen subgroups. 'luster sampling tries to secure heterogeneity ithin subgroups and

homogeneity beteen subgroups.

The elements are chosen randomly ithin each subgroup in stratified sampling. In cluster

sampling the subgroups are randomly chosen and each and every element of the subgroup

is studied in"depth.

.#$%e /+&p%in9

This is also called sequential or multiphase sampling. ouble sampling is opted hen furtherinformation is needed from a subset of group from hich some information has already been

collected for the same study. It is called as double sampling because initially a sample is used in

the study to collect some preliminary information of interest and later a subsample of this

primary sample is used to eamine the matter in more detail The process includes collecting data

from a sample using a previously defined technique. 4ased on this information, a sub sample is

selected for further study. It is more convenient and economical to collect some information by

sampling and then use this information as the basis for selecting a sub sample for further study.

NONPROBABI*IT5 SAMP*IN7

In non"probability sampling method, the elements in the population do not have any probabilities

attached to being chosen as sample subjects. This means that the findings of the study cannot be

generali+ed to the population.


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eploratory phase of a research project and it is the best ay of getting some basic information

quickly and efficiently. The assumptions is that the target population is homogeneous and the

individuals selected as samples are similar to the overall defined target population ith regard to

the characteristics being studied.


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assurance that prespecified subgroups of the defined target population are represented on

pertinent sampling factors that are determined by the researcher. It ensures that certain groups are

adequately represented in the study though the assignment of the quota.

Sn#)$+%% S+&p%in9

Snoball sampling is a non"probability sampling method in hich sets of respondents are

chosen ho helps the researcher to identify additional respondents to be included in the study.

This method of sampling is also called as referral sampling because one respondent refers other

potential respondents. Snoball sampling is typically used in research situations here the

defined target population is very small and unique and compiling a complete list of sampling

units is a nearly impossible task. >hile the traditional probability and other non"probability

sampling methods ould normally require an etreme search effort to qualify a sufficient

number of prospective respondents, the snoball method ould yield better result at a much

loer cost. The researcher has to identify and intervie one qualified respondent and then solicit

his help to identify other respondents ith similar characteristics.

;Ca)S"(e E""#"/'8rrors may occur at any stage during the collection and processing of

survey data, hether it is a census or a sample survey. There are to main sources of survey

error$ Sampling error (errors associated directly ith the sample design and estimation methods

used) and non"sampling error (a blanket term used to cover all other errors). &on"sampling errors

are usually sub"divided as follos$

'overage errors, hich are mainly associated ith the sampling frame, such as missing

units, inclusion of units not in the population of interest, and duplication.

5esponse errors, hich are caused by problems related to the ay questions ere

phrased, the order in hich the questions ere asked, or respondentsD reporting errors(also referred to as measurement error if possible errors made by the intervieer are

included in this category).

&on"response errors, hich are due to respondents either not providing information or

providing incorrect information. &on"response increases the likelihood of bias in the

survey estimates. It also reduces the effective sample si+e, thereby increasing theobserved sampling error.


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!ll of these sources may contribute to either, or both, of the to types of survey error. These are

bias, or systematic error, and variance, or random error.

Sampling error is not an error in the sense of a mistake having been made in conducting the

survey. 5ather it indicates the degree of uncertainty about the DtrueD value based on information

obtained from the number of people that ere surveyed.

It is reasonably straightforard for knoledgeable, eperienced survey"taking organi+ations to

control sampling error through the use of suitable sampling methods and to estimate its impact

using information from the sample design and the achieved sample. !ny statement about

sampling errors, namely variance, standard error, margin of sampling error or coefficient of

variation, can only be made if the survey data come from a probability sample.

The non"sampling errors, especially potential biases, are the most difficult to detect, to control

and to measure, and require careful planning, training and testing.

;Cb) Re%i+$i%it'Internal consistency reliability is used to assess the reliability of a summated

scale here several items are summed to form a total score. In internal consistency reliability

estimation a single measurement instrument is administered to a group of people on one occasion

to estimate reliability. In effect the reliability of the instrument is judged by estimating ho ell

the items that reflect the same construct yield similar results. >e are looking at ho consistent

the results are for different items for the same construct ithin the measure.

