Accounting Research Methods: Measurement and Sampling
Metode Riset Akuntansi
Measurement and Sampling
Measurement
Measurement in research consists of
assigning numbers to empirical events,
objects, or properties, or activities in
compliance with a set of rules
Measurement
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Measurement Scales
Several types of measurement are
possible
Depends on what you assume about
mapping rule
Mapping rules have four characteristics:
Classification
Order
Distance
Origin
Types of Scales
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Levels of Measurement
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Levels of Measurement
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Levels of Measurement
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Levels of Measurement
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Sources of Error
Evaluating
Measurement Tools
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Evaluating
Measurement Tools
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
Two major forms:
External validity: data’s ability to be
generalized
Internal validity: the ability of a research
instrument to measure what it is purported
to measure
Validity Determinants
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Content Validity
The extent to which it provides
adequate coverage of the investigative
questions guiding the study
Increasing Content Validity
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Literature
Search
Expert
Interviews
Group
Interviews
Validity Determinants
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Construct Validity
Consider both theory and the
measuring instrument being used
Validity Determinants
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Criterion-Related Validity
Reflects the success of measures used
for prediction or estimation
Understanding
Validity and Reliability
Reliability Estimates
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Practicality
Economy
Interpretability
Convenience
Methods of Scaling
Rating scales
Have several response categories and
are used to elicit responses with regard
to the object, event, or person studied.
Ranking scales
Make comparisons between or among
objects, events, persons and elicit the
preferred choices and ranking among
them.
Simple Category/Dichotomous
Scale
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Nominal Data
Multiple-Choice, Single Response
Scale
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Nominal Data
Multiple-Choice, Multiple
Response Scale
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Nominal Data
Likert Scale
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Interval Data
Semantic Differential
Interval Data
Numerical Scale
Ordinal or Interval Data
Multiple Rating List Scales
Interval Data
Stapel Scales
Interval Data
Constant-Sum Scales
Interval Data
Graphic Rating Scales
Interval Data
Ranking Scales
Paired-comparison scale
Forced ranking scale
Comparative scale
Paired-Comparison Scale
Ordinal Data
Forced Ranking Scale
Ordinal Data
Comparative Scale
Ordinal or Interval Data
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
The Nature of Sampling
Population element: the individual participant
or object on which the measurement is taken
Population: total collection of elements about
which we wish to make some inferences
Census: a count of all the elements in a
population
Sample frame: listing of all population
elements from which the sample will be
drawn
Why Sample?
Greater
accuracy
Availability of
elements
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Lower cost
What Is A Good Sample?
Precision
Accuracy
Accuracy
Accuracy is the degree to which bias is
absent from the sample
Systematic variance
Increasing the sample size
Precision
A measure of how closely the sample
represents the population
Measured by the standard error of
estimate
Sampling Designs
Probability sampling
Elements in the population have some
known chance or probability of being
selected as sample subjects
Nonprobability sampling
Elements do not have known or
predetermined chance of being selected as
subjects
Types of Sampling Designs
Simple Random
Purest form of probability sampling
Simple Random
Advantages
Easy to implement
Disadvantages
Requires list of
population elements
Time consuming
Can require larger
sample sizes
Systematic
Every
k
th element in the population is
sampled, beginning with a random start
of an element in the range of 1 to
k
Systematic
Advantages
Simple to design
Easier than simple
random
Disadvantages
Periodicity within
population may
skew sample and
results
Trends in list may
bias results
Stratified
The process by which the sample is
constrained to include elements from
each of the segments
Stratified
Advantages
Increased statistical
efficiency
Provides data to
represent and
analyze subgroups
Enables use of
different methods in
strata
Disadvantages
Especially expensive if
strata on population
must be created
Stratified
Proportionate: sample drawn from the
stratum is proportionate to the
stratum’s share of the total population
Disproportionate
Cluster
Advantages
Economically more
efficient than simple
random
Easy to do without
list
Disadvantages
Often lower statistical
efficiency due to
subgroups being
homogeneous rather
than heterogeneous
Stratified and Cluster Sampling
Stratified
Population divided
into few subgroups
Homogeneity within
subgroups
Heterogeneity
between subgroups
Choice of elements
from within each
subgroup
Cluster
Population divided
into many
subgroups
Heterogeneity
within subgroups
Homogeneity
between subgroups
Random choice of
subgroups
Area Sampling
Double
It may be more convenient or
economical to collect some information
by sample and then use this
information as the basis for selecting a
subsample for further study
Double
Advantages
May reduce costs if
first stage results in
enough data to
stratify or cluster
the population
Disadvantages
Increased costs if
discriminately used
Nonprobability Sampling
Cost
Feasibility
I
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s
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s
No need to
generalize
Limited
objectives
Nonprobability
Sampling Methods
Convenience
Judgment
Quota
Snowball
Convenience
Collection of information from members
of the population who are conveniently
available to provide it
Purposive
Conform to some criteria set by the
researcher
Judgment sampling
Quota sampling
Snowball
Individuals are discovered and this
group is then used to refer the
researcher to others that possess
similar characteristics and who, in turn,
will identify others
Measurement is a crucial aspect of research in accounting. It involves assigning numbers to empirical events, objects, or properties. Measurement consists of selecting measurable phenomena, developing mapping rules, and applying these rules appropriately. The process of measurement includes determining measurement scales and understanding different types of scales such as nominal, ordinal, interval, and ratio. Levels of measurement classification further elaborate on the characteristics of scales. Evaluating measurement tools involves assessing validity, reliability, and practicality to ensure accurate and meaningful results.
