Quantitative Research

What is Quantitative Research?
Quantitative market research studies are designed to assess, predict, and estimate buyer attitudes and behaviors, used for market sizing, market segmentation, and uncovering "drivers" for brand and product preference
Quantitative research is about measuring a market and quantifying that measurement with data. Most often the data required relates to market size, market share, penetration, installed base and market growth rates.
However, quantitative research can also be used to measure customer attitudes, satisfaction, commitment and a range of other useful market data that can tracked over time.
Quantitative research can also be used to measure customer awareness and attitudes to different manufacturers and to understand overall customer behaviour in a market by taking a statistical sample of customers to understand the market as a whole. Such techniques are extremely powerful when combined with techniques such segmentation analysis and mean that key audiences can be targeted and monitored over time to ensure the optimal use of the marketing budget.
At the heart of all quantitative research is the statistical sample. Great care has to be taken in selecting the sample and also in the design of the sample questionnaire and the quality of the analysis of data collected.
Market research involves the collection of data to obtain insight and knowledge into the needs and wants of customers and the structure and dynamics of a market. In nearly all cases, it would be very costly and time-consuming to collect data from the entire population of a market. Accordingly, in market research, extensive use is made of sampling from which, through careful design and analysis, Marketers can draw information about the market.
Quantitative market research is numerically oriented, requires significant attention to the measurement of market phenomena and often involves statistical analysis. For example, a bank might ask its customers to rate its overall service as excellent, good, poor or very poor.
This will provide quantitative information that can be analysed statistically. The main rule with quantitative market research is that every respondent is asked the same series of questions. The approach is very structured and normally involves large numbers of interviews/questionnaires.

Perhaps the most common quantitative technique is the ‘market research survey’. These are basically projects that involve the collection of data from multiple cases – such as consumers or a set of products. Quantitative market research surveys can be conducted by using post (self-completion), face-to-face (in-street or in-home), telephone, email or web techniques. The questionnaire is one of the more common tools for collecting data from a survey, but it is only one of a wide ranging set of data collection aids.

Types of Quantitative Researches

•Descriptive: Descriptive research involves collecting data in order to test hypotheses or answer questions concerning the current status of the subjects of the study. It determines and reports the way things are.

•Correlational: Correlational research attempts to determine whether and to what degree a relationship exists between two or more quantifiable variables. However, it never establishes a cause-effect relationship. The relationship is expressed by correlation coefficient, which is a number between .00 and 1.00.

•Cause-comparative: Causal-comparative research: establishes the cause-effect relationship, compares the relationship, but the cause is not manipulated, such as "gender."

•Experimental: Experimental research establishes the cause-effect relationship and does the comparison, but the cause is manipulated. The cause, independent variable makes the difference. The effect, dependent variable is dependent on the independent variable.

Before Conducting a Quantitative Research
•Research Plan: Research plan must be completed before a study is begun. Why?
1.The plan makes a research to think;
2.A written plan facilitates evaluation of the proposed study;
3.The plan provides a guide for conducting the study.
Components of a Research Plan :
1.Introduction: It includes a statement of the problem, a review of related literature, and a statement of the hypothesis.
2.Method: This part includes subjects, instruments-- materials if appropriate, design procedure.
3.Data analysis: A description of the statistical technique or techniques that will be sued to analyze study data.
4.Time schedule: The time schedule is equally important for both beginning researchers working on the thesis or dissertation and for experienced researchers working under the deadlines of a research grant or contract. It basically includes a listing of major activities or phases of the proposed study and a corresponding expected completion time for each activity.
5.Budget: It should list all tentative expenses specifically and submitted to funding agency. It includes such items as personnel, clerical assistance, travel and postage and other expenses, equipment, and fringe benefits etc.

•Ethical Consideration:
THREE ethical considerations are:
1)The subjects should not be harmed in any way (physically or mentally) in the name of science. If an experiment involves any risk to subjects, they should be completely informed concerning the nature of the risk and the permission for participation in the experiment should be acquired in writing from the subjects themselves, or from persons legally responsible for the subjects if they are not of age. If school children are involved, it is a good idea to inform parents before the study is conducted if possible.
2)Subject’s privacy should be strictly confidential. Individual scores should never be reported, or made public.
3)Ethical principle in the conduct of research with human participants is the most definitive source of ethical guidelines for researcher. It is prepared and published by the American Psychological Association (APA). “.... with respect and concern for the dignity and welfare of the people who participate and with cognizance of federal and state regulations and professional standards governing the conduct of research with human participants.” That is “to respect and concern for the dignity and welfare of the people who participate.”

