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Likert Scale: Explored and Explained

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Likert scale is applied as one of the most fundamental and frequently used psychometric tools in educational and social sciences research. Simultaneously, it is also subjected to a lot of debates and controversies in regards with the analysis and inclusion of points on the scale. With this context, through reviewing the available literature and then clubbing the received information with coherent scientific thinking, this paper attempts to gradually build a construct around Likert scale. This analytical review begins with the necessity of psychometric tools like Likert scale andits variants and focuses on some convoluted issues like validity, reliability and analysis of the scale.
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*Corresponding author: E-mail: drankurjoshi7@gmail.com;
British Journal of Applied Science & Technology
7(4): 396-403, 2015, Article no.BJAST.2015.157
ISSN: 2231-0843
SCIENCEDOMAIN international
www.sciencedomain.org
Likert Scale: Explored and Explained
Ankur Joshi
1*
, Saket Kale
2
, Satish Chandel
3
and D. K. Pal
1
1
Department of Community Medicine, Gandhi Medical College, Bhopal, 462001,
India.
2
Technical Support Unit, Madhya Pradesh State AIDS Control Society, India.
3
Department of Pharmacology, All India Institute of Medical Sciences, Bhopal,
India.
Authors’ contributions
This work was carried out in collaboration between all authors. Author AJ initiated the idea, wrote the
first draft and contributed in further refinement with critical inputs to literature review. Author SK
conceptualized the variation of Likert scale and provided critical input to the several drafts of
manuscript and literature review. Author SC contributed for common understanding of psychometrics
with critical inputs. Author DKP reviewed and facilitated the final shape of paper. All authors read and
approved the final manuscript.
Article Information
DOI: 10.9734/BJAST/2015/14975
Editor(s):
(1)
Meng Ma, Anhui University, Hefei, Anhui, China and Icahn Institute for Genomics and Multiscale Biology, Icahn School of
Medicine at Mount Sinai, New York, USA.
Reviewers:
(1)
Anonymous, USA.
(2)
Adalberto Campo-Arias, Faculty of Researches and Publications, Human Behavioral Research Institute, Bogota, Colombia.
(3)
David Magis, Department of Education, University of Liège, Belgium.
(4)
Anonymous, Croatia.
(5)
Anonymous, Canada.
Complete Peer review History:
http://www.sciencedomain.org/review-history.php?iid=773&id=5&aid=8206
Received 30
th
October 2014
Accepted 27
th
January 2015
Published 20
th
February 2015
ABSTRACT
Likert scale is applied as one of the most fundamental and frequently used psychometric tools in
educational and social sciences research. Simultaneously, it is also subjected to a lot of debates
and controversies in regards with the analysis and inclusion of points on the scale. With this
context, through reviewing the available literature and then clubbing the received information with
coherent scientific thinking, this paper attempts to gradually build a construct around Likert scale.
This analytical review begins with the necessity of psychometric tools like Likert scale andits
variants and focuses on some convoluted issues like validity, reliability and analysis of the scale.
Keywords: Psychometrics; Likert scale; points on scale; analysis; education.
Opinion Article
Joshi et al.; BJAST, 7(4): 396-403, 2015; Article no.BJAST.2015.157
397
1. INTRODUCTION
Nothing is more than a fear you cannot name.
― Cornelia Funke, Inkheart
Since the inception of human race there is an
inclination to capture the ethereal attributes of
human behaviour and performance.
Simultaneously, it has been a challenge from the
same time to quantify the thing which cannot be
measured through conventional measurement
techniques. The perceived need of this
quantification lies in the necessity to transform an
individual's subjectivity into an objective reality.
Attitude, perceptions and opinions are such
qualitative attributes amenable for quantitative
transformation due to above mention reason.
Qualitative research techniques do try to
compensate, by depicting the complexity of
human thoughts, feelings and outlooks through
several social science techniques, still the
quantification of these traits remains a
requirement and that’s how psychometric
techniques come into picture.
2. PSYCHOMETRICS AND LIKERT SCALE
Psychometrics techniques are being developed,
instituted and refined in order to meet the
quantification of traits like ability, perceptions,
qualities and outlooks- the requirement of social
sciences and educational researches [1,2].
Psychometrics operates through two ways; the
first is to formulate approaches (theoretical
construct) for measurements, followed by
development of measuring instruments and their
validation. Stanford Binet test (measures human
intelligence) and Minnesota Multiphasic
Personality Inventory (measures human
personality) are the example for the same. The
content in such instruments are rather ‘pre-fixed’
[3,4,5]. The another path is same up to
formulation of theoretical construct for the
measurement. This conceptualization is followed
by operational assembly of abstract
ideas/experiences/issues under investigation into
some statements (items) largely guided by the
aim of the study. This permits the contents
(items) in such scales/models to be rather
flexible and need based. Rasch measurement
model (use for estimation of ability), Likert scale
(measures human attitude) are the examples of
such scales in Psychometrics used widely in the
social science & educational research [3,4,5].