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Presentation Transcript
Metode Riset Akuntansi Measurement and Sampling
Measurement Measurement in research consists of assigning numbers to empirical events, objects, or properties, or activities in compliance with a set of rules
Measurement Selecting measurable phenomena Developing a set of mapping rules Applying the mapping rule to each phenomenon
Measurement Scales Several types of measurement are possible Depends on what you assume about mapping rule Mapping rules have four characteristics: Classification Order Distance Origin
Types of Scales Nominal Ordinal Interval Ratio
Levels of Measurement Classification Nominal Ordinal Interval Ratio
Levels of Measurement Classification Nominal Classification Ordinal Order Interval Ratio
Levels of Measurement Classification Nominal Classification Ordinal Order Classification Distance Interval Order Ratio
Levels of Measurement Classification Nominal Classification Ordinal Order Classification Distance Interval Order Classification Distance Natural Origin Ratio Order
Sources of Error Respondent Situation Measurer Instrument
Evaluating Measurement Tools Validity Criteria Reliability Practicality
Evaluating Measurement Tools 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 Two major forms: External validity: data s ability to be generalized Internal validity: the ability of a research instrument to measure what it is purported to measure
Validity Determinants Content Criterion Construct
Content Validity The extent to which it provides adequate coverage of the investigative questions guiding the study
Increasing Content Validity Literature Search Group Interviews Content Expert Interviews
Validity Determinants Content Construct
Construct Validity Consider both theory and the measuring instrument being used
Validity Determinants Content Criterion Construct
Criterion-Related Validity Reflects the success of measures used for prediction or estimation
Understanding Validity and Reliability
Reliability Estimates Stability Internal Consistency Equivalence
Practicality Economy Convenience Interpretability
Methods of Scaling Rating scales Have several response categories and are used to elicit responses with regard to the object, event, or person studied. Ranking scales Make comparisons between or among objects, events, persons and elicit the preferred choices and ranking among them.
Simple Category/Dichotomous Scale I plan to purchase a MindWriter laptop in the 12 months. Yes No Nominal Data
Multiple-Choice, Single Response Scale What newspaper do you read most often for financial news? East City Gazette West City Tribune Regional newspaper National newspaper Other (specify:_____________) Nominal Data
Multiple-Choice, Multiple Response Scale What sources did you use when designing your new home? Please check all that apply. Online planning services Magazines Independent contractor/builder Designer Architect Other (specify:_____________) Nominal Data
Likert Scale The Internet is superior to traditional libraries for comprehensive searches. Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree Interval Data
Semantic Differential Interval Data
Numerical Scale Ordinal or Interval Data
Multiple Rating List Scales Interval Data
Stapel Scales Interval Data
Constant-Sum Scales Interval Data
Graphic Rating Scales Interval Data
Ranking Scales Paired-comparison scale Forced ranking scale Comparative scale
Paired-Comparison Scale Ordinal Data
Forced Ranking Scale Ordinal Data
Comparative Scale Ordinal or Interval Data
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
The Nature of Sampling Population element: the individual participant or object on which the measurement is taken Population: total collection of elements about which we wish to make some inferences Census: a count of all the elements in a population Sample frame: listing of all population elements from which the sample will be drawn
Why Sample? Availability of elements Lower cost Sampling provides Greater speed Greater accuracy
What Is A Good Sample? Accuracy Precision
Accuracy Accuracy is the degree to which bias is absent from the sample Systematic variance Increasing the sample size
Precision A measure of how closely the sample represents the population Measured by the standard error of estimate
Sampling Designs Probability sampling Elements in the population have some known chance or probability of being selected as sample subjects Nonprobability sampling Elements do not have known or predetermined chance of being selected as subjects
Types of Sampling Designs Element Selection Unrestricted Probability Nonprobability Simple random Convenience Restricted Complex random Purposive Systematic Judgment Cluster Quota Stratified Snowball Double
Simple Random Purest form of probability sampling
Simple Random Advantages Easy to implement Disadvantages Requires list of population elements Time consuming Can require larger sample sizes
Systematic Every kth element in the population is sampled, beginning with a random start of an element in the range of 1 to k
Systematic Advantages Simple to design Easier than simple random Disadvantages Periodicity within population may skew sample and results Trends in list may bias results
