UNIT II DATA COLLECTION AND SOURCES

UNIT II DATA COLLECTION AND SOURCES

Measurements, Measurement Scales, Questionnaires and Instruments, Sampling
and methods. Data - Preparing, Exploring, examining and displaying.

1. MEASUREMENT
1. The Nature of Measurement
• Measurement in research consists of assigning numbers to empirical events,
objects or properties, or activities in compliance with a set of rules.
• This definition implies that measurement is a three-part process:
– Selecting observable empirical events
– Developing a set of mapping rules: a scheme for assigning numbers or
symbols to represent aspects of the event being measured
– Applying the mapping rule(s) to each observation of that event
Characteristics of Measurement

What Is Measured?
• Variables being studied in research may be classified as objects or as
properties.
• Objects include the concepts of ordinary experience, such as tangible items like
furniture, laundry detergent, people, or automobiles.
• Objects also include things that are not as concrete, such as genes, attitudes,
and peer-group pressures.
• Properties are the characteristics of the object. A person’s physical properties
may be stated in terms of weight, height, and posture, among others.
– Psychological properties include attitudes and intelligence.
– Social properties include leadership ability, class affiliation, and status.

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1. Measurement Scales
 In measuring, one devises some mapping rule and then translates the
observation of property indicants using this rule.
 Each one has its own set of underlying assumptions about how the
numerical symbols correspond to real-world observations.
 Mapping rules have four assumptions:
1. Numbers are used to classify, group, or sort responses. No order
exists.
2. Numbers are ordered. One number is greater than, less than, or
equal to another number.
3. Differences between numbers are ordered.
4. The difference between any pair of numbers is greater than, less
than, or equal to the difference between any other pair of numbers.
5. The number series has a unique origin indicated by the number
zero. This is an absolute and meaningful zero point.
Measurement Scales

Scales of Measurement

Nominal, Qualitative measures- Classify into non numeric

Ordinal categories

Interval Quantitative Measures-Measurement is numerical

Ratio
Nominal Scale
• Assign responses to different categories
• No numerical difference between categories
• Example
– Gender
– Student ID
– College major

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Ordinal scale
• Set of categories that are ordered from least to most
• Don’t know numerical distance from each category to the next
• Example
– Military rank
– Letter Grade in Class
Interval scale
• Scale with values, and there is the same numerical distance between each value
• This scale has an aribitrary zero point(no true meaningful zero point)
• Examples
– How appealing is this cereal box to children?
Not at all Very
-3 -2 -1 0 1 2 3
– Current temperature
– Many behavioral science questionnaires
Ratio Scale
• Scale with scores where there is the same numerical distance between each
score
• The scale has a true, meaning full zero point that anchors the scale
• Only scale that allows you to make ratio comparisons, such as “Maribel’s income
is 35 % more than Susan’s?
• Examples
– Weight of a packages of candy
– Amount of money in your checking account
– Number of questions correct on a quiz
3. Sources of Measurement Differences
• The ideal study should be designed and controlled for precise and unambiguous
measurement of the variables.
• Since complete control is unattainable, error does occur. Much error is
systematic (results from a bias), while the remainder is random (occurs
erratically).
• One authority has pointed out several sources from which measured differences
can come
Error Sources
• The Respondent
– Opinion differences that affect measurement come from relatively stable
characteristics of the respondent.
– Typical of these are employee status, ethnic group membership, social
class, and nearness to manufacturing facilities.

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– The skilled researcher will anticipate many of these dimensions,

adjusting the design to eliminate, neutralize, or otherwise deal with them.
– Respondents may also suffer from temporary factors like fatigue,
boredom, anxiety, hunger, impatience, or general variations in mood or
other distractions; these limit the ability to respond accurately and fully.
Situational Factors
• Any condition that places a strain on the interview or measurement session can
have serious effects on the interviewer-respondent rapport.
• If another person is present, that person can distort responses by joining in, by
distracting, or by merely being there.
The Measurer
• The interviewer can distort responses by rewording, paraphrasing, or
reordering questions.
• Stereotypes in appearance and action introduce bias. Inflections of voice and
conscious or unconscious prompting with smiles, nods, and so forth, may
encourage or discourage certain replies
The Instrument
• A defective instrument can cause distortion in two major ways. First, it can be
too confusing and ambiguous
• The use of complex words and syntax beyond participant comprehension is
typical. Leading questions, ambiguous meanings, mechanical defects
4. The Characteristics of Good Measurement
• There are three major criteria for evaluating a measurement tool: validity,
reliability, and practicality.
• Validity is the extent to which a test measures what we actually wish to
measure.
• Reliability has to do with the accuracy and precision of a measurement
procedure.
• Practicality is concerned with a wide range of factors of economy,
convenience, and interpretability
Validity
• Many forms of validity are mentioned in the research literature, and the number
grows as we expand the concern for more scientific measurement
• Internal validity is further limited in this discussion to the ability of a research
instrument to measure what it is purported to measure
• The external validity of research findings is the data’s ability to be generalized
across persons, settings, and times;

