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Marketing research involves the identification, collection, analysis, dissemination, and us of information about consumers, channel members, competitors, and changes and trends in the marketplace and other aspects in the firm’s environment.Defining the problem: Problem definition comes by discussions with decision makers, interviews with industry experts, analysis of secondary data and perhaps qualitative research. Develop An Approach to the Problem: Developing an approach to the problem includes formulating an analytical framework and models, research questions, and hypotheses.Formulating a Research Design: The blueprint for conducting marketing research. It details procedures needed to obtain the required information. Doing Field Work or Collecting Data: Data collection is accomplished using a staff that operates in the field. Field work involves personal, telephone, mail, or electronic interviewing. Preparing and Analyzing Data: Data processing steps leading up to analysis. This includes editing, coding, and transcribing of collected data. The verified for accuracy.Preparing and Presenting the Report: The entire project should be documented in a written report that addresses the specific research questions; describes the approach, the research design, data collection, and data analysis procedures; and presents the results and the major findings. It is supplemented by tables, figures, and graphs to enhance clarity and impact and is usually accompanied by a formal presentation.

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Exploratory: Primary objective is insight into and comprehension of the problem situation confronting the researcher. It has to be flexible and unstructured. Often conducted at the beginning stages of a project. Can be used in any point during the research process when the researcher is unclear about the problem situation.

Conclusive: Designed to assist the decision maker in determining, evaluating, and selecting the best course of action for a given situation. Can be used to verify the insights from exploratory research. More formal and structured than exploratory research.

Descriptive Research: Major objective the description of something-usually market characteristics of functions. It is particularly useful when research questions seek to describe a market phenomenon, such as determining purchases frequencies, identifying relationships, or making predictions. The researcher has prior knowledge of the problem situation.

Casual Research: Major objective is to obtain evidence regarding cause-and-effect relationships. Casual Design: A design in which that casual or independent variables are manipulated in a relatively controlled environment.

Cross Sectional Design: (Sample Survey) One time collection of information from any given sample of population elements.

Longitudinal Design: A fixed sample of population elements that is measured repeatedly. The sample remains the same over time, providing a series of pictures which m when viewed together, portray both the situation and the changes that are taking place.

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Nominal Ordinal Interval RatioBasic Characteristics

Numbers identify and classify objects

Numbers indicate the relative positions of the objects but not the magnitude of differences between them

Differences between objects can be compared; zero point is arbitrary

Zero point is fixed; ratios of scale values can be computed

Common Examples

Social Security numbers, numbers of football players

Quality rankings, rankings of teams in a tournament Temperature (Fahrenheit, Celsius) Length, weight

Marketing Examples

Brand numbers, store types, gender classification

Preference rankings, market position, social class Attitudes, opinions, index numbers Age, income, cost, sales, market shares

Permissible Statistics

Percentages mode Percentile median Range, mean, standard deviation Geometric mean, all statistics

Scale Characteristics

Description Description and Order Description, order and distance Description, order, distance and origin

DescriptionOrder, DistanceOrigin

Description: Unique labels or descriptors that are used to designate each value of the scale

Order: The relative sizes of positions of the descriptors; it is denoted by descriptors such as greater than, less than, and equal to.

Distance: The absolute differences between the scale descriptors are known and can be expressed in units.

Origin: A unique or fixed beginning or true zero point of a scale

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Comparative scales involve the direct comparison of two or more objects. For example, coke and Pepsi

Non Comparative scales (monadic or metric); objects are scaled independently of each other. For example, rate coke on a 1 to 7 preference scale (1=least pref/7= most pref)

Paired Comparison Rank Order Constant Sum Continuous Rating Scale Itemized Rating Scales has a number of brief descriptions associated with each response category.

Presentenced with a pair of alternatives and asked to select one based on some criterion

Simultaneously presented with several alternatives and asked to rank them according to some criterion

Allocate a constant sum of units, such as points, dollars, or chips, among a set of alternatives according to some specified criterion

Place a mark at any point along a line running between two extreme points rather than selecting from among a set of predetermined response categories. i.e. Worst……………I………………………Best 1 2 3 4 5 6 7 8

Likert: Indicate level of agreement. Strongly agree-Strongly disagree

Semantic Differential: 7 point rating scale with end points associated with bipolar labels that have semantic meaning

Stapel: A scale measuring attitudes that consist of a single adjective in the middle of an even-numbered range of values.

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Number of Scale Categories: The larger the number of categories contained in a scale, the finer the discrimination between the brands, alternatives, or other objects under study. The sensitivity of a scale is the ability to detect subtle differences in the attitude or the characteristic being measured. Increasing the number of scale categories increases sensitivity.Balanced vs. Unbalanced Scale: A scale with an equal number of favorable and unfavorable categoriesOdd or Even Numbers of Categories: Typically the midpoint represents the neutral category. Odd number of categories-The researcher believes a portion of the respondents is neutral on a certain subject. Even number of categories-The researcher believes there is no neutral category or wants a forced response.