Likert scale was devised in order to measure
‘attitude’ in a scientifically accepted and validated
manner in 1932 [6,7]. An attitude can be defined
as preferential ways of behaving/reacting in a
specific circumstance rooted in relatively
enduring organization of belief and ideas (around
an object, a subject or a concept) acquired
through social interactions [8]. This is clear from
this discourse mentioned above that thinking
(cognition), feeling (affective) and action
(psychomotor) all together in various
combination/permutation constitute delivery of
attitude in a specified condition. The issue is how
to quantify these subjective preferential thinking,
feeling and action in a validated and reliable
manner: a help is offered by Likert scale [9,10].
The original Likert scale is a set of statements
(items) offered for a real or hypothetical situation
under study. Participants are asked to show their
level of agreement (from strongly disagree to
strongly agree) with the given statement (items)
on a metric scale. Here all the statements in
combination reveal the specific dimension of the
attitude towards the issue, hence, necessarily
inter-linked with each other [11].
With this context, this exploratory article attempts
to describe two confusing issues related with
Likert scale- (would be) preferable numbers of
points on a scale and analysis of the scale.
During one of the contributing authors’
participation in a web based conversational
learning forum on medical education. These two
issues emerged as thrust area amenable for
further exploration and lucid explanation for the
educational researchers. An initial literature
searched by authors led to aggregation of mutual
conflicting evidences which compelled us to re-
explore and further construct arguments based
upon accumulated knowledge.
3. LIKERT SCALE AND ITS VARIATION
Before proceeding further, let’s have a brief look
on several constructional diversities of a Likert
scale as the analytical treatment and
interpretation with Likert scale largely depends
upon these diversities.-Symmetric versus
asymmetric Likert scale- If the position of
neutrality (neutral/don't know) lies exactly in
between two extremes of strongly disagree (SD)
to strongly agree (SA), it provides independence
to a participant to choose any response in a
balanced and symmetric way in either directions.
This construction is known as symmetric scale.
On the other hand, asymmetric Likert scale offer
less choices on one side of neutrality (average)
as compared to other side. Asymmetric scale in
Joshi et al.; BJAST, 7(4): 396-403, 2015; Article no.BJAST.2015.157
398
some cases also indicatesipsative (forced)
choices where there is no perceived value of
indifference/neutrality of the researcher
[12, 13,14].
Seven /ten point scale - They are the variation of
5 point scale in which adjacent options are less
radically different(or more gradually different)
from each other as compare to a 5 point scale.
This larger (step by step) spectrum of choices
offers more independence to a participant to
pick the exact’ one (which he prefers most)
rather than to pick some ‘nearby’ or ‘close’ option
[15]. These variations are discussed in more
details (in reference with validity and reliability)
further in this paper.
Likert and Likert type scale- The construction of
Likert (or Likert type) scale is rooted into the aim
of the research Sometimes the purpose of the
research is to understand about the
opinions/perceptions of participants related with
single ‘latent’ variable (phenomenon of interest)
.This ‘latent’ variable is expressed by several
‘manifested’ items in the questionnaire. These
constructed items in a mutually exclusive manner
address a specific dimension of phenomenon
under inquiry and in cohesion measure the whole
phenomena. Here during analysis, the scores of
the all items of the questionnaire are combined
(sum) to generate a composite score, which
logically in totality measures anuni-dimensional
trait. This instrument is known as Likert scale.
Sometimes the primary interest of the researcher
is not to synthesize the stance of the participants
per se but to capture feelings, actions and
pragmatic opinion of the participants about
mutually exclusive issues around phenomenon/s
under study. This fact demands the individual
analysis of item to ascertain the participants’
collective degree of agreement around that
issue. The scale used so can be labeled as Likert
type and not Likert scale [16]. A word of caution;
this ‘direction of enquiry’ must be decided during
the planning phase and at least during the
designing of questionnaire and not at the time of
analysis.
4. IS 7 POINT LIKERT SCALE BETTER
THAN 5 POINT LIKERT SCALE? - A
PERSPECTIVE CONTROVERSY OR
ESTABLISHED WITH A CONSENSUS?
Since the advent of Likert scale in 1932, there
have been debates among the users about its
best possible usability in term of reliability and
validity of number of points on the scale [17-20].
Likert (1932,7) in his original paper, discussed
about the infinite number of definable attitudes
existing in a given person with possibility of
grouping them into “clusters” of responses. He
further conversed about the assumption of his
“survey of opinions” on which he provided his
results and psychological interpretations [21].
The key assumptions of his survey being firstly,
the presentation of item on scale are such that,
so as to allow the participants to choose clearly
opposed alternatives. Secondly, the conflicting
issues chosen were empirically important issues
thus, results themselves constituting an empirical
check on the degree of success.
Thus, it is argued in particular context of
clustering of attitudes. Considering reliability of
the responses from participants in a survey,
chances are that the 7 point scale may perform
better compared to 5 point scale owing to the
choice of items on scale defined by the construct
of survey. The 7 point scale provides more
varieties of options which in turn increase the
probability of meeting the objective reality of
people. As a 7-point scale reveals more
description about the motif and thus appeals
practically to the “faculty of reason” of the
participants [19,20].