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• Validity consists of three major forms:

• Content validity,
• Criterion-related validity,
• Construct validity

Content Validity
– The content validity of a measuring instrument is the extent to which it
provides adequate coverage of the investigative questions guiding the
study
Criterion-Related Validity
• Criterion-related validity reflects the success of measures used for prediction or
estimation
• The researcher must ensure that the validity criterion used is itself “valid.”
• Any criterion measure must be judged in terms of four qualities:
– relevance,
– freedom from bias,
– reliability,
– Availability.

Construct Validity
• Construct validity is used to determine how well a test measures
• Construct validity is usually verified by comparing the test to other tests that
measure similar qualities to see how highly correlated the two measures
Reliability
• Reliability is a necessary contributor to validity but is not a sufficient condition
for validity.
• Reliability is concerned with estimates of the degree to which a measurement is
free of random or unstable error
Stability
• A measure is said to possess stability if you can secure consistent results with
repeated measurements of the same person with the same instrument
• Types
• Test–Retest
• Parallel Forms
• Split-Half
• Some of the difficulties that can occur in the test–retest methodology and cause
a downward bias in stability include:
– Time delay between measurements
– Insufficient time between measurements
– Respondent’s discernment of a study’s disguised purpose
– Topic sensitivity

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Equivalence
• Equivalence is concerned with variations at one point in time among observers
and samples of items.
Internal Consistency
• A third approach to reliability uses only one administration of an instrument or
test to assess the internal consistency or homogeneity among the items
Practicality
• Practicality has been defined as economy, convenience, and interpretability .
Economy
• Some trade-off usually occurs between the ideal research project and the
budget. Data are not free, and instrument length is one area where economic
pressures dominate
Convenience
• A measuring device passes the convenience test if it is easy to administer. A
questionnaire or a measurement scale with a set of detailed but clear
instructions.
Interpretability
• This aspect of practicality is relevant when persons other than the test
designers must interpret the results.
• A statement of the functions the test was designed to measure and the
procedures by which it was developed.
– Detailed instructions for administration.
– Scoring keys and instructions.
– Norms for appropriate reference groups.
– Evidence about reliability.
– Evidence regarding the inter correlations of sub scores.
– Evidence regarding the relationship of the test to other measures. Guides
for test use

2. MEASUREMENT SCALES
• Scales in business research are generally constructed to measure behavior,
knowledge, and attitudes.
• Attitude scales are among the most difficult to construct
1. The Nature of Attitudes
• An attitude is a learned, stable predisposition to respond to oneself, other
persons, objects, or issues in a consistently favorable or unfavorable way.
• cognitively based attitude
• Affectively based attitude
• behaviorally based attitudes

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The Relationship between Attitudes and Behavior

• Attitudes and behavioral intentions do not always lead to actual behaviors; and
although attitudes and behaviors are expected to be consistent with each other
• Several factors have an effect on the applicability of attitudinal research:
– Specific attitudes are better predictors of behavior than general ones.
– Strong attitudes are better predictors of behavior than weak attitudes
composed of little intensity or topical interest.
– Direct experiences with the attitude object produce behavior more
reliably.
– Cognitive-based attitudes influence behaviors better than affective-
based attitudes.
– Affective-based attitudes are often better predictors of consumption
behaviors
– The influence of reference group and the individual’s inclination to
conform to these influences improves the attitude-behavior linkage

