524 Assignement (Completed) Assg # 01

8/13/2019 524 Assignement (Completed) Assg # 01

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Qno1). Solution:What is a good business research?Good research is carefully planned and conducted; resulting in dependable data that manager can useto reduce the decision-making risks. It follows the standards of scientific method and is systematic,clearly defined and planned and is based on empirical procedures.

Following are the attributes of a GOOD BUSINESS RESEARCH:

1. The PURPOSE is clearly defined.

The problem involved should be clearly stated, indicating the scope, limitations and the actual meaningof the terms involved should be explained.

2.

The research PROCESS should be explained in details.Significant procedural details should be described to permit another researcher to repeat the research.Each step, such as acquiring participants, sampling methods, and gathering procedures should berevealed. If this information is omitted it is difficult to estimate

The reliability and validity of the data and the research overall.

3. Research DESIGN should be thoroughly planned.

The procedural design should be planned to yield as objective results as possible. If possible methodslike opinion survey should be substituted with more reliable methods like direct observation and gettingdata from documented sources. All possible efforts should be made to minimize the influence ofpersonal bias while working with data collection and recording.

4. LIMITATIONS of the research should be revealed.

The researcher should report the flaws in procedures and methods to be used and how it might affectthe data and findings. It is important as some of the imperfections in the research design and conductmay invalidate the results completely.

5. The data should be ABEQUATELY analyzed.

The data should be classified and analyzed in a way that will help the researcher to come to valuableconclusions.

6. FINDINGS should be presented unambiguously.

The report should include the clearly stated findings and should be reported with maximum objectivity.Presentation of the data should be clear, precise to be reasonably interpreted and easily understood bythe decision maker and organized in a manner so that manager is able to locate crucial data.


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7. AllCONCLUSIONS must be justified!

Only those conclusions should be included into the report for which the data provides solid basis.Researcher should omit the mistake of broaden the basis with the help of his personal

Experience and should try not to draw universal conclusions form the study, which is uses limitedpopulation sample.

8. Clearly Define Your Objectives

One characteristic of good business research is clearly defining your objectives. You have to know inadvance what information you want to procure. For example, you might want to see how satisfiedcustomers are with various product features and with current prices of your products. Additionally, youmight want to determine how your customers rate your customer service department on variables suchas professionalism, timeliness and accuracy. Another objective might be to determine why 10 percent ofyour customers are returning merchandise to the store. Whatever the case, outline your objectives,

then write a questionnaire that will address those objectives.

9. Choose the Right Methodology

Your methodology is the method you use for obtaining your business research data. Business researchmethodologies include phone, mail and Internet surveys as well as personal interviews. Mall interceptstudies are another type of methodology, where researchers talk to consumers in stores. A phonesurvey might be the right methodology if you want to garner information quickly. However, phonerespondents can hang up on you before the survey is completed. You might consider using personalinterviews for longer questionnaires. One advantage of personal interviews is that the respondent isgenerally more attentive, according to Via-Interactive, a business advisory company. Internet surveysmight work best if you run an online business. Mail surveys can take a considerable amount of time, butyou can generally collect more information by mail. Pay people a few bucks as an incentive for mailsurveys. People might be more open about providing demographic information such as race and incomeby mail, especially if they remain anonymous.

10. Use Both Open- and Closed-Ended Responses

It is best to use combination open- and- closed-ended responses in your questionnaire. Closed-endedresponses are multiple choices in nature. Respondents can answer "yes" or "no" or state that they are"very satisfied" or "very dissatisfied with closed-ended responses. Closed-ended responses should make

up the bulk of your questionnaire. Open-ended responses are "fill-in-the-blank" responses. Open-endedresponses will allow your respondents to tell why they answered a question a certain way. An exampleof an open-ended response is the question "Why?" For example, ask a customer "Why do you feel thatway?" when they respond negatively to a survey question.


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11. Do Ongoing Research

Business research should be conducted on a regular basis instead of just periodically. Ongoing surveyscan prevent certain data skews that occur with periodic surveys. For example, a soft drinkmanufacturer's customers might drink fewer soft drinks in the winter than summer. The soft drink

company likely will get a better read on customer usage if they survey customers year-round. That waythe usage information can be averaged throughout the year.

Ultimately the research should help the managers to select more effective, less risky and moreprofitable alternatives.

12. What are the dimensions of a MANAGER?

1. Lodging managers

2. Property, real estate, and community association managers

3. Financial managers

4. Administrative services managers

5. Construction managers

6. Food service managers

7. Purchasing managers, buyers, and purchasing agents

8. Industrial production managers

9. Hotels and Other Accommodations

10. Sales worker supervisors

11. Occupational Information Network Coverage

12. Home appliance repairers

13. Clothing, Accessory, and General Merchandise Stores

14. State and Local Information

15. Advertising sales agents

16. The Career Guide to Industries

17. Construction

18. Medical assistants


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19. Security guards and gaming surveillance officers

20. Top executives

2a) Solution>The business research process entails learning everything possible about company customers,competitors and the industry. The major objectives of the process are determining what products orservices to offer, which customers are most likely to buy them, where to sell them and how to price andpromote them. Various steps in the business research process help a company achieve these objectives.Until the sixteenth century, human inquiry was primarily based on introspection. The way to knowthings was to turn inward and use logic to seek the truth. This paradigm had endured for a millenniumand was a well-established conceptual framework for understanding the world. The seeker ofknowledge was an integral part of the inquiry process.

Identifying Competitors

The first step is identifying key competitors in the industry. One way to garner information on thecompetition is through secondary research. Secondary research information is data that are alreadyavailable about the industry: market share and total market sales. Secondary research may also providedetailed information about competitors, such as number of employees, products they sell and their keystrengths. Secondary research can be obtained through various sources, depending on the industry. Forexample, the NPD Group uses their CREST analysis for restaurants. Nielsen provides data aboutconsumer package goods.