Basic Concepts of Quantitative Research
•Introduction
a.Defining a problem
b.Literature review
c.Hypotheses

•Method
a.Population and subjects
b.Instruments
c.Design and procedures

•Results
Data and statistics
1.Types of measurement scales
2.Descriptive statistics
1.Types of descriptive statistics
2.Calculation for interval data
Inferential statistics
1.Level of significance
2.Tests of significance
(a)Z test for independent variables
(b)Z test for dependent variables
(c)ANOVA

•Discussion
i)Interpretation of results
ii)Generalization
iii)Discussion of implications

•Conclusion and recommendation
i)Based on practical significance to draw conclusion and make suggestions.

Types of Hypothesis Tests use in Quantitative Research
This includes:
•Parametric tests of a single sample:
1.T test
2.Z test
•Parametric tests of two independent samples:
1.Two-group T test
2.Z test
•Parametric tests of paired samples:
1.Paired T test
•Nominal/ordinal level test of a single sample:
2.Chi-square
3.Kolmogorov-Smirnov one sample test
4.Runs test
5.Binomial test
•Nominal/ordinal level test of two independent samples:
1.Chi-square
2.Mann-Whitney U
3.Median
4.Kolmogorov-Smirnov two sample test
•Nominal/ordinal level test for paired samples:
1.Wilcoxon test
2.McNemar test


Point to remember:
•If a Variable (e.g. preference of the respondences on color of a product) is interval/ ratio scaled and meet some statistical assumption (e.g. Normality), then it is eligible for Parametric test.
•If a Variable (e.g. gender or rank order of few products on their certain attributes) is Nominal/ Ordinal scaled and/ or does not meet some statistical assumption (e.g. Normality), then it is not eligible for Parametric test. In this situation we have to use Non-parametric test.
We should use non-parametric test only if sample/ variable is not eligible for parametric test. Remember that, the non-parametric test is mostly used and misused technique in the world.

Inferential Techniques
Inferential techniques involve generalizing from a sample to the whole population. It also involves testing a hypothesis. A hypothesis must be stated in mathematical/statistical terms that make it possible to calculate the probability of possible samples assuming the hypothesis is correct. Then a test statistic must be chosen that will summarize the information in the sample that is relevant to the hypothesis. A null hypothesis is a hypothesis that is presumed true until a hypothesis test indicates otherwise. Typically it is a statement about parameter that is a property of a population. The parameter is often a mean or a standard deviation.
Not unusually, such a hypothesis states that the parameters, or mathematical characteristics, of two or more populations are identical. For example, if we want to compare the test scores of two random samples of men and women, the null hypothesis would be that the mean score in the male population from which the first sample was drawn, was the same as the mean score in the female population from which the second sample was drawn:
H0:μ1 = μ2

Where:
H0 = the null hypothesis
μ1 = the mean of population 1, and
μ2 = the mean of population 2.
The equality operator makes this a two-tailed test. The alternative hypothesis can be either greater than or less than the null hypothesis. In a one-tailed test, the operator is an inequality, and the alternative hypothesis has directionality:
H0:μ1 = or < μ2
These are sometimes called a hypothesis of significant difference because you are testing the difference between two groups with respect to one variable.
Alternatively, the null hypothesis can postulate that the two samples are drawn from the same population:
H0:μ1 − μ2 = 0
A hypothesis of association is where there is one population, but two traits being measured. It is a test of association of two traits within one group.
The distribution of the test statistic is used to calculate the probability sets of possible values (usually an interval or union of intervals). Among all the sets of possible values, we must choose one that we think represents the most extreme evidence against the hypothesis. That is called the critical region of the test statistic. The probability of the test statistic falling in the critical region when the hypothesis is correct is called the alpha value of the test. After the data is available, the test statistic is calculated and we determine whether it is inside the critical region. If the test statistic is inside the critical region, then our conclusion is either the hypothesis is incorrect, or an event of probability less than or equal to alpha has occurred. If the test statistic is outside the critical region, the conclusion is that there is not enough evidence to reject the hypothesis.
The significance level of a test is the maximum probability of accidentally rejecting a true null hypothesis (a decision known as a Type I error).For example, one may choose a significance level of, say, 5%, and calculate a critical value of a statistic (such as the mean) so that the probability of it exceeding that value, given the truth of the null hypothesis, would be 5%. If the actual, calculated statistic value exceeds the critical value, then it is significant "at the 5% level".

Types of Errors
Random Sampling Errors:
•Sample too small
•Sample not representative
•Inappropriate sampling method used
•Random errors
Research design Errors:
•Bias introduced
•Measurement error
•Data analysis error
•Sampling frame error
•Population definition error
•Scaling error
•Question construction error

Interviewer Errors:
•Recording errors
•Cheating errors
•Questioning errors
•Respondent selection error
Respondent Errors:
•Non-response error
•Inability error
•Falsification error
Hypothesis Errors:
•Type I error (also called alpha error)
i)The study results lead to the rejection of the null hypothesis even though it is actually true
•Type II error (also called beta error)
1.The study results lead to the acceptance (non-rejection) of the null hypothesis even though it is actually false