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Attitude Scaling
• Attitude scales provide a quantitative measurement of attitudes, opinions or
values by summarizing numerical scores given by researchers to people's
responses to sets of statements exploring dimensions of an underlying theme.
2. Selecting a Measurement Scale
• Selecting and constructing a measurement scale requires the consideration of
several factors that influence the reliability, validity, and practicality of the
scale:
– Research objectives.
– Response types.
– Data properties.
– Number of dimensions.
– Balanced or unbalanced.
– Forced or unforced choices
– Number of scale points.
– Rater errors.
Research Objectives
• Researchers’ objectives are too numerous to list such as attitude change,
purchase intention
• Two general types of scaling objectives:
– To measure characteristics of the participants who participate in the
study.
– To use participants as judges of the objects or indicants presented to
them.
Response Types
• Measurement scales fall into one of four general types: rating, ranking,
categorization, and sorting.
• A rating scale is used when participants score an object or indicant without
making a direct comparison to another object or attitude
• Ranking scales constrain the study participant to making comparisons and
determining order among two or more properties or objects.
• Categorization asks participants to put themselves or property indicants in
groups or categories
• Sorting requires that participants sort cards into piles using criteria established
by the researcher
Data Properties
• Decisions about the choice of measurement scales are often made with regard
to the data properties generated by each scale.
• We classify scales in increasing order of power; scales are nominal, ordinal,
interval, or ratio.

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Number of Dimensions
 Measurement scales are either unidimensional or multidimensional.
o An unidimensional scale, one seeks to measure only one attribute of the
participant or object
o A multidimensional scale recognizes that an object might be better
described with several dimensions than on a unidimensional continuum
Balanced or Unbalanced
• A balanced rating scale has an equal number of categories above and below the
midpoint.
• Example
– very good—good—average—poor—very poor.”
• An unbalanced rating scale has an unequal number of favorable and
unfavorable response choices.
• Example
– “poor—fair—good— very good—excellent
Forced or Unforced Choices
• An unforced-choice rating scale provides participants with an opportunity to
express no opinion when they are unable to make a choice among the
alternatives offered.
• A forced-choice rating scale requires that participants select one of the offered
alternatives
• Example
• “no opinion,” “undecided,” “don’t know,” “uncertain,” or “neutral”
Number of Scale Points
• A scale should be appropriate for its purpose. For a scale to be useful, it should
match the stimulus presented and extract information proportionate to the
complexity of the attitude object, concept, or construct
Rater Errors
• Some raters are reluctant to give extreme judgments, and this fact accounts for
the error of central tendency.
• Participants may also be “easy raters” or “hard raters,” making what is called an
error of leniency.
3. Rating Scales
• We use rating scales to judge properties of objects without reference to other
similar objects.
• These ratings may be in such forms as “like—dislike,” “approve— indifferent—
disapprove,” or other classifications using even more categories
Simple Attitude Scales
• The simple category scale offers two mutually exclusive response choices.

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• When there are multiple options for the rater but only one answer is sought, the
multiple-choice, single-response scale is appropriate
• A variation, the multiple-choice, multiple-response scale (also called a
checklist ), allows the rater to select one or several alternatives
Likert Scales
• The Likert scale, developed by Rensis Likert the most frequently used variation
of the summated rating scale.
• Summated rating scales consist of statements that express either a favorable
or an unfavorable attitude toward the object of interest.

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Semantic Differential Scales

• The semantic differential (SD) scale measures the psychological meanings of an
attitude object using bipolar adjectives
• Researchers use this scale for studies such as brand and institutional image

Numerical/Multiple Rating List Scales

• Numerical scales have equal intervals that separate their numeric scale points
• A multiple rating list scale is similar to the numerical scale but differs in two
ways:
– It accepts a circled response from the rater the layout facilitates visualization
of the result
Stapel Scales
• The Stapel scale is used as an alternative to the semantic differential, especially
when it is difficult to find bipolar adjectives that match the investigative
question
Constant-Sum Scales
• A scale that helps the researcher discover proportions is the constant-sum scale

Graphic Rating Scales

• The graphic rating scale was originally created to enable researchers to discern
fine differences

