Hypohtesis Testing

Steps to hypothesis testing.

Hypothesis testing can be summarize in a six step procedure:

• Establish a null hypothesis as well as the alternative hypothesis.
• It is a one-tailed test of significance if the alternative hypothesis states the direction of no difference in the other hand if no direction or difference is given, it is a two-tailed test.
• Choose the statistical test on the basis of the assumption about the population distribution and measurement level.
• The form of the data can be a factor, in that case you will typically chose the test that has the greatest power efficiency or ability to reduce decision errors.
• Select the desired level of confidence.
• =0.5 is the most frequently used level, many others are also used.
• Compute the actual test value of the data.
• After the data is collected, use the formula for the appropriate significance test to obtain the calculated value. The computation results come from a software program.
• Obtain the critical test value, usually by referring to a table for the appropriate type of distribution.
• The critical value is the criterion that defines the region of rejection from the region of acceptance of the null hypothesis.
• Interpret the result by comparing the actual test value with the critical test value.
• For most test if the calculated value is larger than the critical value, we reject the null hypothesis and conclude that the alternative hypothesis is supported. If the critical value is larger, we conclude we have failed to reject the null.

 Hypothesis testing is crucial because it helps us to reason from evidence found in the sample  to conclusions. The virtues of this steps also allow us to to determine the accuracy of the hypotheses due to the fact that you have collected a sample of data.

Business Research Methods, Eleventh Edition. D. Cooper/P. Schindler

Pretest Survey Instruments

Why is desirable to pretest survey instruments?

Researchers should pretest survey instruments because:

• It helps to discover ways to increase participant interest
• Increases the likelihood that participants will remain engaged to the completion of the survey
• It helps discover question content, wording and sequencing problems
• Discovers target question groups where researcher training is needed
• Helps explore different ways to improve the overall quality of survey data.

The information that a researcher can secure by pretesting is the participant's reaction to the questions. Also, the researcher can secure errors in the questionnaire and fixed it along the way.

The best wording for a questionnaire can be found by asking your self the following questions:

1. Is the question stated in terms of a shared vocabulary?
2. Does the question contain vocabulary with a single meaning?
3. Does the question contain unsupported or misleading assumptions?
4. Does the question contain biased wording?
5. Is the question correctly personalized?
6. Are adequate alternatives presented withing the question?

The researcher must know who the target participants are going to be and what kind of answers is looking for in order to create a good set of questions.The vocabulary use in the questionnaire must be understood by both the researcher and the participant. The vocabulary can be found by using simple English words and phrases. You should also remember that questions should not be only constructed on words, you can incorporated visual images as part of the questioning process.

Business Research Methods, Eleventh Edition. D.Copper/P. Schindler

Four major sources of measurement error.

Four major error sources:

• The Respondent
• The Situation
• The Measurer
• The Data Collection Instrument

The Respondent

When it comes to a face to face interview the researcher can encounter  participants that don't want to give a strong negative or positive opinion.  Also, the participants may have no knowledge of the subject with no intentions of revealing that to the researcher, which can cause the data collected to be full with guesses or assumptions.

The Situation

In a face to face interview the situation factor can affect the data in different ways. First, if someone is present during the interview this person can interrupt, distract or just by being there can change the participants answers. Second, if the participant is afraid that the interview responses are not going to be kept anonymous the may be reluctant to give the right answer. Finally, participants in face to face interviews give less elaborate answers compare to the home interviews.

The Measurer

 The interviewer can change responses by paraphrasing, rewording or reordering the questions, which can cause a major change in the responses by the participants. Also, not concentrating by checking the wrong response or not recording the complete responses can distort the findings.

The Instrument

If the researcher instruments to conduct the interview can cause distortion in two major ways. First, it can cause confusion to the participant by using complex words beyond the participant comprehension. The use of mechanical defect tools, not waiting or giving enough space for response, and poor printing can cause the final data findings. Second, poor selection from the universe of content items. The interviewer can create a bad survey by not including more broad questions about the entire subject or company that they are trying to investigate. Even when the general issue or subject are studied, the questions may not cover enough aspects of each area of concern.

Observational Studies, Experiment, and Surveys in Qualitative Research

Observational  studies are use in qualitative research when the need for studying records, mechanical processes, lower animals, small children and complex interactive processes. Observational studies is only one of the few options available for such research.The observational studies are good because it help the researcher gather data as the event occurs, to do so the researcher has to be present or have some recording device on the scene. Another good use for observational studies is that can secure information about people or activities that can not be possible from experiments or surveys.

Experiments in qualitative research are use when a researcher intervene beyond that required for measurement. The usual intervention is when  the subject being study is manipulated by the observer  and how the subject is affected by it. Experiments are good for the ability they have to uncover casual relationships, provision for controlling extraneous and environmental variables, convenience of low cost  test situations rather than searching for their appearance in business situations, the ability to replicate findings, and the ability to exploit natural events.