There are a ide variety of internal consistency measures that can be used$

i< A(e"+9e Inte"ite& C#""e%+ti#n

The average inter"item correlation uses all of the items on the instrument that are designed to

measure the same construct. The correlation beteen each pair of items is computed first. 3oreample, if e have si items e ill have G different item pairings (i.e., G correlations). The

average inter item correlation is simply the average or mean of all these correlations.

ii< A(e"+9e Ite& t#t+% C#""e%+ti#n

This approach also uses the inter"item correlations. In addition, a total score for the si items is

computed and use that as a seventh variable in the analysis. The figure shos the si item"to"

total correlations at the bottom of the correlation matri.

iii< Sp%itH+%f Re%i+$i%it

In split"half reliability all items that purport to measure the same construct into to sets are

randomly divided. The entire instrument is administered to a sample of people and the total scorefor each randomly divided half is calculated.


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i(< C"#n$+!=/ A%p!+ >?@

'ronbach0s alpha is a statistic. It is generally used as a measure of internal consistency or

reliability of a psychometric instrument. In other ords, it measures ho ell a set of variablesor items measures a single, one"dimensional latent aspect of individuals.

The value of alpha (H) may lie beteen negative infinity and .


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description of several commonly used statistical tools, decision support models, and optimi+ation

routines

;uantitative arket 5esearch ecision Support Tools

Statistical ethods

ultiple 5egression " This statistical procedure is used to estimate the equation ith the

best fit for eplaining ho the value of a dependent variable changes as the values of a

number of independent variables shift. ! simple market research eample is theestimation ofthe best fitfor advertising by looking at ho sales revenue (the dependent variable) changes

in relation to ependitures on advertising, placement of ads, and timing of ads.

3actor !nalysis " This statistical method is used to determine hich are the strongest

underlying dimensionsof a larger set of variables that are inter"correlated. >here manyvariables are correlated, factor analysis identifies hich relations are strongest. ! Bsing factor

analysis, a market researcher ho ants to kno hat combination of variables or factors are

most appealing to a particular type of consumer can use factor analysis to reduce the datadon to a fe variables are most appealing to consumers.

'onditions for a 3actor !nalysis 8ercise$ It requires metric data. This means that the data should be either interval or ratio scale

in nature.

The si+e of the sample respondents should be at least four to five times more than the

number of variables. Initial set of variables should be highly correlated. ! correlation matri of the variables

could be computed and tested for its statistical significance. The test is carried out by

using a 4arttlet test of sphericity, hich takes the determinant of the correlation matri

into consideration.

Laiser"eyer"7lkinis carried out before performing factor analysis, it takes a valuebeteen * and .The L7 statistics compares the magnitude of observed correlation

coefficients ith magnitudes of partial correlation coefficients.

iscriminant !nalysis " This statistical technique is used to for classification of people,

products, or other tangibles into to or more categories. arket research can make use of

discriminant analyses in a number of ays. 7ne simple eample is to distinguishhatadvertising channelsare most effective for different types of products.

ajor assumptions of iscriminant !nalysis are$

the observations are a random sample.

each predictor variable is normally distributed/

each of the allocations for the dependent categories in the initial classiM cation are correctly classiM ed/

there must be at least to groups or categories, ith each case belonging to only one

group so that the groups are mutually eclusive and collectively ehaustive (all cases

can be placed in a group)/

each group or category must be ell deM ned, clearly differentiated from any other

group(s) and natural. #utting a median split on an attitude scale is not a natural ay to
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form groups. #artitioning quantitative variables is only justiM able if there are easily

identiM able gaps at the points of division/

for instance, three groups taking three available levels of amounts of housing loan/

the groups or categories should be deM ned before collecting the data/

the attribute(s) used to separate the groups should discriminate quite clearly beteen

the groups so that group or category overlap is clearly non"eistent or minimal/

group si+es of the dependent should not be grossly different and should be at least M ve

times the number of independent variables.

iscriminat !nalysis is used hen$

the dependent is categorical ith the predictor IK0s at interval level such as age,

income,

attitudes, perceptions, and years of education, although dummy variables can be used

as predictors as in multiple regression. %ogistic regression IK0s can be of any level of

measurement.

there are more than to K categories, unlike logistic regression, hich is limited to a

dichotomous dependent variable.

'luster !nalysis " This statistical procedure is used to separate objectsinto a specific

number of groups that are mutually eclusive but that are also relatively homogeneous inconstitution. This process is similar to hat occurs in market segmentationhere the market

researcher is interested in the similarities that facilitate grouping consumers into segments and

is also interested in the attributes that make the market segments distinct.3or eample, Jouneed to identify people ith similar patterns of past purchases so that you can tailor your

marketing strategies.