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4. Ranking Scales
• In ranking scales, the participant directly compares two or more objects and
makes choices among them.
• Frequently, the participant is asked to select one as the “best” or the “most
preferred.”
• When there are only two choices, this approach is satisfactory.
• The forced ranking scale, some attributes that are ranked relative to each
other.
•The comparative scale is ideal for such comparisons if the participants are
familiar with the standard
5. Sorting
• Q-sorts require sorting of a deck of cards into piles that represent points
along a continuum.
• The participant—or judge—groups the cards based on his or her response
to the concept written on the card.
• Researchers using Q-sort resolve three special problems: item selection,
structured or unstructured choices in sorting, and data analysis.
• The basic Q-sort procedure involves the selection of a set of verbal
statements, phrases, single words, or photos related to the concept being
studied.
6. Cumulative Scales
• Total scores on cumulative scales have the same meaning.
• Given a person’s total score, it is possible to estimate which items were
answered positively and negatively.
• A pioneering scale of this type was the scalogram.
• Scalogram analysis is a procedure for determining whether a set of items
forms a unidimensional scale
• We are surveying opinions regarding a new style of running shoe. We have
developed a preference scale of four items:
1. The Airsole is good-looking.
2. I will insist on Airsole next time because it is great-looking.
3. The appearance of Airsole is acceptable to me.
4. I prefer the Airsole style to other styles.

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3. QUESTIONNAIRES AND INSTRUMENTS

• The questionnaire is the most common data collection instrument in business
research.

Phase 1: Revisiting the Research Question Hierarchy

• The management-research question hierarchy is the foundation of the research
process and also of successful instrument development
– Management question —the dilemma, stated in question form, that the
manager needs resolved.
– Research question(s) —the fact-based translation of the question the
researcher must answer to contribute to the solution of the management
question.
– Investigative questions —specific questions the researcher must
answer to provide sufficient detail and coverage of the research question.
– Measurement questions —questions participants must answer if the
researcher is to gather the needed information and resolve the
management question.
Flowchart for Instrument Design: Phase 1

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Type of Scale for Desired Analysis

• The analytical procedures available to the researcher are determined by the
scale types used in the survey
• We demonstrate how to code and extract the data from the instrument, select
appropriate descriptive measures or tests, and analyze the results
Communication Approach
• Communication-based research may be conducted by personal interview,
telephone, mail, computer (intranet and Internet), or some combination of
these (called hybrid studies ).
Disguising Objectives and Sponsors
• Disguised question is designed to conceal the question’s true purpose.
• Some degree of disguise is often present in survey questions, especially to
shield the study’s sponsor
– Willingly shared, conscious-level information.
– unwillingly shared, conscious-level information
– Knowable, limited-conscious-level information.
– Subconscious-level information
Preliminary Analysis Plan
• The preliminary analysis plan serves as a check on whether the planned
measurement questions meet the data needs of the research question.
• This also helps the researcher determine the type of scale needed for each
question—a preliminary step to developing measurement questions for
investigative questions.
Phase 2: Constructing and Refining the Measurement Questions
• Drafting or selecting questions begins once you develop a complete list of
investigative questions and decide on the collection processes to be used.
– Encourage each participant to provide accurate responses.
– Encourage each participant to provide an adequate amount of
information.
– Discourage each participant from refusing to answer specific questions.
– Discourage each participant from early discontinuation of participation.
– Leave the participant with a positive attitude about survey participation

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Question Categories and Structure

• Questionnaires and interview schedules can range from those that have a great
deal of structure to those that are essentially unstructured.
• Questionnaires contain three categories of measurement questions:
– Administrative questions.
– Classification questions.
– Target questions (structured or unstructured).
Question Content
• Question content is first and foremost dictated by the investigative questions
guiding the study
Major Issues Related to Measurement Questions
• Purposeful versus interesting
• Incomplete or unfocused
• Precision
• Time for thought
• Participation at the expense of accuracy
• Balance (general vs. specific)
• Objectivity
• Sensitive information
Question Wording
• Target questions need not be constructed solely of words. Computer-assisted,
computer-administered, and Web surveys and interview schedules, and to a
lesser extent printed surveys, often incorporate visual images as part of the
questioning process
• Major Issues Related to Measurement Questions
• Example
– Shared vocabulary
– Unsupported assumption
– Frame of reference
– Biased wording
– Personalization vs. projection
– Adequate alternatives
Response Strategy
– A third major decision area in question design is the degree and form of structure
imposed on the participant.
– unstructured response (or open ended response, the free choice of words)
– Structured response (or closed response, specified alternatives provided).
– Free responses, in turn, range from those in which the participants express
themselves extensively to those in which participants’ latitude is restricted by
space, layout.
– Objectives of the study.

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– Participant’s level of information about the topic.