Studying Customers

The process continues with a study of the consumer or business customer. It is important to determinewhat the customer wants and needs before developing products to meet those needs. The consumerwill usually dictate which products will sell. If consumer s needs are not met, they will usually buycompetitive products. The best way to determine customer needs is through primary research. Primaryresearch includes phone surveys, personal interviews and even mail surveys. With these surveys,marketing research professionals will often test certain product concepts, measure customersatisfaction and determine the best features and prices for their products.

SWOT Analysis

Once detailed information on customers and the competition has been garnered, a SWOT analysis canbe used to study the company strengths, weaknesses, opportunities and threats. Strength may be thecompany market share or a good reputation among customers, according to SWOT Analysis at a popularbusiness reference site. A weakness may be inexperienced management. Additionally, a company may


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have an opportunity to purchase another company. Threats may include new government regulation inthe industry or a well-financed new competitor. A company uses the SWOT analysis to exploit itsstrengths via available opportunities. For example, a company with strong financial backing couldpurchase another company to increase its distribution and market share. A business can also minimizeits weaknesses against potential threats, for example by hiring more experienced marketing people to

deal with an increase in competition.

Studying the Target Audience

At least part of the business research process should be devoted to studying a company target audience--the customers who are most likely to purchase the company products. For example, a small radiostation& primary target audience may be white professional women between the ages of 35 and 54. Acompany can determine its target audience through primary research.Application

The Basic Steps of the Marketing Research ProcessThe steps taken during the business research process are effective only if the company uses them todevelop marketing strategies. Also, business research is a constant endeavor. Technologies change, asdo customer tastes. Therefore, it is important to conduct business research throughout the year.

Problem Recognition & Definition

All research begins with a question. Intellectual curiosity is often the foundation for scholarly inquiry.Some questions are not testable. The classic philosophical example is to ask, "How many angels candance on the head of a pin?" While the question might elicit profound and thoughtful revelations, itclearly cannot be tested with an empirical experiment. Prior to Descartes, this is precisely the kind ofquestion that would engage the minds of learned men. Their answers came from within. The modernscientific method precludes asking questions that cannot be empirically tested. If the angels cannot beobserved or detected, the question is considered inappropriate for scholarly research.

A paradigm is maintained as much by the process of formulating questions as it is by the answers tothose questions. By excluding certain types of questions, we limit the scope of our thinking. It isinteresting to note, however, that modern physicists have began to ask the same kinds of questionsposed by the Eastern philosophers. "Does a tree falling in the forest make a sound if nobody is there tohear it?" This seemingly trivial question is at the heart of the observer/observed dichotomy. In fact,quantum mechanics predicts that this kind of question cannot be answered with complete certainty. It isthe beginning of a new paradigm.

Defining the goals and objectives of a research project is one of the most important steps in theresearch process. Clearly stated goals keep a research project focused. The process of goal definition


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usually begins by writing down the broad and general goals of the study. As the process continues, thegoals become more clearly defined and the research issues are narrowed.

Exploratory research (e.g., literature reviews, talking to people, and focus groups) goes hand-in-handwith the goal clarification process. The literature review is especially important because it obviates the

need to reinvent the wheel for every new research question. More importantly, it gives researchers theopportunity to build on each others work.

The research question itself can be stated as a hypothesis. A hypothesis is simply the investigator's beliefabout a problem. Typically, a researcher formulates an opinion during the literature review process. Theprocess of reviewing other scholar's work often clarifies the theoretical issues associated with theresearch question. It also can help to elucidate the significance of the issues to the research community.

The hypothesis is converted into a null hypothesis in order to make it testable. "The only way to test ahypothesis is to eliminate alternatives of the hypothesis." (Anderson, 1966, p.9) Statistical techniqueswill enable us to reject a null hypothesis, but they do not provide us with a way to accept a hypothesis.Therefore, all hypothesis testing is indirect.

Creating the Research Design

Defining a research problem provides a format for further investigation. A well-defined problem pointsto a method of investigation. There is no one best method of research for all situations. Rather, thereare a wide variety of techniques for the researcher to choose from. Often, the selection of a techniqueinvolves a series of trade-offs. For example, there is often a trade-off between cost and the quality ofinformation obtained. Time constraints sometimes force a trade-off with the overall research design.Budget and time constraints must always be considered as part of the design process (Walonick, 1993).

Many authors have categorized research design as either descriptive or causal. Descriptive studies aremeant to answer the questions of who, what, where, when and how. Causal studies are undertaken todetermine how one variable affects another. McDaniel and Gates (1991) state that the twocharacteristics that define causality are temporal sequence and concomitant variation.

The word causal may be a misnomer. The mere existence of a temporal relationship between twovariables does not prove or even imply that A causes B. It is never possible to prove causality. At best,

we can theorize about causality based on the relationship between two or more variables, however, thisis prone to misinterpretation. Personal bias can lead to totally erroneous statements. For example,Blacks often score lower on I.Q. scores than their White counterparts. It would be irresponsible toconclude that ethnicity causes high or low I.Q. scores. In social science research, making falseassumptions about causality can delude the researcher into ignoring other (more important) variables.


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There are three basic methods of research: 1) survey, 2) observation, and 3) experiment (McDaniel andGates, 1991). Each method has its advantages and disadvantages.

The survey is the most common method of gathering information in the social sciences. It can be a face-to-face interview, telephone, or mail survey. A personal interview is one of the best methods obtaining

personal, detailed, or in-depth information. It usually involves a lengthy questionnaire that theinterviewer fills out while asking questions. It allows for extensive probing by the interviewer and givesrespondents the ability to elaborate their answers. Telephone interviews are similar to face-to-faceinterviews. They are more efficient in terms of time and cost, however, they are limited in the amountof in-depth probing that can be accomplished, and the amount of time that can be allocated to theinterview. A mail survey is generally the most cost effective interview method. The researcher canobtain opinions, but trying to meaningfully probe opinions is very difficult.