A respondents’ absolute agreement with the
motif of topic may lie between the two descriptive
options provided on a 5 point scale. On repeated
administration, he/she may differ in choosing one
of the options, e.g. 3 instead of 4 when the
person thinks in between the two of the response
options on 5 point scale. A 7 point scale may
eliminate this problem up to an extent, by eliciting
retrieval beyond the utmost level of agreement
provided by a 5 point scale, the dilemma of
choosing between the two undesirable points on
5 point. Hence this dilemma of forced choosing
between two equally undesirable point imposed
by the 5-point Likert scale may be addressed up
to a extent by offering more choices (in between)
by a 7-point scale [22-24]. The provision of
number of scale points, 5 point or 7 point, would
be more engaging to the minds of respondents
when the items on the scale carry the statement
of ideas near the truth of the universe for both
the participants and the surveyor. It may create
the ‘curves of reliability’ around the ‘zenith of
validity’. The dilemma of choice and explicit
greater extent of measurement by 7 point scale
is very much in the territory of the reason of
Joshi et al.; BJAST, 7(4): 396-403, 2015; Article no.BJAST.2015.157
399
response without which consideration of
reliability is of no weight [19].
Validity of Likert scale is driven by the
applicability of the topic concerned; in context of
respondents’ understanding and judged by
creator of the response item. We can appreciate
it by an example: “How efficacious is a
therapeutic modality in treating a particular
disease?” This question when asked to a group
of individuals, indifferent with the disease or the
modality, the response pattern may remain
similar, independent of the number of point on
the scale. The responses may cluster around
center or to the extreme ends. On the contrary,
when the topic concerned is relevant to the
respondents’ context provision of more option,
may add to the content & construct validity of the
scale. Providing options more close to the
original view of the respondent reduce the role of
ambiguity in the responses [23,12]. Furthermore,
comprehension of all items and points on a scale
needs a judgment time and a memory span
different for different means and also depends on
communication mode. While listening to the
responses of a long scale may discern the
various options on the scale with lesser time to
judge compared to a written scale. Written scale
thus will add to validity even with more points on
the Likert scale. Also research concerning span
of immediate memory support this notion of
accuracy of response categories around seven,
as human mind has span of absolute judgment
that can distinguish 7 categories at a time [25].
5. ANALYSIS OF THE ITEM RESPONSE
Before we proceed to the method of analysis
available to Likert scale, a very fundamental but
equally controversial question should be
addressed- which type of scale Likert is?
There are two schools of thoughts - One school
considers Likert scale as ordinal and other treats
it as Interval scale. This conflict is primarily
rooted into the question: whether points on a
items are equivalent and equidistant? Points on
scale are not close enough to consider them
equal (in other words strongly agree is definitely
away from agree and agree is away from
neutral), they should be considered as non-
equivalent entity. There is an agreement in both
schools for the above fact. The conflict arises on
asking another question: if the points on scale
are non –equivalent, are they equi-distant (in
other words is ‘neutral of same distance from
‘agree’ as ‘agree’ from ‘strongly ‘agree’)? This
question is important as by answering of this
question only, one can decide whether Likert
scale can be treated as Interval scale?
The first school of researchers and statisticians
consider Likert scale as ordinal scale. They
argue that choices or responses are arranged in
some ranking order. However, as this scale
doesn’t show the relative magnitude and
distance between two responses quantitatively, it
can’t be treated as interval scale. The other
school interprets this dilemma from a different
perspective, stating that when the aim of the
researcher is to ‘combine’ all the items in order to
generate a ‘composite’ score for an individual
rather than separate analysis of single item
responded by all individuals, then this
individualistic summative score (for all the items)
of a participant shows a sensible realistic
distance from the individual summative score of
another individual; hence, can be labeled as
‘interval estimates’ [26,16].
To understand this concept, let’s assume a
scenario in which the aim of the researcher is to
measure the attitude towards classroom lectures
and to make out relative preferences (library
reading and small group teaching) compared
with lecture. (Fig. 1) He designs the following
survey instrument on a 5 point Likert scale for the
stated aim-
The first question of importance is: ‘Can these
items be clubbed (see together) in order to
generate a composite index for measuring the
attitude?’ In order to evaluate their
appropriateness for transformation into a single
composite index, following points can be
considered-
1. Whether the items are arranged in logical
sequence?
2. Whether the items are closely interrelated
but provide some independent information
as well?
3. Whether there is some element of
‘coherence/expectedness’ between
responses (whether next response can be
predicted up to some extent based upon
previous one)?
4. Whether each item measures a distinct
element of the issue?
Joshi et al.; BJAST, 7(4): 396-403, 2015; Article no.BJAST.2015.157
400
Fig. 1. Survey instrument for measuring attitude towards classroom lectures
Fig. 2. Choice of Analysis of Likert Items: Aim and Construct of Research
If answer to all the above questions is affirmative
for all the items of a set, they may be combined
to construct a composite index which measures
the collective stance of the participant towards
phenomenon under study. In the above example
as item 1, 2 and 3 fulfill all four criteria for each
other, they may be combined and can be treated
further in unison.
On the other hand, item-4 and item-5, offer
separate and sovereign (mutually exclusive)
preferences regarding two different teaching-
learning methods: self-directed reading and small
group teaching. Hence, they can’t be combined
and further they should be analyzed
independently from item 1, 2 and 3 and even
from each other.