– Degree to which participant has thought through the topic.
– Ease with which participant communicates.
– Participant’s motivation level to share information
Types of Responses strategy
• Free-Response Question
• Dichotomous Question
• Multiple-Choice Question
• Checklist
• Rating Question
• Ranking Question
• Sources of Existing Questions
– The tools of data collection should be adapted to the problem, not the
reverse. Thus, the focus of this chapter has been on crafting an
instrument to answer specific investigative questions
Phase 3: Drafting and Refining the Instrument
• Phase 3 of instrument design—drafting and refinement—is a multistep process:
– Develop the participant-screening process
– Arrange the measurement question sequence
– Prepare and insert instructions for the interviewer
– Create and insert a conclusion, including a survey disposition statement.
– Pretest specific questions and the instrument as a whole

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Participant Screening and Introduction

– The introduction also reveals the research organization or sponsor and
possibly the objective of the study. In personal or phone interviews as
well as in e-mail and Web surveys, the introduction usually contains one
or more screen questions or filter questions to determine if the potential
participant has the knowledge or experience necessary to participate in
the study
Measurement Question Sequencing
– The question process must quickly awaken interest and motivate the
participant to participate in the interview
– The participant should not be confronted by early requests for
information that might be considered personal or ego-threatening
– The questioning process should begin with simple items and then move
to the more complex,as well as move from general items to the more
specific
– Changes in the frame of reference should be small and should be clearly
pointed out
– Awaken Sensitive and
– Ego-Involving Information Interest and Motivation
– Simple to Complex
– General to Specific
Instructions
• Instructions to the interviewer or participant attempt to ensure that all
participants are treated equally, thus avoiding building error into the results
– Terminating an unqualified participant
– Terminating a discontinued interview
– Moving between questions on an instrument
– Disposing of a completed questionnaire
Conclusion
• The role of the conclusion is to leave the participant with the impression that
his or her involvement has been valuable. Subsequent researchers may need
this individual to participate in new studies.
Overcoming Instrument Problems
• There is no substitute for a thorough understanding of question wording,
question content, and question sequencing issues
– Build rapport with the participant.
– Redesign the questioning process
– Explore alternative response strategies.
– Use methods other than surveying to secure the data.
– Pretest all the survey elements.

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The Value of Pretesting

• The final step toward improving survey results is pretesting, the assessment of
questions and instruments before the start of a study
– Discovering ways to increase participant interest,
– Increasing the likelihood that participants will remain engaged to the
completion of the survey,
– Discovering question content, wording, and sequencing problems,
– Discovering target question groups where researcher training is needed.

4. SAMPLING AND METHODS

1. The Nature of Sampling
• The basic idea of sampling is that by selecting some of the elements in a
population, we may draw conclusions about the entire population.
• A population element is the individual participant or object on which the
measurement is taken.
• A population is the total collection of elements about which we wish to make
some inferences. All office workers in the firm compose a population of interest;
all 4,000 files define a population of interest.
• A census is a count of all the elements in a population. If 4,000 files define the
population, a census would obtain information from every one of them.
• We call the listing of all population elements from which the sample will be
drawn the sample frame.
Why Sample?
• There are several compelling reasons for sampling, including
– (1) lower cost,
– (2) greater accuracy of results,
– (3) greater speed of data collection,
– (4) availability of population elements
Sample versus Census
o Census refers to the quantitative research method, in which all the
members of the population are enumerated.
o Sampling is the widely used method, in statistical testing, wherein a data
set is selected from the large population, which represents the entire
group.

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What Is a Good Sample?

Accuracy
• Accuracy is the degree to which bias is absent from the sample.
• When the sample is drawn properly, the measure of behavior, attitudes, or
of some sample elements will be less than the measure of those same
variables drawn from the population.
• Systematic variance has been defined as “the variation in measures due to
some known or unknown influences that ‘cause’ the scores to lean in one
direction more than another.
• Precision is measured by the standard error of estimate, a type of standard
deviation measurement; the smaller the standard error of estimate, the
higher is the precision of the sample
Types of Sample Design

• Nonprobability sampling is arbitrary and subjective; when we choose

subjectively, we usually do so with a pattern or scheme in mind
• Probability sampling is based on the concept of random selection—a
controlled procedure that assures that each population element is given a
known nonzero chance of selection

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2. Steps in Sampling Design

What Is the Target Population?
• The definition of the population may be apparent from the management
problem or the research question
• There also may be confusion about whether the population consists of
individuals, households, or families, or a combination of these.

What Are the Parameters of Interest?

• Population parameters are summary descriptors of variables of
interest in the population.
• Sample statistics are descriptors of those same relevant variables
computed from sample data.
• The population proportion of incidence “is equal to the number of
elements in the population belonging to the category of interest

What Is the Sampling Frame?