Forced Rating Scale: A rating scale that forces the respondents to express an opinion because a “no opinion” option is not provided in the middle of the scale. However; placing a “No Opinion” on the outside of the scale could increase the validity.Nature and Degree of Verbal Description Describing a scale with verbal, numerical or pictorial description can have a considerable effect on responses. The strength of the adjectives used to anchor the scale also influences the responses. i.s. (Strongly disagree-Strongly agree) and (Generally disagree-Generally agree)Physical Form or Configuration Scales can be presented vertically or horizontally. Categories can be expressed by boxes, discrete lines, or units on a continuum and might or might not have numbers assigned to them. If numbers are used they can either positive or negative.

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Develop the construct: (The characteristic to be measured) A construct is a specific type of concept that exists at a higher level of abstraction than do every day concepts, such as brand loyalty, product involvement, attitude, satisfaction and so forth.Develop a theoretical definition: This is the meaning or central idea of concept of interest. For this, we need an underlying theory of the construct being measured.Develop an operation definition: Specifies which observable characteristics will be measured and the process of assigning value to the construct.Develop a multi-item scale: Generate a set of questions based on theory, analysis of secondary date, and qualitative research. Reduce the set of questions based on judgment. Collect the data. Purify the scale based on statistical analysis.Evaluate scale reliability and validity: To understand these concepts it is useful to think of total measurement error as the sum of the systematic error and random error. Systematic Error: Affects the measurement in a constant way and represents stable factors that affect the observed score in the same way each time the measurement is made. Random Error: Measurement error that arises from random changes that have a different effect each time the measurement is made.

Total measurement error = Systematic error + Random errorEvaluation based on Reliability: The extent to which a scale produces consistent results if repeated measurements are made. The extent to which measures are free from random error.

Evaluation based on Validity: The extent to which differences in observed scale scores reflect true differences in what is being measured, rather than systematic or random error. A scale with perfect validity would contain no measurement error; that is, no systematic error and no random error.

Test-Retest reliability: Respondents are administered identical scales at two different times under as nearly equivalent condition as possible.Alternative Forms Reliability: 2 equivalent forms of the scale are constructed and used to measures the same respondents 2 different times.Internal Consistency Reliability: Used to assess the reliability of a summated scale and refers to the consistency with which each item represents the construct of interest.

Content Validity: Subjective but systematic evaluation of the representativeness of the content of a scale for the measuring task at hand.Criterion Validity: Examines whether the measurement scale performs as expected in relations to other variables selected as meaningful criteria.Construct Validity: Addresses the question of what construct or characteristic the scale is measuring. An attempt is made to answer theoretical questions of why a scale works and what deductions can be made concerning the theory underlying the scale.

Convergent Validity: Measures the extent to which the scale correlates positively with other measures of the same construct.Discriminant Validity: Assess the extent to which a measure does not correlate with other constructs from which it is supposed to differ.Nomo logical Validity: Assesses the relationship between theoretical constructs. It seeks to confirm significant correlations between the constructs as predicted by theory.

Apply the scale and accumulate research findings: In selecting scaling techniques for a research problem, the researcher should consider the level of measurement desired (nominal, ordinal, interval, or ratio), the experience of the respondents with the research topic, difficulty of administering the scales, and the context. The technique used should be the one that will yield the highest level of measurement feasible.

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Define the population:Element: Object or person from which the info is desired.Population: Total of all elements that share a common set of

Elements: Object or person from which the info is desired.Sampling Unit: Basic unit containing the elements of the population to be sampled.Extent: Geographical boundaries.Time Frame: Time period of

Determine the Sampling Frame: Representation of the elements of the target population. It consists of a list or set of directions for identifying the target population. Compiling the list of population elements often is difficult and imperfect, leading to Sampling Frame Error. Elements may be omitted or the list might contain more than the desired population. It is important to recognize sampling frame error so that inappropriate population inferences can be avoided.Select Sampling Technique(s): Choosing nonprobability or probability sampling.

Research Design

Conclusive Research

Descriptive Research

Cross sectional Design

Longitudinal Design

Casual Research

Exploratory Research Design

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characteristics. Census: Complete count of each element in the population.Sample: Subgroup of the population.Target Population: Collection of elements possessing required info the researcher wants.Target Population is divided into elements, sampling units, extent and time frame.

interest. Nonprobability Sampling relies on the personal judgment of the researcher, rather than chance, in selecting sample elements. Although nonprobability produces good estimates of population characteristics, these techniques are limited. It is not possible to objectively evaluate the precision of the sample results.Precision refers to the level of uncertainty about the characteristic being measured. The greater the precision, the smaller the sampling error. The probability of selecting one element over another is unknown. Therefore, the estimates obtained cannot be projected to the population with any specified level of confidence

.Probability Sampling: Elements are selected by chance (randomly). Each element of the population has the same (fixed) probabilistic chance of being selected for the sample.

