RESEARCH.

RESEARCH

Research is the process of gathering information about a market, analyzing it, and interpreting it. Although management research often means the same thing, technically, it only refers to research into a specific market. Consumer research (used to discover behavior patterns, how people act, and customer needs) is an essential management research element. Motivation research investigates the psychological reasons why individuals respond to specific advertising appeals.

There are two main methods of consumer research:

• Desk (desktop) research or secondary research: an analysis of the information we can find easily without leaving our desk. Examples include the internet, books, newspapers, magazines, and online statistics.

• Field or primary research involves talking to people and determining their thoughts about a market.

Consumer research can be either qualitative or quantitative. In qualitative research, small group discussions or in-depth consumer interviews are used to understand a problem better. Quantitative research involves collecting or gathering large samples of data, followed by statistical analysis. Quantitative research is often used to investigate the findings of qualitative research.

Management Research

University Managers need the information to introduce products and services that create value in the customer's mind. However, the perception of value is subjective, and what customers value this year is quite different from what they love next year. As such, the attributes that create value cannot only be deduced from common knowledge. Data must be collected and analyzed. The research aims to provide the facts and direction managers need to make more critical marketing decisions.

To maximize the benefit of the research, we need to understand the research process and its limitations.

Management research deals with gathering information about a market's size and trends.

The value of information

The value of information can be helpful, but what determines its real value to the organization is:

• The ability and willingness to act on the information;

• The accuracy of the information;

• The level of indecisiveness that would exist without the information;

• The amount of variation in the possible results;

• The level of risk aversion;

• The reaction of competitors to any decision improved by the information;

• The cost of the information in terms of time and money.

The Management Research Process

Once the need for management research has been established, most research projects involve these steps:

• Define the problem;

• Determine research design (exploratory research, descriptive research, causal research);

• Identify data types and sources (secondary data, primary data);

• Design data collection forms and questionnaires (nominal, ordinal, interval, ratio scales, validity and reliability, attitude measurement);

• Determine the sample plan and size;

• Collect the data;

• Analyze and interpret the data;

• Prepare the research report.

STATISTICS

Conjoint Analysis

The conjoint analysis is a powerful technique for determining consumer preferences for product attributes.

Hypothesis Testing

Hypothesis Testing involves the following steps:

• Formulate the null and alternative hypotheses;

• Choose the appropriate test;

• Choose a level of significance (alpha) - determine the rejection region;

• Gather the data and calculate the test statistic;

• Determine the probability of the observed value of the test statistic under the null hypothesis given the sampling distribution that applies to the chosen test;

• Compare the value of the test statistic to the rejection threshold;

• Based on the comparison, reject or do not reject the null hypothesis;

• Make the marketing research conclusion.

Tests of Statistical Significance

The chi-square test determines whether a set of proportions has numerical values. It is often used to analyze bivariate cross-tabulated data. There is a mathematical function that has to be followed. Before calculating the chi-square value, we need to determine the expected frequency for each cell. It is done by dividing the number of samples by the number of cells in the table.

ANOVA

Another test of significance is the Analysis of Variance (ANOVA) test. The primary purpose of ANOVA is to test for differences between multiple means. Whereas the t-test can compare two means, ANOVA is needed to reach three or more means. If multiple t-tests were applied, the probability of a TYPE A error increases as the number of comparisons increases.

One-way ANOVA examines whether multiple means differ. The test is called an F-test. ANOVA calculates the ratio of the variation between groups to the variation within groups. We can use ANOVA for two purposes, too. Two-way ANOVA allows for a second independent variable and addresses interaction.

To run a one-way ANOVA, we use the following steps:

• Identify the independent and dependent variables;

• Describe the variation by breaking it into three parts (the total variation, the portion within groups, and the portion between groups).

• Measure the difference between each group's mean and the grand mean;

• Perform a significance test on the differences;

• Interpret the results.

This F-test assumes that the group variances are approximately equal and that the observations are independent. It also usually accepts distributed data; however, since this is a test, the Central Limit Function holds as long as the sample size is small.

ANOVA efficiently analyzes data using relatively few observations and can be used with categorical variables.

Factor Analysis

Factor analysis is a prevalent technique for analyzing interdependence. Factor analysis studies the entire set of interrelationships without defining variables to be dependent or independent. A Factor Analysis combines variables to create a smaller set of factors. Mathematically, an actor is a linear combination of variables. A factor is not directly observable; it is inferred from the variables. The technique identifies the variables' underlying structure, reducing the number of variables to a more manageable set—factor analysis groups variables according to their correlation.

Cluster Analysis

Segmentation usually is based not on one factor but on multiple factors. The challenge is to find a way to combine variables to form relatively homogeneous clusters. Such clusters should be internally homogeneous and externally heterogeneous. Cluster analysis is one way to accomplish this goal.

More than a statistical test, it is more a collection of algorithms for grouping objects or marketing research, grouping people. Cluster analysis is helpful in the exploratory research phase when there are no a-priori hypotheses.

• Identify the problem measure, collect data, and choose the variables to analyze;

• Choose a distance measure. The most common is the Euclidean distance. Other possibilities include the squared Euclidean distance, city-block distance, Chebyshev distance, power distance, and percent disagreement;

• Choose a clustering procedure linkage, nodal, or factor procedure;

• Determine the number of clusters. They should be well separated, and ideally, they should be distinct enough to give them descriptive names;

• Profile the clusters;

• Assess the validity of the clustering.

Research Report

• Authorization letter for the research;

• Table of contents;

• List of illustrations;

• Executive summary;

• Research objectives;

• Methodology;

• Results;

• Limitations;

• Conclusions and recommendations;

• Appendices containing copies of the questionnaires.

Questionnaire Design

• Determine which information is being sought;

• Choose a question type and method of administration;

• Determine the general question content needed to obtain the desired information;

• Determine the form of response;

• Choose the exact question wording;

• Arrange the questions into a compelling sequence;

• Specify the characteristics of the questionnaire;

• Test the questionnaire and revise it as needed.

Question Type and Administration Method

Some question types include fixed alternative or open-ended.

There are three standard rating scales: graphic, itemized, and comparative.

Here are some essential concepts to know

Central Tendency

The term central tendency refers to the data's middle value or typical value and is measured using the mean, median, or mode. Each measure is calculated differently, and the best use depends on the situation.

Dispersion

Without knowing something about how data is dispersed, measures of central tendency may be misleading. Measures of dispersion provide a complete picture. Dispersion measures include the range, average deviation, variance, and standard variation.

Probability

The classical interpretation of probability is a theoretical probability based on physics and experiment but does not require the experiment to be performed. Sometimes, a situation may be too complex to understand the physical nature of it well enough to calculate probabilities. However, we can estimate the probability by running many trials and observing the events. It is the empirical probability based on long-run relative frequencies. It is defined as the ratio of attended events favorable to the event divided by the total number of observed events. The larger the number of trials, the more accurate the probability estimate. If a computer can model the system, simulations can be performed in place of trials.

• Events;

• Law of addition;

• Conditional probability;

• Law of multiplication.

Permutations and Combinations

There are equations for determining how members of a set can be arranged, like the combinations of groups.