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LIkert scales, levels of measurement adn the “laws” of statistics


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Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for "getting the wrong answer".
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Likert scales, levels of measurement and the ‘‘laws’
of statistics
Geoff Norman
Received: 22 January 2010 / Accepted: 22 January 2010
Springer Science+Business Media B.V. 2010
Abstract Reviewers of research reports frequently criticize the choice of statistical
methods. While some of these criticisms are well-founded, frequently the use of various
parametric methods such as analysis of variance, regression, correlation are faulted
because: (a) the sample size is too small, (b) the data may not be normally distributed, or
(c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be
used. In this paper, I dissect these arguments, and show that many studies, dating back to
the 1930s consistently show that parametric statistics are robust with respect to violations
of these assumptions. Hence, challenges like those above are unfounded, and parametric
methods can be utilized without concern for ‘‘getting the wrong answer’’.
Keywords Likert Statistics Robustness ANOVA
One recurrent frustration in conducting research in health sciences is dealing with the
reviewer who decides to take issue with the statistical methods employed. Researchers do
occasionally commit egregious errors, usually the multiple test phenomenon associated
with data—dredging. But this is rarely the basis of reviewer’s challenges. As Bacchetti
(2002) has pointed out, many of these comments are unfounded or wrong, and appear to
result from a review culture that encourages ‘‘overvaluation of criticism for its own sake,
inappropriate statistical dogmatism’’, and is subject to ‘‘time pressure, and lack of rewards
for good peer reviewing’’. Typical reviewers’ comments in this genre may resemble those
listed below, drawn from reviews of 5 different papers, all brought to my attention in a
2 month period:
Paper 1
and in case of use of parametric tests (like t-test) I’d like to see the results of the
assumption of normality of the distribution
G. Norman (&)
McMaster University, 1200 Main St. W., Hamilton, ON L8N3Z5, Canada
Adv in Health Sci Educ
DOI 10.1007/s10459-010-9222-y
Paper 2
the authors [use]analytical practices which are not supported by the type of data
they have available. Ordinal data do not support mathematical calculations such
as change scores, . the approach adopted by the authors is indefensible.
Paper 3
The statistical analysis of correlation . is done with a method not suitable for non-
parametric, consult with statistician.
The t-test performed requires that the data be normally distributed. However, the
validity of these assumptions has not been justified
Given the small number of participants in each group, can the authors claim
statistical significance?
Paper 4:
The sample size is very low . As the data was not drawn from a normal distribution
due to the very low sample size, it is not possible to analyse the data using para-
metric tests, such as ANOVA.
Paper 5:
Did you complete a power analysis to determine if your N was high enough to do
these tests?
with the low N, not sure if you can claim significance without a power analysis to
confirm; otherwise Type II error is highly possible in your results
Some of these comments, like the proscription on the use of ANOVA with small
samples, the suggestion to use power analysis to determine if sample size was large enough
to do a parametric test, or the concern that a significant result still might be a Type II error,
are simply wrong and reveal more about the reviewer’s competence than the study design.
Others, like the various distributional assumptions or the use of parametric statistics
with ordinal data, may be strictly true, but fail to account for the robustness of parametric
tests, and ignore a substantial literature suggesting that parametric statistics are perfectly
appropriate. Regrettably, these reviewers can find compatible company in the literature.
For example, Kuzon et al. (1996) writes about the ‘‘seven deadly sins of statistical anal-
ysis’’. Sin 1 is using parametric statistics on ordinal data; Sin 2 relates to the assumption of
normality and claims that ‘‘Before parametric statistical analysis is appropriatethe study
sample must be drawn from a normally distributed population [ital. theirs]’ and (2) the
sample size must be large enough to be representative of the population’’.
The intention of this paper is to redress the balance. One of the beauties of statistical
methods is that, although they often involve heroic assumptions about the data, it seems to
matter very little even when these are violated. In order to help researchers more effec-
tively deal with challenges like those above, this paper is a review of the assumptions of
Representativeness is required of all statistical tests and is fundamental to statistical inference. But it is
unrelated to sample size.
G. Norman
various statistical methods and the problems (or more commonly the lack of problems)
when the assumptions are violated.