The objective of cluster analysis is to identify groups of object that are very similar ith regardto their price consciousness and brand loyalty and assign them into clusters. !fter having decided

on the clustering variables (brand loyalty and price consciousness), e need to decide on the

clustering procedure to form our groups of objects. This step is crucial for the analysis, as

different procedures require different decisions prior to analysis.

'onjoint !nalysis " This statistical method is used to unpack thepreferences of consumersith

regard to different marketing offers. To dimensions are of interest to the market researcher

inconjoint analysis$ () The inferred utility functions of each attribute, and (6) the relative

importance of the preferred attributes to the consumers. 3or eample a computer may be

described in terms of attributes such as processor type, hard disk si+e and amount of memory.8ach of these attributes is broken don into levels " for instance levels of the attribute for

memory si+e might be =4, 6=4, -=4 and C=4.

These attributes and levels can be used to define different products or product profiles. The first

stage in conjoint analysis is to create a set of product profiles hich customers or respondents are
http://marketresearch.about.com/od/market-research-quantitative/a/Surveys-Research-Bayesian-Networks.htmhttp://marketresearch.about.com/sitesearch.htm?q=market+research+market+segmentation&SUName=marketresearchhttp://marketresearch.about.com/od/market.research.techniques/a/Using-Conjoint-Analysis.htmhttp://marketresearch.about.com/od/market.research.techniques/ss/Creating-And-Using-Profiles-For-Conjoint-Analysis.htmhttp://marketresearch.about.com/od/market.research.techniques/ss/Creating-And-Using-Profiles-For-Conjoint-Analysis.htmhttp://marketresearch.about.com/od/market-research-quantitative/a/Surveys-Research-Bayesian-Networks.htmhttp://marketresearch.about.com/sitesearch.htm?q=market+research+market+segmentation&SUName=marketresearchhttp://marketresearch.about.com/od/market.research.techniques/a/Using-Conjoint-Analysis.htmhttp://marketresearch.about.com/od/market.research.techniques/ss/Creating-And-Using-Profiles-For-Conjoint-Analysis.htm


17/25

then asked to compare and choose from. 7bviously, the number of potential profiles increases

rapidly for every ne attribute added, so there are techniques to simplify both the number of

profiles to be tested and the ay in hich preferences are discovered.ifferent type of conjoint

analysis (eg choice based conjoint, full"profile conjoint, or adaptive conjoint analysis) havedifferent approaches to coping ith the balance beteen attribute number and amount of data

that needs to be collected.

4y analysing hich items are chosen or preferred from the product profiles offered to the

customer it is possible to ork out statistically both hat is driving the preference from the

attributes and levels shon, but more importantly, give an implicit numerical valuation for each

attribute and level " knon as utilities or part"orths and importance scores.

The result is a detailed picture of ho customers make decisions and a set of data that can be

used to build market modelshich can predict market share in ne market conditions and test

the impact of product or service changes on the market to see here and ho you can gain the

greatest improvements over your competitors. 4asic assumptions of conjoint analysis are$

The product is a bundle of attributes

Btility of a product is a simple function of the utilities of the attributes

Btility predicts behavior (i.e., purchases)

Q S+%e >/#i+% /iene/@

In the social sciences, /+%in9is the process of measuringor ordering entities ith respect to

quantitative attributes or traits. 3or eample, a scaling technique might involve estimating

individualsD levels of etraversion, or the perceived quality of products. 'ertain methods of

scaling permit estimation of magnitudes on a continuum, hile other methods provide only for

relative ordering of the entities.

See level of measurementfor an account of qualitatively different kinds of measurement scales.
http://www.dobney.com/Conjoint/conjoint_flavours.htmhttp://www.dobney.com/Conjoint/conjoint_flavours.htmhttp://www.dobney.com/Conjoint/conjoint_flavours.htmhttp://www.dobney.com/Conjoint/ModelDemo.htmhttp://www.dobney.com/Conjoint/ModelDemo.htmhttp://en.wikipedia.org/wiki/Measurementhttp://en.wikipedia.org/wiki/Quantitativehttp://en.wikipedia.org/wiki/Continuum_(mathematics)http://en.wikipedia.org/wiki/Level_of_measurementhttp://www.dobney.com/Conjoint/conjoint_flavours.htmhttp://www.dobney.com/Conjoint/conjoint_flavours.htmhttp://www.dobney.com/Conjoint/ModelDemo.htmhttp://en.wikipedia.org/wiki/Measurementhttp://en.wikipedia.org/wiki/Quantitativehttp://en.wikipedia.org/wiki/Continuum_(mathematics)http://en.wikipedia.org/wiki/Level_of_measurement