Observation research monitors respondents' actions without directly interacting with them. It has beenused for many years by A.C. Nielsen to monitor television viewing habits. Psychologists often use one-way mirrors to study behavior. Social scientists often study societal and group behaviors by simplyobserving them. The fastest growing form of observation research has been made possible by the barcode scanners at cash registers, where purchasing habits of consumers can now be automaticallymonitored and summarized.

In an experiment, the investigator changes one or more variables over the course of the research. Whenall other variables are held constant (except the one being manipulated), changes in the dependentvariable can be explained by the change in the independent variable. It is usually very difficult to controlall the variables in the environment. Therefore, experiments are generally restricted to laboratorymodels where the investigator has more control over all the variables.

Sampling

It is incumbent on the researcher to clearly define the target population. There are no strict rules tofollow, and the researcher must rely on logic and judgment. The population is defined in keeping withthe objectives of the study.

Sometimes, the entire population will be sufficiently small, and the researcher can include the entirepopulation in the study. This type of research is called a census study because data is gathered on every

member of the population.

Usually, the population is too large for the researcher to attempt to survey all of its members. A small,but carefully chosen sample can be used to represent the population. The sample reflects thecharacteristics of the population from which it is drawn.


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Sampling methods are classified as either probability or nonprobability. In probability samples, eachmember of the population has a known probability of being selected. Probability methods includerandom sampling, systematic sampling, and stratified sampling. In nonprobability sampling, membersare selected from the population in some nonrandom manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling. The other common form of nonprobability

sampling occurs by accident when the researcher inadvertently introduces no randomness into thesample selection process. The advantage of probability sampling is that sampling error can becalculated. Sampling error is the degree to which a sample might differ from the population. Wheninferring to the population, results are reported plus or minus the sampling error. In nonprobabilitysampling, the degree to which the sample differs from the population remains unknown. (McDaniel andGates, 1991)

Random sampling is the purest form of probability sampling. Each member of the population has anequal chance of being selected. When there are very large populations, it is often difficult or impossibleto identify every member of the population, so the pool of available subjects becomes biased. Randomsampling is frequently used to select a specified number of records from a computer file.

Systematic sampling is often used instead of random sampling. It is also called an Nth name selectiontechnique. After the required sample size has been calculated, every Nth record is selected from a list ofpopulation members. As long as the list does not contain any hidden order, this sampling method is asgood as the random sampling method. Its only advantage over the random sampling technique issimplicity.

Stratified sampling is commonly used probability method that is superior to random sampling because itreduces sampling error. A stratum is a subset of the population that share at least one commoncharacteristic. The researcher first identifies the relevant stratums and their actual representation in thepopulation. Random sampling is then used to select subjects for each stratum until the number ofsubjects in that stratum is proportional to its frequency in the population.

Convenience sampling is used in exploratory research where the researcher is interested in getting aninexpensive approximation of the truth. As the name implies, the sample is selected because they areconvenient. This nonprobability method is often used during preliminary research efforts to get a grossestimate of the results, without incurring the cost or time required to select a random sample.

Judgment sampling is a common nonprobability method. The researcher selects the sample based on judgment. This is usually and extension of convenience sampling. For example, a researcher may decideto draw the entire sample from one "representative" city, even though the population includes all cities.When using this method, the researcher must be confident that the chosen sample is trulyrepresentative of the entire population.

Quota sampling is the nonprobability equivalent of stratified sampling. Like stratified sampling, theresearcher first identifies the stratums and their proportions as they are represented in the population.


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Then convenience or judgment sampling is used to select the required number of subjects from eachstratum. This differs from stratified sampling, where the stratums are filled by random sampling.

Snowball sampling is a special nonprobability method used when the desired sample characteristic israre. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball

sampling relies on referrals from initial subjects to generate additional subjects. While this techniquecan dramatically lower search costs, it comes at the expense of introducing bias because the techniqueitself reduces the likelihood that the sample will represent a good cross section from the population.

Data Collection

There are very few hard and fast rules to define the task of data collection. Each research project uses adata collection technique appropriate to the particular research methodology. The two primary goals forboth quantitative and qualitative studies are to maximize response and maximize accuracy.

When using an outside data collection service, researchers often validate the data collection process bycontacting a percentage of the respondents to verify that they were actually interviewed. Data editingand cleaning involves the process of checking for inadvertent errors in the data. This usually entails usinga computer to check for out-of-bounds data.

Quantitative studies employ deductive logic, where the researcher starts with a hypothesis, and thencollects data to confirm or refute the hypothesis. Qualitative studies use inductive logic, where theresearcher first designs a study and then develops a hypothesis or theory to explain the results of theanalysis.

Quantitative analysis is generally fast and inexpensive. A wide assortment of statistical techniques isavailable to the researcher. Computer software is readily available to provide both basic and advancedmultivariate analysis. The researcher simply follows the preplanned analysis process, without makingsubjective decisions about the data. For this reason, quantitative studies are usually easier to executethan qualitative studies.

Qualitative studies nearly always involve in-person interviews, and are therefore very labor intensiveand costly. They rely heavily on a researcher's ability to exclude personal biases. The interpretation ofqualitative data is often highly subjective, and different researchers can reach different conclusions from

the same data. However, the goal of qualitative research is to develop a hypothesis--not to test one.Qualitative studies have merit in that they provide broad, general theories that can be examined infuture research.