Surveys in qualitative research are use when the need is to derive comparable data across subsets of the chosen sample so that similarities and differences can be found. Surveys findings and conclusions can be more reachable to a larger and diverse population. Also surveys as a primary data collection is really versatile and can save the researches money  expanding geographically more efficient than with observation.

This data collection can translate to quantitative research by inputting the data collected by observational studies, experiment or surveys into a computerize method like a spreadsheet. By using a spreadsheet the data concentrates more on the quantity of responses of the same or opposite question, giving the researcher a total number of the data found or percentage of the event or question being answer. In survey for example we can try to find out how many subjects like using the  new can opener by asking them how easy was to use or do you like the style or how this product make you feel after using it.

After this data is collected it can be transform into numbers of a percentage on how many of this people would buy  or not the new product. Any data collected in observation study, experiment or surveys can be transform into quantitative data if we eliminate the how, and when and concentrate in the how many.

 Business Research Methods (Eleventh Edition) D.Cooper/P.Schindler

Qualitative Data VS Quantitative Data

Qualitative Data VS Quantitative Data


The difference between qualitative data and quantitative data is that, qualitative answers the why? and the how? more in depth than quantitative data using people's hidden interpretation. Qualitative data helps understand the different meanings people place in their experience, which is something quantitative data can't answer completely. Also, qualitative data  can be collected in many different ways.

Some of the ways qualitative data can be collected is by using focus groups, individual depth interviews, and case studies. Using this types of data help the researcher extract feelings, emotions, motivations, perceptions and consumer language or self-described behavior. In the other hand quantitative data lack techniques of providing the insights needed to make those business decisions.

Quantitative data measures consumer behavior, which answer the questions related to how much, how often, how many, when and who. Quantitative data is collected more often thru survey and is more use for theory testing. One of the biggest difference between qualitative data and quantitative data is the interpretation. Qualitative data is reduce to numeral codes and maintain a clear distinction between facts and judgements, while qualitative data is more verbal.

Why do senior executives feel more comfortable relying on quantitative data than qualitative data?

Senior executives feel more comfortable relying on quantitative data, because qualitative data is too subject too human errors and bias in data collection and interpretation. They believe such data provides an unstable foundation for expensive and critical business decisions.

How qualitative research company can lessen the senior executives skepticism?

The best way to lessen the senior executives skepticism is by doing some of the following:

• Carefully using literature searches to build probing questions.
• Thoroughly justifying the methodology or combination of methodologies chosen.
• Executing the chosen methodology in its natural setting rather than a highly controlled setting.
• Choosing sample participants for relevance to the breadth of the issue rather than how well they represent the target population.
• Developing and including questions that reveal the exceptions to a rule or theory.
• Carefully structuring the data analysis.
• Comparing data across multiple sources and different context.
• Conducing peer-researcher debriefing on results for added clarity, additional insights, and reduced bias.

In conclusion, there are many reasons to believe that qualitative data takes less time to get results and is being use more and more in many companies, and you can't avoid human error by analyzing the data collected following the steps above.

Business Research Methods (Eleventh Edition) Donald R. Cooper/Pamela S. Schindler

Sinceramente Hallmark

"Sinceramente Hallmark"

In order to help my designers to create the new line of Hispanic cards I need to do some research to present to them so the can execute cards that the Hispanic community will buy. Some of the steps that must be follow are below:

1.  Research the four themes that we are trying to target.
2. Obtain general information about the Hispanic community.
3. Make a questionnaire with 20 questions about the Hispanic culture and send them to the top selling states where Hispanic cards are more sold.
4. Do an Internet research of the top holidays and celebrations in the Hispanic community.
5. Create a focus group  between the Hispanic community to ask things they will like to see in this cards.
6. Compile pictures that reflects the Hispanic culture and the four themes to present to the designers of the "Sinceramente Hallmark" line of greeting cards.

Once I collect this information and put all the ideas together in a presentation type of project, I can now present to the designers. Having  all the data collected for the new line of greeting cards and the must up to date research in the targeted community will help created the cards easily and will assure the selling of this cards. It is really important to know the target communities for the cards so the company can have a successful lunch of the "Sinceramente Hallmark" greeting cards.

Without this research is impossible to reach the Hispanic community and sell them the most inspiring greeting cards in their own language. The first steps in this research will make the designers jobs easy to execute and create an amazing product that the Hispanic community will run to buy.

"Theory is impractical and thus no good" vs "Good theory is the most practical approach to problems"

When reading this two different statements,  I agree more with "Good theory is the most practical approach to problems". The reason I take this position is because we need theory to resolve many problems in life. Theory makes everything more easy to understand and it helps predict what the outcome of any situation can possibly be. 

Theory helps to answer the "why" in many situations. Theory  evaluates the problem in different stages and find different solutions to give you more options to approach the problem.  Without theories so many businesses and big companies will not be successful today. 

Good theories makes us analyze the situation and make rational decisions. It helps us kind of predict what could happen and when. Not all theories are good but even the bad theories can point you to a better direction to approach the problem.

In conclusion "Theory" is the most practical approach to our every day dilemmas and help us find a better  solution to any problem in  life, jobs, family issues, and personal decisions.