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C#&p+"+ti(e +nd n#n #&p+"+ti(e /+%in9

>ith comparative scaling, the items are directly compared ith each other (eample $ o you

prefer #epsi or 'okeN). In noncomparative scalingeach item is scaled independently of the

others (eample $


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C#&p+"+ti(e /+%in9 te!nie/

P+i")i/e #&p+"i/#n /+%e" a respondent is presented ith to items at a time and

asked to select one (eample$ o you prefer #epsi or 'okeN). This is an ordinal level

technique hen a measurement model is not applied.

R+n#"de" /+%e " a respondent is presented ith several items simultaneously and

asked to rank them (eample$ 5ate the folloing advertisements from to *.). This is anordinal level technique.

C#n/t+nt /& /+%e" a respondent is given a constant sum of money, script, credits, or

points and asked to allocate these to various items (eample $ If you had ** Jen to spend

on food products, ho much ould you spend on product !, on product 4, on product ',

etc.). This is an ordinal level technique.

QS#"t /+%e" Bp to C* items are sorted into groups based a rank"order procedure.

7tt&+n /+%e" This is a procedure to determine hether a set of items can be rank"

ordered on a unidimensional scale. It utili+es the intensity structure among several

indicators of a given variable. Statements are listed in order of importance. The rating is

scaled by summing all responses until the first negative response in the list. The =uttmanscale is related to 5asch measurement/ specifically, 5asch models bring the =uttman

approach ithin a probabilistic frameork.

N#n#&p+"+ti(e /+%in9 te!nie/

C#ntin#/ "+tin9 /+%e(also called the graphic rating scale) " respondents rate items byplacing a mark on a line. The line is usually labeled at each end. There are sometimes a

series of numbers, called scale points, (say, from +ero to **) under the line. Scoring and

codification is difficult.

*ie"t /+%e " 5espondents are asked to indicate the amount of agreement or

disagreement (from strongly agree to strongly disagree) on a five" or seven"point scale.

The same format is used for multiple questions.

P!"+/e #&p%eti#n /+%e/" 5espondents are asked to complete a phrase on an "point

response scale in hich * represents the absence of the theoretical construct and *

represents the theori+ed maimum amount of the construct being measured. The samebasic format is used for multiple questions.

Se&+nti diffe"enti+% /+%e" 5espondents are asked to rate on a : point scale an item on

various attributes. 8ach attribute requires a scale ith bipolar terminal labels.

St+pe% /+%e" This is a unipolar ten"point rating scale. It ranges from OG to "G and has no

neutral +ero point.
http://en.wikipedia.org/wiki/Pairwise_comparisonhttp://en.wikipedia.org/w/index.php?title=Rank-order_scale&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Constant_sum_scale&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Q-Sort_scale&action=edit&redlink=1http://en.wikipedia.org/wiki/Guttman_scalehttp://en.wikipedia.org/w/index.php?title=Continuous_rating_scale&action=edit&redlink=1http://en.wikipedia.org/wiki/Likert_scalehttp://en.wikipedia.org/wiki/Phrase_completionshttp://en.wikipedia.org/wiki/Semantic_differential_scalehttp://en.wikipedia.org/w/index.php?title=Stapel_scale&action=edit&redlink=1http://en.wikipedia.org/wiki/Pairwise_comparisonhttp://en.wikipedia.org/w/index.php?title=Rank-order_scale&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Constant_sum_scale&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Q-Sort_scale&action=edit&redlink=1http://en.wikipedia.org/wiki/Guttman_scalehttp://en.wikipedia.org/w/index.php?title=Continuous_rating_scale&action=edit&redlink=1http://en.wikipedia.org/wiki/Likert_scalehttp://en.wikipedia.org/wiki/Phrase_completionshttp://en.wikipedia.org/wiki/Semantic_differential_scalehttp://en.wikipedia.org/w/index.php?title=Stapel_scale&action=edit&redlink=1


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T!"/t#ne /+%e " This is a scaling technique that incorporates the intensity structure

among indicators.

The folloing table classifies the various simple data types, associated distributions,

permissible operations, etc. 5egardless of the logical possible values, all of these data types

are generally coded using real numbers, because the theory of random variablesoften

eplicitly assumes that they hold real numbers.