These issues are particularly germane to educational research because so many of our
studies involve rating scales of one kind or another and virtually all rating scales involve
variants on the 7 point Likert scale. It does not take a lot of thought to recognize that Likert
scales are ordinal. To quote a recent article in Medical Education (Jamieson 2004) ‘‘the
response categories have a rank order but the intervals between values cannot be presumed
equal’’. True—strictly speaking. The consequence is that, again according to Jamieson,
‘the appropriate descriptive and inferential statistics differ for ordinal and interval vari-
ables and if the wrong statistical technique is used, the researcher increases the chance of
coming to the wrong conclusion’’. Again, true—strictly speaking. But what is left unsaid is
how much it increases the chance of an erroneous conclusion. This is what statisticians call
‘robustness’’, the extent to which the test will give the right answer even when assump-
tions are violated. And if it doesn’t increase the chance very much (or not at all), then we
can press on.
It is critically important to take this next step, not simply because we want to avoid
‘coming to the wrong conclusion’’. As it turns out, parametric methods are incredibly
versatile, powerful and comprehensive. Modern parametric statistical methods like factor
analysis, hierarchical linear models, structural equation models are all based on an
assumption of normally distributed, interval-level data. Similarly generalizability theory, is
based on ANOVA that again is a parametric procedure. By contrast, rank methods like
Spearman rho, Kruskal–Wallis, appear frozen in time and are used only rarely. They can
handle only the simplest of designs. If Jamieson and others are right and we cannot use
parametric methods on Likert scale data, and we have to prove that our data are exactly
normally distributed, then we can effectively trash about 75% of our research on educa-
tional, health status and quality of life assessment (as pointed out by one editor in dis-
missing one of the reviewer comments above).
Well, despite the fact that Jamieson’s recent paper has apparently taken the medical
education world by surprise and was the most downloaded paper in Medical Education in
2004, the arguments back and forth have been going on for a very long time. I will spend
some time reviewing these issues, but instead of focusing on assumptions, I will directly
address the issue of robustness. I will explore the impact of three characteristics-sample
size, non-normality, and ordinal-level measurement, on the use of parametric methods. The
arguments and responses:
1) You can’t use parametric tests in this study because the sample size is too small
This is the easiest argument to counter. The issue is not discussed in the statistics
literature, and does not appear in statistics books, for one simple reason. Nowhere in the
assumptions of parametric statistics is there any restriction on sample size. It is simply not
true, for example, that ANOVA can only be used for large samples, and one should use a t
test for smaller samples. ANOVA and t tests are based on the same assumptions; for two
groups the Ftest from the ANOVA is the square of the ttest. Nor is it the case that below
some magical sample size, one should use non-parametric statistics. Nowhere is there any
evidence that non-parametric tests are more appropriate than parametric tests when sample
sizes get smaller.
In fact, there is one circumstance where non-parametric tests will give an answer that
can be extremely conservative (i.e. wrong). The act of dichotomizing data (for example,
using final exam scores to create Pass and Fail groups and analyzing failure rates, instead
of simply analyzing the actual scores), can reduce statistical power enormously. Simula-
tions I conducted showed that if the data are reasonably continuous and reasonably ‘‘well-
Likert scales, levels of measurement and the ‘‘laws’’ of statistics
behaved’’ (begging the issue of what is ‘‘reasonable’’) dichotomizing the data led to a
reduction in statistical power. To do this, I began with data from two hypothetical dis-
tributions with a known separation, so that I could compute a Ztest on the difference
between means. (For example, two distributions centered on 50 and 55, with a sample size
of 100, and a standard deviation of 15. I then drew a cutpoint so that each distribution was
divided into 2 groups (a ‘‘pass and a ‘‘fail’’). This then led to a 2 92 table with proportions
derived from the overlap of the original distributions and the location of the cutpoint. I then
computed the required sample size for a P-value of .05 using a standard formula. Finally, I
calculated the ratio of the sample size for a significant Z test and computed the ratio. The
result was a cost in sample size from 20% (when the cutpoint was on the 50th percentile) to
2,600% (when the cutpoint was at the 5th or 95th percentile). The finding is neither new
nor publishable; other authors have shown similar effects (Suissa 1991; Hunter and
Schmidt 1990).