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Data Analysis

Modern computer software has made the analysis of quantitative data a very easy task. It is no longerincumbent on the researcher to know the formulas needed to calculate the desired statistics. However,this does not obviate the need for the researcher to understand the theoretical and conceptualfoundations of the statistical techniques. Each statistical technique has its own assumptions andlimitations. Considering the ease in which computers can calculate complex statistical problems, thedanger is that the researcher might be unaware of the assumptions and limitations in the use andinterpretation of a statistic.

Reporting the Results

The most important consideration in preparing any research report is the nature of the audience. Thepurpose is to communicate information, and therefore, the report should be prepared specifically for

the readers of the report. Sometimes the format for the report will be defined for the researcher (e.g., adissertation), while other times, the researcher will have complete latitude regarding the structure ofthe report. At a minimum, the report should contain an abstract, problem statement, methods section,results section, discussion of the results, and a list of references (Anderson, 1966).

Validity and Reliability

Validity refers to the accuracy or truthfulness of a measurement. Are we measuring what we think weare? "Validity itself is a simple concept, but the determination of the validity of a measure is elusive"(Spector, 1981, p. 14).

Face validity is based solely on the judgment of the researcher. Each question is scrutinized andmodified until the researcher is satisfied that it is an accurate measure of the desired construct. Thedetermination of face validity is based on the subjective opinion of the researcher.

Content validity is similar to face validity in that it relies on the judgment of the researcher. However,where face validity only evaluates the individual items on an instrument, content validity goes further inthat it attempts to determine if an instrument provides adequate coverage of a topic. Expert opinions,literature searches, and pretest open-ended questions help to establish content validity.

Criterion-related validity can be either predictive or concurrent. When a dependent/independentrelationship has been established between two or more variables, criterion-related validity can beassessed. A mathematical model is developed to be able to predict the dependent variable from theindependent variable(s). Predictive validity refers to the ability of an independent variable (or group ofvariables) to predict a future value of the dependent variable. Concurrent validity is concerned with therelationship between two or more variables at the same point in time.


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Construct validity refers to the theoretical foundations underlying a particular scale or measurement. Itlooks at the underlying theories or constructs that explain a phenomena. This is also quite subjectiveand depends heavily on the understanding, opinions, and biases of the researcher.

Reliability is synonymous with repeatability. A measurement that yields consistent results over time is

said to be reliable. When a measurement is prone to random error, it lacks reliability. The reliability ofan instrument places an upper limit on its validity (Spector, 1981). A measurement that lacks reliabilitywill necessarily be invalid. There are three basic methods to test reliability: test-retest, equivalent form,and internal consistency.

A test-retest measure of reliability can be obtained by administering the same instrument to the samegroup of people at two different points in time. The degree to which both administrations are inagreement is a measure of the reliability of the instrument. This technique for assessing reliabilitysuffers two possible drawbacks. First, a person may have changed between the first and secondmeasurement. Second, the initial administration of an instrument might in itself induce a person toanswer differently on the second administration.

The second method of determining reliability is called the equivalent-form technique. The researchercreates two different instruments designed to measure identical constructs. The degree of correlationbetween the instruments is a measure of equivalent-form reliability. The difficulty in using this methodis that it may be very difficult (and/or prohibitively expensive) to create a totally equivalent instrument.

The most popular methods of estimating reliability use measures of internal consistency. When aninstrument includes a series of questions designed to examine the same construct, the questions can bearbitrarily split into two groups. The correlation between the two subsets of questions is called the split-half reliability. The problem is that this measure of reliability changes depending on how the questionsare split. A better statistic, known as Chronbach's alpha (1951), is based on the mean (absolute value)interim correlation for all possible variable pairs. It provides a conservative estimate of reliability, and

generally represents "the lower bound to the reliability of an unweight scale of items" (Carmines andZeller, p. 45). For dichotomous nominal data, the KR-20 (Kuder-Richardson, 1937) is used instead ofChronbach's alpha (McDaniel and Gates, 1991).

Variability and Error

Most research is an attempt to understand and explain variability. When a measurement lacksvariability, no statistical tests can be (or need be) performed. Variability refers to the dispersion ofscores.

Ideally, when a researcher finds differences between respondents, they are due to true difference onthe variable being measured. However, the combination of systematic and random errors can dilute theaccuracy of a measurement. Systematic error is introduced through a constant bias in a measurement. It


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can usually be traced to a fault in the sampling procedure or in the design of a questionnaire. Randomerror does not occur in any consistent pattern, and it is not controllable by the researcher.

Summary

Scientific research involves the formulation and testing of one or more hypotheses. A hypothesis cannotbe proved directly, so a null hypothesis is established to give the researcher an indirect method oftesting a theory. Sampling is necessary when the population is too large, or when the researcher isunable to investigate all members of the target group. Random and systematic sampling are the bestmethods because they guarantee that each member of the population will have an known non-zerochance of being selected. The mathematical reliability (repeatability) of a measurement, or group ofmeasurements, can be calculated, however, validity can only be implied by the data, and it is not directlyverifiable. Social science research is generally an attempt to explain or understand the variability in agroup of people.

References:

Anderson, B. (1966) the Psychology Experiment: An Introduction to the Scientific Method. Belmont, CA:Wadsworth.

McDaniel, C. and R. Gates (1991) Contemporary Marketing Research. St. Paul, MN: West.

Carmines, E., and R. Zeller, (1979) Reliability and Validity Assessment. Beverly Hills: Sage.

Spector, P. (1981) Research Design. Beverly Hills: Sage.

Willowick, D. (1993) Stat Pac Gold IV: Marketing Research and Survey Edition. Minneapolis, MN: StatPac, Ink

Qno2b). Solution:What is a research question?A research question is a clear, focused, concise, complex and arguable question around which

you center your research. You should ask a question about an issue that you are genuinely

curious about.