.+t+ TpeP#//i$%e

(+%e/

E:+&p%e

/+9e

*e(e%

#f

&e+/

"e&en

t

.i/t"i$ti#n

S+%e

#f

"e%+ti(

e

diffe"

ene/

Pe"&i//i$%e

/t+ti/ti/

Re9"e//i#n

+n+%/i/

$in+"

*,

(arbitrary

labels)

binary

outcome

(EyesAnoE,

EtrueAfalse

E,

EsuccessAf

ailureE,

etc.) nomin

al

scale

4ernoulli

incom

parabl

e

mode, 'hi"

squared

logistic,prob

it

+te9#"i+

%

, 6, ..., L

(arbitrary

labels)

categorica

l outcome

(specific

blood

type,politi

cal party,

ord,

etc.)

categorical

multinomial

logit,multino

mial probit

#"din+% integerorre

al

number(arb

itrary

relative

score,

significan

t only for

ordinal

scale

categoricalNN relativ

e

compa

ordinal

regression(or

dered

logit,ordered
http://en.wikipedia.org/wiki/Thurstone_scalehttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Random_variablehttp://en.wikipedia.org/wiki/Random_variablehttp://en.wikipedia.org/wiki/Binary_variablehttp://en.wikipedia.org/wiki/Nominal_scalehttp://en.wikipedia.org/wiki/Nominal_scalehttp://en.wikipedia.org/wiki/Nominal_scalehttp://en.wikipedia.org/wiki/Bernoulli_distributionhttp://en.wikipedia.org/wiki/Comparabilityhttp://en.wikipedia.org/wiki/Comparabilityhttp://en.wikipedia.org/wiki/Comparabilityhttp://en.wikipedia.org/wiki/Mode_(statistics)http://en.wikipedia.org/wiki/Chi-squared_testhttp://en.wikipedia.org/wiki/Chi-squared_testhttp://en.wikipedia.org/wiki/Logistic_regressionhttp://en.wikipedia.org/wiki/Probit_regressionhttp://en.wikipedia.org/wiki/Probit_regressionhttp://en.wikipedia.org/wiki/Probit_regressionhttp://en.wikipedia.org/wiki/Categorical_variablehttp://en.wikipedia.org/wiki/Categorical_variablehttp://en.wikipedia.org/wiki/Blood_typehttp://en.wikipedia.org/wiki/Blood_typehttp://en.wikipedia.org/wiki/Political_partyhttp://en.wikipedia.org/wiki/Political_partyhttp://en.wikipedia.org/wiki/Categorical_distributionhttp://en.wikipedia.org/wiki/Multinomial_logithttp://en.wikipedia.org/wiki/Multinomial_logithttp://en.wikipedia.org/wiki/Multinomial_probithttp://en.wikipedia.org/wiki/Multinomial_probithttp://en.wikipedia.org/wiki/Ordinal_variablehttp://en.wikipedia.org/wiki/Integerhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Ordinal_scalehttp://en.wikipedia.org/wiki/Ordinal_scalehttp://en.wikipedia.org/wiki/Categorical_distributionhttp://en.wikipedia.org/wiki/Ordinal_regressionhttp://en.wikipedia.org/wiki/Ordinal_regressionhttp://en.wikipedia.org/wiki/Ordered_logithttp://en.wikipedia.org/wiki/Ordered_logithttp://en.wikipedia.org/wiki/Ordered_logithttp://en.wikipedia.org/wiki/Ordered_probithttp://en.wikipedia.org/wiki/Thurstone_scalehttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Random_variablehttp://en.wikipedia.org/wiki/Binary_variablehttp://en.wikipedia.org/wiki/Nominal_scalehttp://en.wikipedia.org/wiki/Nominal_scalehttp://en.wikipedia.org/wiki/Nominal_scalehttp://en.wikipedia.org/wiki/Bernoulli_distributionhttp://en.wikipedia.org/wiki/Comparabilityhttp://en.wikipedia.org/wiki/Comparabilityhttp://en.wikipedia.org/wiki/Comparabilityhttp://en.wikipedia.org/wiki/Mode_(statistics)http://en.wikipedia.org/wiki/Chi-squared_testhttp://en.wikipedia.org/wiki/Chi-squared_testhttp://en.wikipedia.org/wiki/Logistic_regressionhttp://en.wikipedia.org/wiki/Probit_regressionhttp://en.wikipedia.org/wiki/Probit_regressionhttp://en.wikipedia.org/wiki/Categorical_variablehttp://en.wikipedia.org/wiki/Categorical_variablehttp://en.wikipedia.org/wiki/Blood_typehttp://en.wikipedia.org/wiki/Blood_typehttp://en.wikipedia.org/wiki/Political_partyhttp://en.wikipedia.org/wiki/Political_partyhttp://en.wikipedia.org/wiki/Categorical_distributionhttp://en.wikipedia.org/wiki/Multinomial_logithttp://en.wikipedia.org/wiki/Multinomial_logithttp://en.wikipedia.org/wiki/Multinomial_probithttp://en.wikipedia.org/wiki/Multinomial_probithttp://en.wikipedia.org/wiki/Ordinal_variablehttp://en.wikipedia.org/wiki/Integerhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Ordinal_scalehttp://en.wikipedia.org/wiki/Ordinal_scalehttp://en.wikipedia.org/wiki/Categorical_distributionhttp://en.wikipedia.org/wiki/Ordinal_regressionhttp://en.wikipedia.org/wiki/Ordinal_regressionhttp://en.wikipedia.org/wiki/Ordered_logithttp://en.wikipedia.org/wiki/Ordered_logithttp://en.wikipedia.org/wiki/Ordered_logithttp://en.wikipedia.org/wiki/Ordered_probit