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401
After this assertion of eligibility for combination,
the next question arises- On what scale can item
1, 2 and 3 be treated and what is the appropriate
measurement scale for item4 and item-5?
The answer of the above question lies in another
question asked by Stevens in his famous paper:
‘what are the rules (if any) under which numerals
are assigned?’ Here we see (a) the minimum
score one can secure for first three items is 3
(and not an absolute zero). The reason for this
apparently dislike for zero lies in the fact that in
psychometrics, attitude is preferably measured in
positive degree and being the ‘strongly disagree
‘cannot be equated with ‘absolute disagreement’;
there is always something below than strongly
disagree. Zero also gives the notion of neutrality
rather disagreement (the attitude is zero; means
one is apathetic to issue) (b) Each numeral
conveys the same meaning in all three items (i.e.
3 denotes the neutral in all three items) (c) As
mentioned above, all three items can be clubbed
while satisfying the content and criterion validity.
This sentence needs a little more explanation.
The idea or concept behind framing item 1, 2 and
3 is to capture the opinion of participants about
the lecture. This theoretical construct how well
can be transformed into operating reality, can be
ascertained by looking at relevant content
domains (content validity/reflection of construct),
ability to distinguish opinion on lecture from other
teaching modality (concurrent validity) and
similarities among items 1 to 3 and dissimilarities
from item 4 and 5 (convergent and discriminant
validity). Concurrent, convergent and
discriminant validities are the domains of criterion
validity. Before deciding any statistical treatment
to items, all the items must be scrutinized for
validity issues.
If we look into point (a), (b) and (c) in cohesion
for the set of item 1, 2 and 3, that composite
score for the item-1, 2 and 3 can be compared
with another composite score for another
individual on an interval scale. A ‘rank-order’
among the composite scores can be presumed
as well as equality of interval among related
composite scores can also be postulated. The
specific point on a particular item is conveying
the same meaning for all individuals (for item -2
point 3 on Likert scale denotes ‘neutral’ among
all individuals.) Moreover a specific point (say 2
for disagree) is conveying the same meaning
(same extent of disagreement) in all the items
and there is no absolute zero in scale (minimum
achievable score is 3). From the discourse, this
can besafely assumed (after going through all
these mathematical characteristics with due
consideration of validity related issues) that the
obtained composite data for item 1, 2 and 3 for
all the participants can be treated on an interval
scale.
The truth has different dimension in case of item
4 and item 5. Item -4 and 5 being a mutually
exclusive observation from each other (opinion
on self-directed reading/ small group teaching)
and from item 1, 2 and 3 should be treated
differently. They may not be combined (validity
restriction) for an individual as they are nowhere
providing complementary observation.
Still item 4 and 5 can be treated on a certain
measurement scale. The arguments for this
assumption are –first, a specific point (say point -
4) for a particular item ( (say for item-4) conveys
the same meaning (agree) for all individuals
treated on that item and second, response
variables obtained for a single item from all the
individuals can be arranged in any order
preserving transformation (like square,
multiplication, square root etc.) to the response
variable(the rank order remains unaffected) ....
so an ordinal scale’s assumptions and treatment
is applicable on this subset of items (4 and 5).
Once it is clear that under which rules the items
are categorized and what the direction of inquiry
is, it becomes obvious that the further statistical
treatment as per their assignment into ordinal or
interval scale.
6. CONCLUSION
The crux that can be extracted from the above
inductive arguments and logical interpretation is
that the methods adopted for Likert scale
analysis largely depends on the item response
variable assignment into ordinal or interval scale
which in turn depends on the construct of the
research instrument. This construct of research
instrument can be derived from objectives of
study and objectives are the operational form of
theoretical construct of phenomenon under
inquiry. In other words, designing of instruments
based upon objectives and frameworks of study
decides further statistical treatment.
Hence if one wishes to combine the items in
order to generate a composite score (Likert
scale) of a set of items for different participants,
then the assigned scale will be an interval scale
(Fig. 2 above). The measures for central
tendency and dispersion for an interval scale are
Joshi et al.; BJAST, 7(4): 396-403, 2015; Article no.BJAST.2015.157
402
mean and standard deviation. Further this data
set can be statistically treated with Pearsons’
correlation coefficient (r), Analysis of Variance
(ANOVA) and regression analysis.
As opposed to, if researcher wishes to analyze
separate item (no composite score; Likert type
scale), the assigned scale for such data set will
be ordinal (Fig. 2 above). Needless to say, the
recommended measure of central tendency and
dispersion for the ordinal data are the median (or
the mode) & frequency (or range). An ordinal
data set can further be statistically tested by non-
parametric techniques such as Chi-square test,
Kendall Tau B or C test.
Before wrapping up, it is imperative to transform
an abstract issue into figurative shape in order to
measure it up to best possible extent.
Simultaneously, this is an integrate process
reason being influenced by perspective and
subjectivity of researcher. Still all attempts should
be directed for quantification of such qualitative
attributes as -‘what get measured, get managed.’
(Peter Druker).
COMPETING
INTERESTS
Authors have declared that no competing
interests exist.