Research Question:A research question is the methodological point of departure of scholarly research in both thenatural and social sciences. The research will answer any question posed. At an undergraduatelevel, the answer to the research question is the thesis statement.


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IMPORTANCE

The research question is one of the first methodological steps the investigator has to take when

undertaking research. The research question must be accurately and clearly defined.

Choosing a research question is the central element of both quantitative and qualitativeresearch and in some cases it may precede construction of the conceptual framework of study.In all cases, it makes the theoretical assumptions in the framework more explicit, most of all itindicates what the researcher wants to know most and first.

USESThe student or researcher then carries out the research necessary to answer the research

question, whether this involves reading secondary sources over a few days for anundergraduate term paper or carrying out primary research over years for a major project.

Once the research is complete and the researcher knows the (probable) answer to the researchquestion, writing can begin. In term papers, the answer to the question is normally given insummary in the introduction in the form of a thesis statement.

TYPES AND PURPOSEThe Research Question serves two purposes: (1) it determines where and what kind of research

the writer will be looking for and (2) it identifies the specific objectives the study or paper willaddress. Therefore, the writer must first identify the type of study (Qualitative, Quantitative, orMixed) before the Research Question is developed.

Qualitative Study: A Qualitative study seeks to learn why or how, so the writers research mustbe directed at determining the why and how of the research topic. Therefore, when crafting aResearch Question for a Qualitative study, the writer will need to ask a why or how questionabout the topic. For example: How did the company successfully market its new product? Thesources needed for qualitative research typically include print and internet texts (written

words), audio and visual media. Here is Creswell's (2009) example of a script for a qualitativeresearch central question: _________ (How or what) is the _________ (story for for narrativeresearch; meaning of the phenomenon for phenomenology; theory that explains the processof for grounded theory; culture -sharing pattern for ethnography; issue in the case forcase study) of _________ (central phenomenon) for _________ (participants) at _________(research site).


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Quantitative Study: A Quantitative study seeks to learn what, where, or when, so the writersresearch must be directed at determining the what, where, or when of the research topic.Therefore, when crafting a Research Question for a Quantitative study, the writer will need toask a what, where, or when question about the topic. For example: Where should the company

market its new product? Unlike a Qualitative study, a Quantitative study is mathematicalanalysis of the research topic , so the writers research will consist of numbers and statistics.Here is Creswell's (2009) example of a script for a quantitative research question: Does _________ (name the theory) explain the relationship between _________ (independentvariable) and _________ (dependent variable), controlling for the effects of _________ (controlvariable)? Alternatively, a script for a quantitative null hypothesis might be as follows: There isno significant difference between _________ (the control and experimental groups on theindependent variable) on _________ (dependent variable).

Quantitative Studies also fall into two categories: (a) Correlational Studies and (b) ExperimentalStudies: A Quantitative-Correlational study is non-experimental, requiring the writer toresearch relationships without manipulating or randomly selecting the subjects of the research.The Research Question for a Quantitative-Correlational study may look like this: What is therelationship between long distance commuters and eating disorders? A Quantitative-Experimental study is experimental in that it requires the writer to manipulate and randomlyselect the subjects of the research. The Research Question for a Quantitative-Experimentalstudy may look like this: Does the consumption of fast food lead to eating disorders?

Mixed Study: A Mixed study integrates both Qualitative and Quantitative studies, so thewriters research must be directed at determining the why or how and the what, where, orwhen of the research topic. Therefore, the writer will need to craft a Research Question foreach study required for the assignment. Note: A typical study may be expected to havebetween 1 to 6 Research Questions.

Once the writer has determined the type of study to be used and the specific objectives thepaper will address, the writer must also consider whether the Research Question passes the sowhat test. The so what test means that the writer must construct evidence to convince theaudience why the research is expected to add new or useful knowledge to the literature.

Why is a research question essential to the research process?

Research questions help writers focus their research by providing a path through the researchand writing process. The specificity of a well-developed research question helps writers avoidthe all -about paper and work toward supporting a specific, argua ble thesis.


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Steps to developing a research question: Choose an interesting general topic . Even directed academic research should focus

on a topic in which the writer is at least somewhat personally invested. Writers shouldchoose a broad topic about which they genuinely would like to know more. An example

of a general topic might be Slavery in the American South or Films of the 1930s. Do some preliminary research on your general topic . Do a few quick searches in

current periodicals and journals on your topic to see whats already been done and tohelp you narrow your focus. What questions does this early research raise?

Consider your audience. For most college papers, your audience will be academic,but always keep your audience in mind when narrowing your topic and developing yourquestion. Would that particular audience be interested in this question?

Start asking questions. Taking into consideration all of the above, start askingyourself open- ended how and why questions about your general topic. For example,

How did the slave trade evolve in the 1850s in the American South? or Why wereslave narratives effective tools in working toward the abolishment of slavery?

Evaluate your question.Is your research question clear? With so much research available on any giventopic, research questions must be as clear as possible in order to be effective inhelping the writer direct his or her research.

Is your research question focused? Research questions must be specific enough to be well

covered in the space available. (See flip side for examples of focused vs. unfocused researchquestions.)Is your research question complex? Research questions should not be answerable with a simpleyes or no or by easily -found facts. They should, instead, require both research and analysison the part of the writer.

Hypothesize. After youve come up with a question, think about what the path youthink the answer will take. Where do you think your research will take you? What kindof argument are you hoping to make/support? What will it mean if your research

disputes your planned argument?

Sample Research Questions

Unclear: Why are social networking sites harmful?


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Clear: How are online users experiencing or addressing privacy issues on such socialnetworking sites as MySpace and Facebook?