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scale)creating a

rankingrison probit)

$in#&i+% *, , ..., &

number of

successes

(e.g. yes

votes) out

ofNpossi

ble

interva

l

scaleNN

binomial,beta

"binomial,

etc.

additiv

eNN

mean,median,

mode,standard

deviation,corr

elation

binomial

regression(lo

gistic,probit)

#nt

nonnegativ

eintegers(*

, , ...)

number of

items(telephon

e calls,

people,

molecules

, births,

deaths,

etc.) in

given

intervalAar

eaAvolum

e

ratio

scale

#oisson,negat

ive binomial,

etc.

multip

licativ

e

!ll statistics

permitted for

interval scales

plus the

folloing$geo

metric

mean,harmoni

c

mean,coefficie

nt of variation

#oisson,

negative

binomial

regression

"e+%

(+%ed+d

diti(e

real

number

temperatu

re,

relative

distance,l

ocation

parameter

, etc. (or

approim

ately,

anything

not

varying

interva

l scale

normal, etc.

(usually

symmetric

about

themean)

additiv

e

mean,median,

mode,standard

deviation,corr

elation

standardline

ar regression
http://en.wikipedia.org/wiki/Ordered_probithttp://en.wikipedia.org/wiki/Binomial_variablehttp://en.wikipedia.org/wiki/Interval_scalehttp://en.wikipedia.org/wiki/Interval_scalehttp://en.wikipedia.org/wiki/Interval_scalehttp://en.wikipedia.org/wiki/Binomial_distributionhttp://en.wikipedia.org/wiki/Beta-binomial_distributionhttp://en.wikipedia.org/wiki/Beta-binomial_distributionhttp://en.wikipedia.org/wiki/Beta-binomial_distributionhttp://en.wikipedia.org/wiki/Meanhttp://en.wikipedia.org/wiki/Medianhttp://en.wikipedia.org/wiki/Medianhttp://en.wikipedia.org/wiki/Mode_(statistics)http://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Correlationhttp://en.wikipedia.org/wiki/Correlationhttp://en.wikipedia.org/wiki/Binomial_regressionhttp://en.wikipedia.org/wiki/Binomial_regressionhttp://en.wikipedia.org/wiki/Logistic_regressionhttp://en.wikipedia.org/wiki/Logistic_regressionhttp://en.wikipedia.org/wiki/Probit_regressionhttp://en.wikipedia.org/wiki/Count_variablehttp://en.wikipedia.org/wiki/Integerhttp://en.wikipedia.org/wiki/Integerhttp://en.wikipedia.org/wiki/Ratio_scalehttp://en.wikipedia.org/wiki/Ratio_scalehttp://en.wikipedia.org/wiki/Poisson_distributionhttp://en.wikipedia.org/wiki/Negative_binomial_distributionhttp://en.wikipedia.org/wiki/Negative_binomial_distributionhttp://en.wikipedia.org/wiki/Geometric_meanhttp://en.wikipedia.org/wiki/Geometric_meanhttp://en.wikipedia.org/wiki/Geometric_meanhttp://en.wikipedia.org/wiki/Harmonic_meanhttp://en.wikipedia.org/wiki/Harmonic_meanhttp://en.wikipedia.org/wiki/Harmonic_meanhttp://en.wikipedia.org/wiki/Harmonic_meanhttp://en.wikipedia.org/wiki/Coefficient_of_variationhttp://en.wikipedia.org/wiki/Coefficient_of_variationhttp://en.wikipedia.org/wiki/Coefficient_of_variationhttp://en.wikipedia.org/wiki/Poisson_regressionhttp://en.wikipedia.org/wiki/Real-valuedhttp://en.wikipedia.org/wiki/Real-valuedhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Location_parameterhttp://en.wikipedia.org/wiki/Location_parameterhttp://en.wikipedia.org