• The sampling frame is closely related to the population. It is the list of
elements from which the sample is actually drawn.

What Is the Appropriate Sampling Method?

The researcher faces a basic choice: a Probability or non probability sample.
– Interviewers or others cannot modify the selections made.
– Only the selected elements from the original sampling frame are
included.
– Substitutions are excluded except as clearly specified and controlled
according to predetermined decision rules

What Size Sample Is Needed?

• A sample must be large or it is not representative
• A sample should bear some proportional relationship to the size of the
population from which it is drawn.

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3. Probability Sampling
Simple Random Sampling
• The unrestricted, simple random sample is the purest form of
probability sampling

Complex Probability Sampling

• Simple random sampling is often impractical. Reasons include
– (1) it requires a population list (sampling frame) that is often
not available;
– (2) it fails to use all the information about a population, thus
resulting in a design that may be wasteful; and
– (3) it may be expensive to implement in both time and money
Systematic Sampling
• A versatile form of probability sampling is systematic sampling

Cluster Sampling
• Population is divided into internally subgroups. Some are randomly selected for
further study.
Area Sampling
• Much research involves populations that can be identifi ed with some
geographic area. When this occurs, it is possible to use area sampling
Double Sampling
 It is Process includes collecting data from a sample using a previously defined
technique. Based on the information found, a subsample is selected for further
study.

4. Nonprobability Sampling
• The probability of selecting population elements is unknown.
• There are a variety of ways to choose persons or cases to include in the sample

Practical Considerations
• We may use nonprobability sampling procedures because they satisfactorily
meet the sampling objectives.

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Methods
– Convenience-Nonprobability samples that are unrestricted are called
convenience samples.
– Judgment sampling occurs when a researcher selects sample members
to conform to some criterion.
– Quota sampling -The logic behind quota sampling is that certain
relevant characteristics describe the dimensions of the population
– Snowball-It is also especially appropriate for some qualitative studies.
– In the initial stage of snowball sampling, individuals are discovered and
may or may not be selected through probability methods.

5. DATA – PREPARING
1. Introduction
• Data preparation includes editing, coding, and data entry and is the activity that
ensures the accuracy of the data and their conversion from raw form to reduced
and classified forms that are more appropriate for analysis
2. Editing
• The customary first step in analysis is to edit the raw data. Editing detects
errors and omissions, corrects them when possible, and certifies that maximum
data quality standards are achieved.
– Accurate.
– Consistent with the intent of the question and other information in the
survey.
– Uniformly entered
–

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Field Editing
• field editing review is a responsibility of the fi eld supervisor. It, too, should be
done soon after the data have been gathered. During the stress of data collection
in a personal interview and paper-and-pencil recording in an observation, the
researcher often uses ad hoc abbreviations and special symbols.
Central Editing
• At this point, the data should get a thorough editing. For a small study, the use
of a single editor produces maximum consistency

3. Coding
• Coding involves assigning numbers or other symbols to answers so that the
responses can be grouped into a limited number of categories. In coding,
categories are the partitions of a data set of a given variable (e.g., if the variable
is gender, the partitions are male and female ).
• Categorization is the process of using rules to partition a body of data. Both
closed- and open-response questions must be coded.
Codebook Construction
• A codebook, or coding scheme, contains each variable in the study and specifies
the application of coding rules to the variable

Coding Closed Questions

• The responses to closed questions include scaled items for which answers can
be anticipated. Closed questions are favored by researchers over open-ended
questions for their efficiency and specificity

Coding Open-Ended Questions

• One of the primary reasons for using open-ended questions is that insufficient
information or lack of a hypothesis may prohibit preparing response categories
in advance.

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Coding Rules
• Four rules guide the pre- and post coding and categorization of a data set.
– Appropriate to the research problem and purpose.
– Exhaustive.
– Mutually exclusive.
– Derived from one classification dimension
Using Content Analysis for Open Questions
• Increasingly text-based responses to open-ended measurement questions are
analyzed with content analysis software. Content analysis measures the
semantic content or the what aspect of a message
Types of Content
• Syntactical units
• Referential units
• Propositional units
• Thematic units

What Content Is Analyzed?

• Content analysis may be used to analyze written, audio, or video data from
experiments, observations, surveys, and secondary data studies.