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© 2015 Joshi et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution License
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Peer-review history:
The peer review history for this paper can be accessed here:
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... The choice is based on the decision of which data collection method would answer the RQ (Black, 2005;Dzwigol, 2020;Vogt, 2007). In the Likert survey design of a quantitative effort, options are offered for each question or statement (Joshi et al., 2015). The presented options represent the degree of agreement a research participant has on each item of inquiry. ...
... The presented options represent the degree of agreement a research participant has on each item of inquiry. The Likert design format allows participants to openly respond (Joshi et al., 2015). With the efficient use of statistics for data analysis, the research instrument enables the study to benefit from the quantitative method design. ...
... The conventional way to analyze a Likert scale instrument, such as the CCSQ-18, is to find the sum of each selection and determine a score for each value (Joshi et al., 2015;Vogt et al., 2014). Responses from each of the 18 items of the CCSQ-18 are coded 1-5 from strongly agree to strongly disagree. ...
Article
Purpose The purpose of the quantitative correlational research study was to determine the relationship, if any, between the predictor variable, cosmeceutical business service quality, and the outcome variable, cosmeceutical client satisfaction, in the southeast region of the United States of America. Cosmeceuticals were cosmetics and medications administered by estheticians. Design/methodology/approach Literature on business service quality and client satisfaction theories was synthesized after extensive review. Quantitative research data were collected and statistically analyzed on the following subscales of consumer satisfaction: general satisfaction, technical quality, interpersonal manner, communication, financial aspects, time spent with professionals and accessibility/convenience. The hypotheses addressed the research question (RQ) of whether cosmeceutical business service quality affects client satisfaction. The Cosmeceutical Client Satisfaction Questionnaire 18 (CCSQ-18), a web-based research instrument, had strong reliability with a Cronbach’s alpha of 0.84. The target population ( N = 50) included randomly selected female cosmeceutical consumers in the southeast region of the United States of America. The researcher did not digress from the detailed research protocol, instrumentation, data collection or data analyses. Through the Likelihood Ratio (LR) chi-squared statistic (18) = 65.35 and its associated probability, Prob > chi-squared = 0.000, the researcher determined the predictor variable cohesively has a statistically significant effect on the outcome variable. Findings Research results concluded that a significant relationship exists between cosmeceutical business service quality and cosmeceutical client satisfaction in the southeast region of the United States of America. Originality/value The findings detailed in the results complimented the argument that, generally, business service quality is important to consider, because good business is based on client satisfaction.
... Primary data collected directly from participants through the questionnaire, focusing on followers' satisfaction levels, including dimensions like reliability, assurance, empathy, and responsiveness. Likert scales were used to measure responses quantitatively (Joshi et al., 2015). Secondary data from literature studies augmented the research findings, providing context and validating results (Slater & Gleason, 2012). ...
... Engaging with the audience is essential in social media interactions as it helps build relationships between content authors and followers. The substantial correlation coefficient of 0.775 between the content variable (X) and follower satisfaction highlights a significant relationship (Joshi et al., 2015). ...
Article
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This study explores the effect of the @Statsrawon social media X account on follower satisfaction by meeting their information demands against the backdrop of an increasing dependency on social media for information consumption. The research, which is based on the Uses and Gratifications theory, attempts to investigate how particular content dimensions—reliability, assurance, empathy, and responsiveness—affect followers' satisfaction levels. A structured questionnaire was used to collect data from a purposive sample of 100 followers using a quantitative research methodology in order to examine the relationship between content features and satisfaction results. The results highlight a strong positive relationship between @Statsrawon content and meeting followers' information needs, highlighting the critical function that personalized matter plays in raising satisfaction levels. The study emphasizes the significance of producing pertinent and interesting content to favorably impact follower satisfaction and recommends for the enlargement of sample size and the addition of qualitative methodologies to enhance insights into follower engagement with social media content.
... Dalam mengukur penilaian responden, peneliti menggunakan skala Likert yang dimana skala tersebut merupakan suatu alat pengukuran yang digunakan dalam penelitian ilmu sosial dan ilmu pendidikan untuk mengukur sikap, pendapat, atau persepsi seseorang terhadap suatu fenomena atau pernyataan (Joshi et al., 2015). Secara umum, skala Likert terdiri dari pernyataan atau pertanyaan yang berkaitan dengan variabel yang ingin diukur, dan responden diminta untuk menunjukkan sejauh mana mereka setuju atau tidak setuju dengan pernyataan tersebut. ...