/wiki/Location_parameterhttp://en.wikipedia.org/wiki/Location_parameterhttp://en.wikipedia.org/wiki/Interval_scalehttp://en.wikipedia.org/wiki/Interval_scalehttp://en.wikipedia.org/wiki/Normal_distributionhttp://en.wikipedia.org/wiki/Meanhttp://en.wikipedia.org/wiki/Meanhttp://en.wikipedia.org/wiki/Medianhttp://en.wikipedia.org/wiki/Medianhttp://en.wikipedia.org/wiki/Mode_(statistics)http://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Correlationhttp://en.wikipedia.org/wiki/Correlationhttp://en.wikipedia.org/wiki/Linear_regressionhttp://en.wikipedia.org/wiki/Linear_regressionhttp://en.wikipedia.org/wiki/Ordered_probithttp://en.wikipedia.org/wiki/Ordered_probithttp://en.wikipedia.org/wiki/Binomial_variablehttp://en.wikipedia.org/wiki/Interval_scalehttp://en.wikipedia.org/wiki/Interval_scalehttp://en.wikipedia.org/wiki/Interval_scalehttp://en.wikipedia.org/wiki/Binomial_distributionhttp://en.wikipedia.org/wiki/Beta-binomial_distributionhttp://en.wikipedia.org/wiki/Beta-binomial_distributionhttp://en.wikipedia.org/wiki/Meanhttp://en.wikipedia.org/wiki/Medianhttp://en.wikipedia.org/wiki/Mode_(statistics)http://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Correlationhttp://en.wikipedia.org/wiki/Correlationhttp://en.wikipedia.org/wiki/Binomial_regressionhttp://en.wikipedia.org/wiki/Binomial_regressionhttp://en.wikipedia.org/wiki/Logistic_regressionhttp://en.wikipedia.org/wiki/Logistic_regressionhttp://en.wikipedia.org/wiki/Probit_regressionhttp://en.wikipedia.org/wiki/Count_variablehttp://en.wikipedia.org/wiki/Integerhttp://en.wikipedia.org/wiki/Ratio_scalehttp://en.wikipedia.org/wiki/Ratio_scalehttp://en.wikipedia.org/wiki/Poisson_distributionhttp://en.wikipedia.org/wiki/Negative_binomial_distributionhttp://en.wikipedia.org/wiki/Negative_binomial_distributionhttp://en.wikipedia.org/wiki/Geometric_meanhttp://en.wikipedia.org/wiki/Geometric_meanhttp://en.wikipedia.org/wiki/Geometric_meanhttp://en.wikipedia.org/wiki/Harmonic_meanhttp://en.wikipedia.org/wiki/Harmonic_meanhttp://en.wikipedia.org/wiki/Harmonic_meanhttp://en.wikipedia.org/wiki/Coefficient_of_variationhttp://en.wikipedia.org/wiki/Coefficient_of_variationhttp://en.wikipedia.org/wiki/Poisson_regressionhttp://en.wikipedia.org/wiki/Real-valuedhttp://en.wikipedia.org/wiki/Real-valuedhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Real_numberhttp://en.wikipedia.org/wiki/Location_parameterhttp://en.wikipedia.org/wiki/Location_parameterhttp://en.wikipedia.org/wiki/Interval_scalehttp://en.wikipedia.org/wiki/Interval_scalehttp://en.wikipedia.org/wiki/Normal_distributionhttp://en.wikipedia.org/wiki/Meanhttp://en.wikipedia.org/wiki/Meanhttp://en.wikipedia.org/wiki/Medianhttp://en.wikipedia.org/wiki/Mode_(statistics)http://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Correlationhttp://en.wikipedia.org/wiki/Correlationhttp://en.wikipedia.org/wiki/Linear_regressionhttp://en.wikipedia.org/wiki/Linear_regression


22/25

over a

large

scale)

"e+%

(+%ed&

%tip%i+ti(

e

positive rea

l number

price,

income,

si+e,scale

parameter

, etc.