The unclear version of this question doe snt specify which social networking sites or suggest

what kind of harm the sites are causing. It also assumes that this harm is proven and/oraccepted. The clearer version specifies sites (MySpace and Facebook), the type of harm (privacyissues), and who the issue is harming (users). A strong research question should never leaveroom for ambiguity or interpretation.

Unfocused: What is the effect on the environment from global warming?Focused: How are glacial melting affecting penguins in Antarctica?The unfocused research question is so broad that it couldnt be adequately answered in a

book-length piece, let alone a standard college-level paper. The focused version narrows downto a specific cause (glacial melting), a specific place (Antarctica), and a specific group that isaffected (penguins). When in doubt, make a research question as narrow and focused aspossible.

Too simple: How are doctors addressing diabetes in the U.S.? Appropriately Complex: What are common traits of those suffering from diabetes inAmerica, and how can these commonalities be used to aid the medical community inprevention of the disease?The simple version of this question can be looked up online and answered in a few factual

sentences; it leaves no room for analysis. The more complex version is written in two parts; it isthought provoking and requires both significant investigation and evaluation from the writer.As a general rule of thumb, if a quick Google search can answer a research question, its likelynot very effective.

Qno3). Solution:Various Descriptors to Classify the Research Design:Definition of Research design:

A blueprint to collect measure and analyze data. Helps in the allocation of limited resources: Choice between:

Experiment Observation Interview


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Simulation Data collection, whether it should be structured or unstructured The sample size should be large or small Quantitative or qualitative research .

Degree of Question Crystallization

Exploratory Study: Loose structure Expand understanding Provide insight Develop hypotheses

Formal Study: Precise procedures Begins with hypotheses Answers research questions

Approaches for Exploratory Investigations

Participant observation Film, photographs Projective techniques Psychological testing Case studies


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Ethnography Expert interviews Document analysis Proxemics and Kinesics

Commonly Used Exploratory Techniques

Experience Surveys: What is being done? What has been tried in the past with or without success? How have things changed? Who is involved in the decisions? What problem areas can be seen? Whom can we count on to assist or participate in the research?

Focus Groups:

Group discussion 6-10 participants Moderator-led 90 minutes-2 hours


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Data Collection Method:

Monitoring Communication

The Time Dimension Cross-sectional Longitudinal

Panel Cohort

The Topical ScopeStatistical Study

Breadth Population inferences Quantitative Generalizable findings

Case Study Depth Detail Qualitative Multiple sources of information

The Research Environment Field conditions Lab conditions Simulations

Purpose of theStudy


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Descriptive Studies

Experimental Effects

Ex Post Facto Study

After -the-fact report on what happened tothe measured variable

Experiment

Study involving the manipulation or controlof one or more variables to determine theeffect on another variable

Ex Post Facto Design:


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Causation:

The basic element in causal research is that A produces B attain A forces B to happen

Causation and Experimental Design:

Mills Method of Agreement:


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Mills Method of Difference:

Example of a Descriptive Research:

A bank manager would like to profile the individuals who are behind on payment more than 6

months. The profile will describe the individual in terms of age, income, type of job andwhether the person is employed full time or part time. This information will help him in makingfuture decisions related to loans to the same individuals

Descriptive Studies

Descriptions of population characteristics Estimates of frequency of characteristics Discovery of associations among variables

Participants Perceptional Awareness

No deviation perceived Deviations perceived as unrelated Deviations perceived as researcher-induced


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Descriptors of Research Design

Qno4 (i) Solution:Qualitative vs. Quantitative Research

Qualitative Research Quantitative Research

Objective /purpose

To gain an understanding ofunderlying reasons andmotivations

To provide insights into thesetting of a problem, generatingideas and/or hypotheses for laterquantitative research

To uncover prevalent trends inthought and opinion

To quantify data and generalizeresults from a sample to thepopulation of interest

To measure the incidence of variousviews and opinions in a chosen

sample Sometimes followed by qualitative

research which is used to exploresome findings further

Sample Usually a small number of non-representative cases.Respondents selected to fulfill a

Usually a large number of casesrepresenting the population ofinterest. Randomly selected


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given quota. respondents.

Datacollection

Unstructured or semi-structuredtechniques e.g. individual depthinterviews or group discussions.

Structured techniques such as onlinequestionnaires, on-street ortelephone interviews.

Dataanalysis

Non-statistical. Statistical data is usually in the formof tabulations (tabs). Findings areconclusive and usually descriptive innature.

Outcome Exploratory and/or investigative.Findings are not conclusive andcannot be used to makegeneralizations about thepopulation of interest. Develop aninitial understanding and soundbase for further decision making.

Used to recommend a final course ofaction.

Qno4 (ii) Solution:

Discrete and continuous variablesIn quantitative research there are two broad types of variable: discrete and continuous.Discrete, or categorical, variables are those for which subjects or observations can be

categorized. For example, vote choice is a discrete variable since there is a limited set of partiesor candidates to vote for.

A useful distinction for quantitative variables is that they can be discrete or continuous.

The most common type of discrete variable is a count , in which case it must be a wholenumber. Examples include family size, number of road deaths and number of cigarettes smokedper day.

Continuous variables are defined on a continuous scale. Examples include length, weight, partsper million of waste material in water. Inevitably, the rounding of the measurement means thatcontinuous variables are often reported on a discrete scale. For example, taxable income ismeasured in dollars and cents but reported to the Tax Office rounded to the nearest wholedollar. Similarly, when someone asks you for your height, you usually round it to the nearest


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whole cm, and you do not report it on a continuous scale even though height (length) can bemeasured to great accuracy on a continuous scale.


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Qno4 (iii) Solution:

Correlational hypothesesState merely that the variables occur together in some specified manner without implying that

one causes the other. Such weak claims are often made when we believe that there are morebasic causal forces that affect both variables.For example:Level of job commitment of the officers is positively associated with their level of efficiency.Here we do not make any claim that one variable causes the other to change. That will bepossible only if we have control on all other factors that could influence our dependentvariable.