Don’t Know” Responses

• The “don’t know” (DK) response presents special problems for data
preparation. When the DK response group is small, it is not troublesome

Dealing with Undesired DK Responses

• The best way to deal with undesired DK answers is to design better
measurement questions.
• Researchers should identify the questions for which a DK response is
unsatisfactory and design around it

Missing Data
• Missing data are information from a participant or case that is not available for
one or more variables of interest. In survey studies, missing data typically occur
when participants accidentally skip, refuse to answer, or do not know the
answer to an item on the questionnaire

Mechanisms for Dealing with Missing Data

 Data missing completely at random (MCAR)
 Data missing at random (MAR)

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 Data missing but not missing at random (NMAR)

4. Data Entry
• Data entry converts information gathered by secondary or primary methods to
a medium for viewing and manipulation
• Alternative Data Entry Formats
– Keyboarding
• Database Development
• Spreadsheet
– Optical Recognition
– optical character recognition (OCR)
• Optical scanning
• optical mark recognition (OMR)
– Voice Recognition
– Digital
• Bar Code
• On the Horizon
– Even with these time reductions between data collection and analysis,
continuing innovations in multimedia technology are being developed by
the personal computer business

6. EXPLORING, DISPLAYING AND EXAMINING DATA

1. Exploratory Data Analysis

• Exploratory Data Analysis (EDA) the researcher has the flexibility to respond
to the patterns revealed in the preliminary analysis of the data.
• Thus, patterns in the collected data guide the data analysis or suggest revisions
to the preliminary data analysis plan
• Confirmatory data analysis is an analytical process guided by classical statistical
inference in its use of significance testing and confidence

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Data Exploration, Examination, and Analysis in the Research Process

Frequency Tables, Bar Charts, and Pie Charts

 Several useful techniques for displaying data are not new to EDA.
 They are essential to any examination of the data. For example, a frequency
table is a simple device for arraying data

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• The values and percentages are more readily understood in this graphic format,
and visualization of the media placements and their relative sizes is improved

Histograms
• Histograms are used when it is possible to group the variable’s values into
intervals.

Stem-and-Leaf Displays
• The stem-and-leaf display is a technique that is closely related to the histogram.
It shares some of the histogram’s features but offers several unique advantages
• Example
– Each line or row in this display is referred to as a stem, and each piece of
information on the stem is called a leaf. The first line or row is
5|455666788889
• The meaning attached to this line or row is that there are 12 items in the data
set whose first digit is five:
– 54, 55, 55, 56, 56, 56, 57, 58, 58, 58, 58, and 59.

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Pareto Diagrams
• The Pareto diagram is a bar chart whose percentages sum to 100 percent. The
data are derived from a multiple-choice, single-response scale; a multiple-
choice, multiple-response scale; or frequency counts of words (or themes) from
content analysis

Mapping
• Increasingly, participant data are being attached to their geographic dimension
as Geographic Information System (GIS) software and coordinate measuring
devices have become more affordable and easier to use. Essentially a GIS works
by linking data sets to each other with at least one common data field (e.g., a
household’s street address).

2. Cross-Tabulation
• Cross-tabulation is a technique for comparing data from two or more
categorical variables such as gender and selection by one’s company for an
overseas assignment. Cross-tabulation is used with demographic variables and
the study’s target variables

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• The technique uses tables having rows and columns that correspond to the
levels or code values of each variable’s categories.
• This table has two rows for gender and two columns for assignment selection.
The combination of the variables with their values produces four cells. Each cell
contains a count of the cases of the joint classification and also the row, column,
and total percentages.

The Use of Percentages

• Percentages serve two purposes in data presentation. First, they simplify the
data by reducing all numbers to a range from 0 to 100. Second, they translate
the data into standard form, with a base of 100, for relative comparisons.

• Percentages are used by virtually everyone dealing with number. The following
guidelines, if used during analysis will help to prevent errors in reporting
– Averaging percentages
– Use of too large percentages
– large percentage
– Using too small a base
– Percentage decreases can never exceed 100 percent

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Other Table-Based Analysis

• The recognition of a meaningful relationship between variables generally
signals a need for further investigation. Even if one finds a statistically
significant relationship, the questions of why and under what conditions
remain. The introduction of a control variable to interpret the relationship is
often necessary
• An advanced variation on n-way tables is automatic interaction detection
(AID). AID is a computerized statistical process that requires that the
researcher identify a dependent variable and a set of predictors or independent
variables

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