(especiall

y hen

varying

over alarge

scale)

ratio

scale

log"

normal,gamm

a,eponential,

etc. (usually

a skeeddistr

ibution)

multip

licativ

e

!ll statistics

permitted for

interval scales

plus the

folloing$geo

metric

mean,harmoni

c

mean,coefficie

nt of variation

generali+ed

linear

modelithlo

garithmiclin

k

;:a)ETHICS IN RESEARCH

5esearch that involves human subjects or participants raises unique and comple ethical,

legal, social and political issues. 5esearch ethics is specifically interested in the analysis ofethical issues that are raised hen people are involved as participants in research. There arethree objectives in research ethics. The first and broadest objective is to protect human

participants. The second objective is to ensure that research is conducted in a ay that

serves interests of individuals, groups andAor society as a hole. 3inally, the third objective

is to eamine specific research activities and projects for their ethical soundness, looking atissues such as the management of risk, protection of confidentiality and the process of

informed consent.

3or the most part, research ethics has traditionally focused on issues in biomedical research.The application of research ethics to eamine and evaluate biomedical research has been

ell developed over the last century and has influenced much of the eisting statutes and

guidelines for the ethical conduct of research.


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5esearch ethicists everyhere today are challenged by issues that reflect global concerns in

other domains, such as the conduct of research in developing countries, the limits of

research involving genetic material and the protection of privacy in light of advances intechnology and Internet capabilities.

;:b) ! proper research report includes the folloing sections, submitted in the order listed,

each section to start on a ne page. Some journals request a summary to be placed at the end of

the discussion. Some techniques articles include an appendi ith equations, formulas,

calculations, etc. Some journals deviate from the format, such as by combining results and

discussion, or combining everything but the title, abstract, and literature as is done in the

journal Science. 5eports ill adhere to the folloing standard format$

Title

!bstract

Introduction

8perimental etails or Theoretical !nalysis

5esults

iscussion

'onclusions and Summary

5eferences

Tit%e +nd Tit%e P+9e

The title should reflect the content and emphasis of the project described in the report. It should

be as short as possible and include essential key ords.

A$/t"+t

The abstract should, in the briefest terms possible, describe the topic, the scope, the principal

findings, and the conclusions. It should be ritten last to reflect accurately the content of the

report. The lengths of abstracts vary, but seldom eceed 6**"-** ords.! primary objective of

an abstract is to communicate to the reader the essence of the paper.

Int"#dti#n

E! good introduction is a clear statement of the problem or project and hy you are studying it.E

The nature of the problem and hy it is of interest should be conveyed in the opening

paragraphs. This section should describe clearly but briefly the background information on the


24/25

problem, hat has been done before (ith proper literature citations), and the objectives of the

current project. ! clear relationship beteen the current project and the scope and limitations of

earlier ork should be made so that the reasons for the project and the approach used ill be

understood.

E:pe"i&ent+% .et+i%/ #" T!e#"eti+% An+%/i/

This section should describe hat as actually done. It is a succinct eposition of the laboratory

notebook, describing procedures, techniques, instrumentation, special precautions, and so on. It

should be sufficiently detailed that other eperienced researchers ould be able to repeat the

ork and obtain comparable results.

In theoretical reports, this section ould include sufficient theoretical or mathematical analysis to

enable derivations and numerical results to be checked. 'omputer programs from the public

domain should be cited. &e computer programs should be described in outline form.

If the eperimental section is lengthy and detailed, as in synthetic ork, it can be placed at the

end of the report or as an appendi so that it does not interrupt the conceptual flo of the report.

Its placement ill depend on the nature of the project and the discretion of the riter.

Re/%t/

In this section, relevant data, observations, and findings are summari+ed. Tabulation of data,

equations, charts, and figures can be used effectively to present results clearly and concisely.

Schemes to sho reaction sequences may be used here or elsehere in the report.

.i///i#n

The cru of the report is the analysis and interpretation of the results. >hat do the results meanN


25/25

Citin9 Refe"ene/

%iterature references should be collated at the end of the report and cited in one of the formats

described in The !'S Style =uide or standard journals. o not mi formats. !ll references

should be checked against the original literature. &ever cite a reference that you have not read

yourself. ouble check all journal year, volume, issue, and inclusive page numbers to insure the

accuracy of your citation.

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