Explanatory (causal) hypotheses

Imply the existence of, or a change in, one variable causes or leads to a change in the othervariable. This brings in the notions of independent and the dependent variables. Cause meansto "help make happen." So the independent variable may not be the sole reason for theexistence of, or change in the dependent variable. The researcher may have to identify theother possible causes, and control their effect in case the causal effect of independent variablehas to be determined on the dependent variable.This may be possible in an experimental design of research

Different ways to state hypotheses

Hi motivation causes hi efficiency. Hi motivation leads to hi efficiency. Hi motivation is related to hi efficiency. Hi motivation influences hi efficiency. Hi motivation is associated with hi efficiency. Hi motivation produces hi efficiency. Hi motivation results in hi efficiency. If hi motivation then hi efficiency. The higher the motivation, the higher the efficiency


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Qno4 (iv) Solution:

Stratified and cluster sampling

Stratified sampling

Basically in a stratified sampling procedure, the population is first partitioned into disjointclasses (the strata) which together are exhaustive. Thus each population element should bewithin one and only one stratum. Then a simple random sample is taken from each stratum, thesampling effort may either be a proportional allocation (each simple random sample wouldcontain an amount of varieties from a stratum which is proportional to the size of that stratum)or according to optimal allocation, where the target is to have a final sample with the minimumvariability possible.

Stratified sampling strategies

A real-world example of using stratified sampling would be for a political survey. If therespondents needed to reflect the diversity of the population, the researcher would specificallyseek to include participants of various minority groups such as race or religion, based on theirproportionality to the total population as mentioned above. A stratified survey could thus claimto be more representative of the population than a survey of simple random sampling orsystematic sampling.

Similarly, if population density varies greatly within a region, stratified sampling will ensure thatestimates can be made with equal accuracy in different parts of the region, and thatcomparisons of sub-regions can be made with equal statistical power. For example, in Ontario asurvey taken throughout the province might use a larger sampling fraction in the less populatednorth, since the disparity in population between north and south is so great that a samplingfraction based on the provincial sample as a whole might result in the collection of only ahandful of data from the north.

Cluster sampling The main difference between stratified and cluster sampling is that in stratified sampling all the

strata need to be sampled. In cluster sampling one proceeds by first selecting a number ofclusters at random and then sampling each cluster or conduct a census of each cluster. Butusually not all clusters would be included.


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Qno5) Solution:

Reliability

Simply put, a reliable measuring instrument is one which gives you the samemeasurements when you repeatedly measure the same unchanged objects or events. We shallbriefly discuss here methods of estimating an instruments reliability. The theory underly ingthis discussion is that which is sometimes called classical measurement theory. Thefoundations for this theory were developed by Charles Spearman (1904, General Intelligence,objectively determined and measures. American Journal of Psychology , 15 , 201-293).

If a measuring instrument were perfectly reliable, then it would have a perfect positive(r = +1) correlation with the true scores. If you measured an object or event twice, and the truescores did not change, then you would get the same measurement both times.

We theorize that our measurements contain random error , but that the mean error iszero. That is, some of our measurements have error that make them lower than the truescores, but others have errors that make them higher than the true scores, with the sum of thescore-decreasing errors being equal to the sum of the score increasing errors. Accordingly,random error will not affect the mean of the measurements, but it will increase the variance ofthe measurements.

Our definition of reliability is 2222

2

2

TM

E T

T

M

T

XX r r

. That is, reliability is the

proportion of the variance in the measurement scores that is due to differences in the truescores rather than due to random error.

Please note that I have ignored systematic (nonrandom) error , optimistically assumingthat it is zero or at least small. Systematic error arises when our instrument consistentlymeasures something other than what it was designed to measure. For example, a test ofpolitical conservatism might mistakenly also measure personal stinginess.

Also note that I can never know what the reliability of an instrument (a test) is, becauseI cannot know what the true scores are. I can, however, estimate reliability .

ValiditySimply put, the construct validity of an operationalization (a measurement or a

manipulation) is the extent to which it really measures (or manipulates) what it claims tomeasure (or manipulate). When the dimension being measured is an abstract construct that isinferred from directly observable even ts, then we may speak of construct validity.


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Face Validity . An operationalization has face validity when others agree that it lookslike it does measure or manipulate the construct of interest. For example, if I tell you that I ammanipulating my subj ects sexual arousal by having them drink a pint of isotonic saline solution,you would probably be skeptical. On the other hand, if I told you I was measuring my male

subjects sexual arousal by measuring erection of their penises, you would probably thi nk thatmeasurement to have face validity.

Content Validity . Assume that we can detail the entire population of behavior (orother things) that an operationalization is supposed to capture. Now consider ouroperationalization to be a sample taken from that population. Our operationalization will havecontent validity to the extent that the sample is representative of the population. To measurecontent validity we can do our best to describe the population of interest and then ask experts(people who should know about the construct of interest) to judge how well representative oursample is of that population.

Criterion-Related Validity . Here we test the validity of our operationalization byseeing how it is related to other variables. Suppose that we have developed a test of statisticsability. We might employ the following types of criterion-related validity:

Concurrent Validity. Are scores on our instrument strongly correlated with scores onother concurrent variables (variables that are measured at the same time). For ourexample, we should be able to show that students who just finished a stats course scorehigher than those who have never taken a stats course. Also, we should be able to showa strong correlation between score on our test and students current level ofperformance in a stats class.

Predictive Validity. Can our instrument predict future performance on an activity that isrelated to the construct we are measuring? For our example, is there a strongcorrelation between scores on our test and subsequent performance of employees in anoccupation that requires the use of statistics?