Determine the sample size: The number of elements to be included in the survey.. Qualitative sample sizes considerations are importance of decision, nature of research, number of variables, nature of analysis, sample size in similar studies, and resource constraints. The more important the decision, the more precise the information must be. This implies the need for larger samples.Execute the Sampling Process: Implementing the various details of the sample design.

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Non-probability: Sampling techniques that do not use chance selection procedures, but that instead rely on the researcher’s personal judgment and/or convence.Convenience: Attempts to obtain a sample of convenient elements at the judgment of the sampler.Judgmental: Population elements are purposively selected based on the judgment of the researcher.Quota: Two stages restricted judgmental sampling: Stage 1 is developed control categories or quotas of population elements. Stage 2 is Sample elements are selected based on convenience or judgment.Snowball: An initial group is selected randomly. Subsequent respondents are selected based on the referrals or information provided by the initial respondents. This process may be carried out in waves by obtaining referrals from referrals.

Probability sampling Varies in terms of sampling efficiency. Sampling efficiency is a concept that reflects a trade-off between sampling cost and precision. Higher cost=Higher precision. Simple Random Sampling: Each element of the population has a known and equal probability of selection. Every element is selected independently of every other element, and the sample is drawn by a random procedure from a sampling frame.Systematic Sampling: Choose a random starting point, then pick every /th element in succession from the sampling frame.Stratified Sampling: 2 step process to partition the population into subpopulations (strata). Elements are selected from each stratum by a random procedure.Cluster Sampling: 2 step probability sampling technique. 1st: The target population is divided into mutually exclusive and collectively exhaustive subpopulations called clusters. 2nd. A random sample of clusters is selected based on probability sampling technique. For each selected cluster, either all the elements are included in the sample or a sample of elements is drawn probabilistically.

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Questionnaire Checking: Check for completeness and interview quality.Editing: A review of the questionnaires with the objective of increasing accuracy and precision. Coding: The assignment of a code to represent a specific response to a specific question, along with the data record and column position that code will occupy. Transcribing: Transferring the codded data from the questionnaires or coding sheets onto disks or directly into computers by keypunching or other means. Data Cleaning: Thorough and extensive checks for consistency and treatment of missing responses.

Selecting a Data Analysis Strategy: Based on the early steps of the marketing research process.Earlier steps in the process: Step 1: Problem Definition, Step 2: Development of an approach, Step 3: Research Design.Known Characteristics of Data: The measurement scales used exert a strong influence on the choice of statistical techniques. Research design might favor certain techniques.Properties of Statistical Techniques Some statistical techniques are appropriate for examining the differences in variables, others for assessing the magnitudes of the relationship between variables, and others are appropriate for making predictions.Univariate Techniques: Statistical techniques appropriate for analyzing data when there is a single measurement of each element in the sample or, if there are several measurements of each element, each variable is analyzed in isolation.

Multivariate Techniques: Statistical techniques suitable for analyzing data when there are two or more measurements on each element and the variables are analyzed simultaneously. Multivariate techniques are concerned with the simultaneous relationships around two or more phenomena.

Background and Philosophy of the Researcher: The experienced, statistically trained researcher will employ a range of techniques and data analysis strategy.

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Frequency Distribution of a single variable provides an indication of the number of out of range, missing, or extreme values, as well as insights into the central tendency and variability of the underlying distribution. The objective is to obtain a count of the number of responses associated with different values of the variable.

Value LabelValu

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Very Unfavorable

1 5 11.1 11.4 11.4

2 6 13.3 13.6 25

3 6 13.3 13.6 38.64 6 13.3 13.6 52.35 8 17.8 18.2 70.56 9 20 20.5 90.9

 Very Favorable 7 4 8.9 9.1 100

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Conducting Frequency AnalysisCalculate the frequency for each value of the variableCalculate the Percentage and cumulative percentage for each value, adjusting for any missing valuesPlot the Frequency HistogramCalculate the descriptive statistics, measures of Location, and Variability. Measures of Location: A statistic that describes a location within a data set. Measures of central tendency describe the center of the distributionMean: Average ValueMode: The value that occurs most frequently.Median: The value when the data are arranged in ascending or descending rank order.

1 2 3 4 5 6 70123456789

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Measures of Variability: Indicate the distribution’s dispersion

Range = X Largest−X Smallest Measures the

spread of the date.Variance and Standard Deviation: The difference between the mean and the observed value is called the deviation from the mean.Variance: The mean squared deviation from the mean, that is, the average of the square

of the deviations from the mean for all

values. Standard Deciation is the square

root of the variance.

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❑X = The mean

n = sample size

X i=¿Observed values of the variable X ¿X=(5x1)+(6x2)+(6x3)+(6x4)+(8x5)+(9x6)+(4x7)/44 =(5+12+18+24+40+54+28)/44 =181/44 =4.11

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