Technical Report
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Penelitian ini bertujuan untuk mengevaluasi efektivitas dan preferensi penggunaan platform Quizizz dalam konteks evaluasi semester mahasiswa Departemen Sistem Informasi di Institut Teknologi Sepuluh Nopember (ITS) Surabaya. Metode cross-sectional digunakan dengan pendekatan kuantitatif melibatkan responden mahasiswa Departemen Sistem Informasi ITS sebagai subjek penelitian. Pengumpulan data dilakukan melalui kuesioner yang menilai berbagai aspek penggunaan Quizizz seperti efektivitas waktu, motivasi, psikologi peserta ujian, serta pengalaman belajar dan latihan. Data juga dibandingkan dengan platform MCQs yang umum digunakan oleh mahasiswa ITS, yaitu MyITSClassroom, untuk mengidentifikasi kelebihan dan kelemahan masing-masing. Teknik pengumpulan data melibatkan pengalaman langsung (experiencing) dan pengajuan pertanyaan (enquiring). Hasil penelitian menunjukkan bahwa sebagian besar mahasiswa cenderung menilai Quizizz dengan baik, terutama dalam hal pengalaman belajar yang meningkatkan motivasi. Namun, tantangan terkait psikologi, manajemen waktu, dan tingkat stres tetap menjadi perhatian. Mayoritas responden menyatakan bahwa penggunaan Quizizz membantu memperkuat pemahaman materi secara interaktif, tetapi mereka juga merasa tertekan dengan batas waktu yang singkat dan ketidakmampuan untuk kembali ke soal sebelumnya. Platform MyITSClassroom dianggap lebih tepat untuk ujian serius yang memerlukan waktu lebih dan keleluasaan dalam memilih soal. Selain itu, beberapa mahasiswa menyarankan penambahan fitur-fitur baru pada Quizizz seperti penjelasan soal setelah ujian dan penyesuaian tingkat kesulitan soal. Dengan demikian, meskipun Quizizz memberikan pengalaman pembelajaran yang interaktif, perlu diperhatikan juga aspek-aspek yang dapat meningkatkan efektivitas dan kenyamanan pengguna dalam proses evaluasi pembelajaran. Implikasi penelitian ini dapat menjadi landasan untuk pengembangan dan penyesuaian fitur-fitur yang lebih fleksibel dan integrasi pada model gamifikasi untuk proses penilaian dan evaluasi belajar lain untuk dapat meningkatkan efektivitas evaluasi pembelajaran di masa mendatang.
... All questionnaires were examined to ensure they are correctly filled, questions numerically coded and each response keyed in STATA 15 and SPSS 28 for ordinal logistic regression analysis and Sanitation Technology Satisfaction Index (STSI). Ordinal logistic regression was considered suitable because the Likert scale data collected in the survey were treated as ordinal scale data as supported by Joshi et al. (2015). A key condition for the use of ordinal logistic regression is that the data must be in ordered categories. ...
Article
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The global sanitation crisis is urbanizing, with more than half of human excreta produced in global south cities not properly disposed of. However, most interventions addressing poor sanitation services have focused much more on infrastructure provision with less consideration for the user satisfaction. This paper contributes towards advancing this discussion by expounding on how socioeconomic factors influence sanitation service satisfaction in Kisumu city, Kenya. The paper is based on a survey of 384 households and focus group discussions. The results are analysed using ordinal logistic regression and sanitation technology satisfaction index is computed by combining users’ satisfaction with service delivery indicators. Analysis shows that income and sanitation technology are significant determinants of sanitation odour (p<0.1), while education, tenancy, income, and sanitation technology are significant determinants of sanitation accessibility (p<0.1). The index shows that users of sewer connection are the most satisfied with sanitation service delivery at 30.71 % as compared to the users of biogas latrine who are the most dissatisfied at 0.38%. Qualitative results reveal a myriad of reasons influencing users’ satisfaction with off-site and on-site sanitation technologies. The paper highlights the pivotal role played by socioeconomic factors in the provision of safe, affordable, secure, and dignified sanitation services in cities. The study recommends the adoption of enabling policy framework and capacity build of sanitation service watchdog organizations to periodically capture, document, and share information on the users’ perspectives on the services accessed by the citizenry with relevant state and non-state agencies for action.
... Penelitian ini bertujuan untuk memahami pendapat atau penilai dari individu sehingga dapat digunakan skala likert (Joshi et al. 2015). Skala likert dapat mengukur tanggapan konsumen terhadap karakteristik dari produk yang memungkinkan konsumen untuk mengekspresikan perasaan konsumen (Sugiyono 2013 ...
Article
This research aims to analyze the level of consumer satisfaction at the Palembang Bird Market and analyze the main variables that greatly influence the level of consumer satisfaction. Data collection and processing will be carried out in September – October 2023 in Palembang City. The research method used is the case study method, with a sampling technique namely purposive sampling. The data analysis used is the Customer Satisfaction Index (CSI). The results of this research showed that the Customer Satisfaction Index (CSI) value was 76.89% in the satisfied category. The variable with the highest level of satisfaction is the product quality variable for ornamental fish health attributes with a Mean Satisfaction Score (MSS) of 4.46. The variable that has the lowest level of satisfaction is the shop atmosphere variable on the shop cleanliness attribute with a Mean Satisfaction Score (MSS) of 2.89. The main variable that most influences satisfaction by looking at the level of importance is the product quality variable on the attributes of variations and types of ornamental fish with a Mean Importance Score (MIS) value of 4.76.
... Each category included three to six questions, presented in statement form. For example, to gauge perceptions of fare, we directly asked respondents to indicate their level of agreement with the statement "the fare is reasonable" on a Likert scale ranging from 1 to 5 [11]. The use of a mandatory Likert scale minimizes the need for data cleaning. ...