Convergent Validity. Is our instrument well correlated with measures of otherconstructs to which it should, theoretically, be related? For our example, we mightexpect scores on our test to be well correlated with tests of logical thinking, abstractreasoning, verbal ability, and, to a lesser extent, mathematical ability.

Discriminant Validity. Is our instrument not well correlated with measures of otherconstructs to which it should not be related? For example, we might expect scores onour test not to be well correlated with tests of political conservatism, ethical ideology,love of Italian food, and so on.


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Item Analysis. If you believe your scale is one-dimensional, you will want to conduct anitem analysis. Such an analysis will estimate the reliability of your instrument by measuring theinternal consistency of the items, the extent to which the items correlate well with oneanother. It will also help you identify troublesome items.

To illustrate item analysis with SPSS, we shall conduct an item analysis on data from oneof my past research projects. For each of 154 respondents we have scores on each of ten Likeritems. The scale is intended to measure ethical idealism. People high on idealism believe thatan action is unethical if it produces any bad consequences, regardless of how many goodconsequences it might also produce. People low on idealism believes that an action may beethical if its good consequences outweigh its bad consequences.

Bring the data (KJ-Idealism.sav) into SPSS.

Click Analyze, Scale, Reliability Analysis.


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Reliability Statis tics

.744 10

Cronbach's Alpha N of Items

Select all ten items and scoot them to the Items box on the right.

Click the Statistics box.

Check Scale if item deleted and then click Continue.

Back on the initial window, click OK.

Look at the output. The Cronbach alpha is .744, which is acceptable.


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The Length of the ScaleThe length of the scale affects the real values and the variances of the observed values. Themeasurement errors are smaller in the measurement values obtained from the long scales thanthe short scales (OConnor 1993). Because huge number of items present the abstract

characteristic better (Gay 1985). In this case the number of the items must be increased toincrease the reliability

But if the scale is not reliable, to increase the number of the items does not make thescale reliable (Tekin 1977). Spearman-Brown equation is used to calculate how many timesincrement or decrement in the item number causes how much increment or decrement in thereliability coefficient.

The Expression of the Items in the Scale

To get the reliable information in relative subject depends on the expression of the item asrequired. If the item isnt expressed as required, it is difficult to get required answers (Sencer &Sencer 1978). The reliability of the scale negatively affected in this case since the answer indifferent times would be different (Tekin 1977).

The basic problem of getting information is not related with the respondents.The units required for the information do not avoid to answer, to state their situations oropinions in case of expressing the item as required.

InsufficiencyThe item is called insufficient if it is prepared that it lets overlooks of the details. The insufficiency basically may become because of deficiency, having several meanings, andindefiniteness. A defected item is an item deprived of the knowledge it has to. An item withseveral meanings is an item having more than one meaning and without a limitation in itssubject. Indefinite item is an item deprived of the measurement that let to a certainmeasurement

Misunderstanding

Misunderstanding basically sources from the linguistic properties the item has.There are several reasons that may cause misunderstanding of the item: (Sencer &Sencer 1978).

i) The words that are out of knowledge and experiences of the respondentsshould not be used.

ii) The words in the item should not include various meanings


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Biasness:Some items have a tendency to get some answers in one way as a result of their way ofconstructing. Those items, not giving the same chance to all answers, are called biased items.The item types causing biasness can be listed as follows.

a) Directing ItemsThe items, affecting the respondents and directing the answers to one way, constructthat class.

b) Loaded ItemsThe items with some feeling or meaning in a defined subject and tending an approvaland remembrance by itself or the items with sayings.

c) The items that cause prestige biasnessThose items cause respondents seem more superior than they are.

d) The items that cause restlessnessThe items that cause restlessness since they research the illegal attitudes and behaviorsprivate subjects or since they require the answers with low prestige construct that class.

Homogeneity of the GroupAnother factor affecting the reliability of the item is homogeneity of the group. When otherconditions are equal, the more homogeny group provides the more reliable scales. That can beseen with a transformation to the equation of the reliability

The Duration of the Scale If the scale is prepared to measure in a limited time, the insufficiency of time decreases thereliability of the scale. The time must be enough for respondents to answer all the items (Oncu1994). Since the limit in time causes excitement and carelessness, the reliability of the scaledecreases. In case of insufficiency time in scales, careless answers will be given and that willcause to get values closed to zero for the scales reliability

Objectivity in Scoring

The reliability of a scale is affected from the scorings being whether objective or theresearchers being whether subjective. The consistency of the scores observed from the sameor different subject in different times is called that scales scoring reliability. If a score obtainedfrom a scale is not changing due to the person scoring the scale or the time of scoring thatmeans the scales scoring reliability is high.


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The Conditions in Making a MeasurementRespondents being tired, careless, and sleepless, the atmosphere of the measurement and thetemperature will cause unwillingness. This will affect the scales reliability in negative way(Oncu 1994).

The Explanation of the ScaleThe same expressions must be used in the first page of the scale explanation part so all therespondents will understand the same things. The aim of the scale must be told therespondents clearly; the information about how the scale will be responded and determinedthe principals will be taken into account about anonymous (Serper & Gursakal 1989).

The Characteristics of the Items of the ScaleThe reliability of the values obtained from a scale must be dependent on then itemscharacteristic properties. In the items of the scale two characteristic properties are taken intoaccount about the reliability of the measurement values. These properties are differentiatingindex and reliability index. Other than those two properties, in the scales that aim tomeasure some other information, another property about then reliability of the measurementvalues, the difficulty index is taken into account

Difficulty of the ScaleAnother factor is the difficulty degree of the items in the scales that aim the level of theknowledge on subject. The knowledge scales must be prepared appropriately to the knowledgelevel of respondents. The reliability of the scales with very difficult or very easy items will below since the variability among the values will be low.

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