Article
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Shared mobility is changing urban transportation in India by providing transportation services without the need for ownership. Sharedautorickshaws (also called as share-autos) are a popular mode of shared mobility in the country. These informal vehicles can hold six to ten passengers and operate on a hail-to-board basis. It is important to evaluate the service quality of share-autos as they gain popularity. While research on passenger satisfaction with shared mobility services exists, studies on service quality perception related to share-autos are limited. To address this research gap, a survey was conducted with 581 shareauto users in India. The study created a Confirmatory Factor Analysisbased model with five latent variables and 22 manifest variables. The results revealed that 18 variables significantly influenced service quality. Variables that had weaker factor loading in the overall analysis were found more important when analysed for different subsets of the sample population. For instance, female-only or low-income-group respondents may prioritize different factors than the overall sample, and the ranking of factor loading changes across the subsets. The study shows that subset-based analysis can provide a more nuanced understanding of the passenger experience in share-autos, identifying potential opportunities to improve the quality of these services.
... Similar to the 4-point Likert scale[64]. Ratings of 3 or lower are considered unsafe, with lower ratings indicating greater unsafety. ...
Preprint
In this work, we introduce the PKU-SafeRLHF dataset, designed to promote research on safety alignment in large language models (LLMs). As a sibling project to SafeRLHF and BeaverTails, we separate annotations of helpfulness and harmlessness for question-answering pairs, providing distinct perspectives on these coupled attributes. Overall, we provide 44.6k refined prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels ranging from minor to severe, with answers generated by Llama-family models. Based on this, we collected 166.8k preference data, including dual-preference (helpfulness and harmlessness decoupled) and single-preference data (trade-off the helpfulness and harmlessness from scratch), respectively. Using the large-scale annotation data, we further train severity-sensitive moderation for the risk control of LLMs and safety-centric RLHF algorithms for the safety alignment of LLMs. We believe this dataset will be a valuable resource for the community, aiding in the safe deployment of LLMs.
... As part of the validation of the effectiveness of the tool, at the end of the laboratory a questionnaire was submitted to the participants to evaluate the usability of the set, while also underpinning the impact the latter had on their design practice. To do this, a Likert method was adopted (Joshi et al., 2015) which integrated, among other various questions related to the perception of the project itself, a System Usability Scale (SUS) which has been structured upon the 10 standard items defined by Brooke (1995) 3 . ...
... • Gobi's vocabulary translation task: Participants translate the provided Gobi's words/phrases into their native language. • Feedback section: Participants will provide feedback on the clarity, ease of understanding, and cultural neutrality of Gobi using a Likert scale [6] ranging from 1 to 5. ...
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This paper explores the feasibility of utilizing Large Language Models for the development of International Auxiliary Languages, with the objective of enhancing cross-cultural communication and fostering linguistic harmony. The study presents a comprehensive experiment designed to evaluate the effectiveness of LLMs in this context, encompassing the development and assessment of a new IAL named Gobi's. Through user-centered design principles, the experiment examines the ease of learning, comprehension, and portability of Gobi's across diverse native languages. Additionally, the paper delves into the linguistic properties of Gobi's, including its adherence to Zipf's Law and lexical richness. The findings shed light on the transformative potential of LLMs in IAL development, highlighting opportunities for enhancing global communication and collaboration. Ultimately, the study underscores the importance of continued research in this domain to refine and optimize the creation and implementation of LLM-generated IALs.
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High-quality care requires precise and timely provider documentation. Hospitals have used technology to document patient care within both the inpatient and outpatient areas and long-term care facilities. Research has demonstrated, by revealing a reduction in medical errors, that there has been a worldwide improvement in our community health and welfare since the implementation and utilization of documenting patient care electronically. Although electronic documentation has proven to be an improvement in patient record keeping, the most efficient location in which this documentation is to occur remains a question. At the location where this project took place, only the ICU had computers within the patient rooms for documentation purposes. This project evaluated bedside nurses' opinions related to the efficiency of documentation practices compounded by the location where documentation took place. The options were at the patient's bedside, on a workstation on wheels, or at the nursing station. Surveys were provided to bedside nursing staff both before and after computers were installed in patients' rooms in surgical and medical/surgical nursing units at a Veteran Affairs Medical Center located in the Northeastern region of the United States. The results of this project inconclusively answer the question posed: “Which mode of entry do nurses feel is more efficient to document patient care, on a computer in the patient room, at the nurses' station, or on a workstation on wheels?” Innovative strategies should be explored to develop a user-friendly design for computers located within the patient rooms for patient documentation.
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This study examined how using Likert-type scales with either 5-point, 7-point or 10-point format affects the resultant data in terms of mean scores, and measures of dispersion and shape. Three groups of respondents were administered a series of eight questions (group n's = 300, 250, 185). Respondents were randomly selected members of the general public. A different scale format was administered to each group. The 5-and 7-point scales were rescaled to a comparable mean score out of ten. The study found that the 5-and 7-point scales produced the same mean score as each other, once they were rescaled. However, the 10-point format tended to produce slightly lower relative means than either the 5-or 7-point scales (after the latter were rescaled). The overall mean score of the eight questions was 0.3 scale points lower for the 10-point format compared to the rescaled 5-and 7-point formats. This difference was statistically significant at p = 0.04. In terms of the other data characteristics, there was very little difference among the scale formats in terms of variation about the mean, skewness or kurtosis. This study is 'good news' for research departments or agencies who ponder whether changing scale format will destroy the comparability of historical data. 5-and 7-point scales can easily be rescaled with the resultant data being quite comparable. In the case of comparing 5-or 7-point data to 10-point data, a straightforward rescaling and arithmetic adjustment easily facilitates the comparison. The study suggests that indicators of customer sentiment – such as satisfaction surveys – may be partially dependent on the choice of scale format. A 5-or 7-point scale is likely to produce slightly higher mean scores relative to the highest possible attainable score, compared to that produced from a 10-point scale.
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Using a self-administered questionnaire, 227 respondents rated service elements associated with a restaurant, retail store, or public transport company on several 5-point and 7-point rating scales. Least-squares regression showed that linear equations for estimating 7-point from 5-point and 5-point from 7-point ratings explained over 85% of the variance and fitted the data almost as well as higher-order polynomials and power functions. In a cross-validation on a new data set, the proportion of variance explained fell to about 76%. Functionally inverse versions of the derived linear equations were calculated for the convenience of researchers and psychometricians.
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A conceptual framework employing the distinction between stimulus-centered and subject-centered scales is presented as a basis for reviewing 80 years of literature on the optimal number of response alternatives for a scale. Concepts and research from information theory and the absolute judgment paradigm of psychophysics are used. The author reviews the major factors influencing the quality of scaled information, points out areas in particular need of additional research, and makes some recommendations for the applied researcher.
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This article provides information for Extension professionals on the correct analysis of Likert data. The analyses of Likert-type and Likert scale data require unique data analysis procedures, and as a result, misuses and/or mistakes often occur. This article discusses the differences between Likert-type and Likert scale data and provides recommendations for descriptive statistics to be used during the analysis. Once a researcher understands the difference between Likert-type and Likert scale data, the decision on appropriate statistical procedures will be apparent.
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Reliability and validity of 4-point and 6-point scales were assessed using a new model-based ap proach to fit empirical data. Different measurement models were fit by confirmatory factor analyses of a multitrait-multimethod covariance matrix. 165 gradu ate students responded to nine items measuring three quantitative attitudes. Separation of method from trait variance led to greater reduction of reliability and heterotrait-monomethod coefficients for the 6-point scale than for the 4-point scale. Criterion-related valid ity was not affected by the number of scale points. The issue of selecting 4- versus 6-point scales may not be generally resolvable, but may rather depend on the empirical setting. Response conditions theorized to in fluence the use of scale options are discussed to pro vide directions for further research. Index terms: Likert-type scales, multitrait-multimethod matrix, reli ability, scale options, validity.
Chapter
From the Introduction: A growing body of literature suggests that attitudes may be much less enduring and stable than has traditionally been assumed. ... self-reports of attitudes are highly context dependent and can be profoundly influenced by minor changes in question wording, question format, or question order. For some researchers, this malleability simply reflects measurement error ... For other researchers, the same findings indicate that all we assess in attitude measurement are evaluative judgments that respondents construct ... based on whatever information happens to be accessible (e.g. Schwarz & Strack, 1991). From this perspective, the traditional attitude concept may not be particularly useful and we may learn more about human cognition and behavior from a detailed analysis of the underlying judgmental processes. Other researchers have taken intermediate positions ... For example, Lord & Lepper (in press) and Tourangeau and his colleagues (e.g. Tourangeau, 1992) equate attitudes with relatively stable memory structures, but assume that individuals sample from these structures when they answer attitude questions. Hence, a stable attitude can result in variable attitude reports, depending on which aspect of the knowledge structure (attitude) is accessed. Others (e.g., Wilson, 1998) suggested that individuals may hold multiple attitudes about an object, accessing different ones at different points in time. As we illustrate below, it is surprisingly difficult to design conclusive empirical tests to evaluate the relative merit of these proposals ... Yet, a scientific concept like “attitude” is to be evaluated on the basis of its explanatory power – and without taking judgmental processes into account, there is little that the attitude concept explains. In fact, the contemporary definition of attitudes as “likes and dislikes” (Bem, 1970, p. 14) equates attitudes with evaluative judgments. Hence, the first section of this chapter highlights judgmental processes and the second section applies these process assumptions to some findings that are typically considered evidence for the enduring nature of attitudes. In response to the malleability of attitude reports, social psychologists have repeatedly tried to replace or supplement verbal self-report measures with other, presumably more direct, ways to assess individuals’ evaluative responses to attitude objects. These attempts range from the “bogus pipeline” (Jones & Sigall, 1971) of the 1970s to the recent development of sophisticated “implicit” measures of attitudes (e.g. Dovidio & Fazio, 1992). Recent findings suggest that such measures may be just as context dependent as verbal reports, although the relevant contextual variables may differ. The third section addresses these developments, which are discussed in more detail by Banaji and colleagues (Chapter 7, this volume) and Bassili (Chapter 4, this volume). Much as the enduring nature of attitudes has been called into question, another body of research suggested that attitudes may not be closely related to behavior either (see Wicker, 1969, for an influential early review). Instead, we may expect a close relationship between attitudes and behavior only under some specific, and relatively narrow, conditions (see Chapter 19, this volume). These conditions can be fruitfully conceptualized within a judgment perspective, as we review in the final section.