Sample size is not unimportant. It may be an issue in the use of statistics for a number of
reasons unrelated to the choice of test:
(a) With too small a sample, external validity is a concern. It is difficult to argue that 2
physicians or 3 nursing students are representative of anything (qualitative research
notwithstanding). But this is an issue of judgment, not statistics.
(b) As we will see in the next section, when the sample size is small, there may be
concern about the distributions (see next section). However, it turns out that the
demarcation is about 5 per group. And the issue is not that one cannot do the test, but
rather that one might begin to worry about the robustness of the test.
(c) Of course, small samples require larger effects to achieve statistical significance. But
to say, as one reviewer said above, ‘‘Given the small number of participants in each
group, can the authors claim statistical significance?’’, simply reveals a lack of
understanding. If it’s significant, it’s significant. A small sample size makes the
hurdle higher, but if you’ve cleared it, you’re there.
2) You can’t use t tests and ANOVA because the data are not normally distributed
This is likely one of the most prevalent myths. We all see the pretty bell curves used to
illustrate ztests, ttests and the like in statistics books, and we learn that ‘‘parametric tests
are based on the assumption of normality’’. Regrettably, we forget the last part of the
sentence. For the standard t tests ANOVAs, and so on, it is the assumption of normality of
the distribution of means, not of the data. The Central Limit Theorem shows that, for
sample sizes greater than 5 or 10 per group, the means are approximately normally dis-
tributed regardless of the original distribution. Empirical studies of robustness of ANOVA
date all the way back to Pearson (1931) who found ANOVA was robust for highly skewed
non-normal distributions and sample sizes of 4, 5 and 10. Boneau (1960) looked at normal,
rectangular and exponential distributions and sample sizes of 5 and 15, and showed that 17
of the 20 calculated P-values were between .04 and .07 for a nominal 0.05. Thus both
theory and data converge on the conclusion that parametric methods examining differences
between means, for sample sizes greater than 5, do not require the assumption of nor-
mality, and will yield nearly correct answers even for manifestly nonnormal and asym-
metric distributions like exponentials.
3) You can’t use parametric tests like ANOVA and Pearson correlations (or regression,
which amounts to the same thing) because the data are ordinal and you can’t assume
The question, then, is how robust are Likert scales to departures from linear, normal
distributions. There are actually three answers. The first, perhaps the least radical, is that
G. Norman
expounded by Carifio and Perla (2008) in their response to Jamieson (2004). They begin, as
I have, in pointing out that those who defend the logical position that parametric methods
cannot be used on ordinal data ignore the many studies of robustness. But their strongest
argument appears to be that while Likert questions or items may well be ordinal, Likert
scales, consisting of sums across many items, will be interval. It is completely analogous to
the everyday, and perfectly defensible, practice of treating the sum of correct answers on a
multiple choice test, each of which is binary, as an interval scale. The problem is that they,
by extension, support the ‘‘ordinalist’’ position for individual items, stating ‘‘Analyzing a
single Likert item, it should also be noted, is a practice that should occur only rarely.’
Their rejoinder can hardly be viewed as a strong refutation.
The second approach, as elaborated by Gaito (1980), is that this is not a statistics
question at all. The numbers ‘‘don’t know where they came from’’. What this means is that,
even if conceptually a Likert scale is ordinal, to the extent that we cannot theoretically
guarantee that the true distance between 1 =‘Definitely disagree’’ and 2 =‘‘Disagree’’ is
the same as ‘4 =‘No opinion’ and 5 =‘Moderately agree’’, this is irrelevant to the
analysis because the computer has no way of affirming or denying it. There are no inde-
pendent observations to verify or refute the issue. And all the computer can do is draw
conclusions about the numbers themselves. So if the numbers are reasonably distributed,
we can make inferences about their means, differences or whatever. We cannot, strictly
speaking, make further inferences about differences in the underlying, latent, characteristic
reflected in the Likert numbers, but this does not invalidate conclusions about the numbers.
This is almost a ‘reductio ad absurbum’ argument, and appears to solve the problem by
making it someone else’s, but not the statistician’s problem. After all, someone has to
decide whether the analysis done on the numbers reflects the underlying constructs, and
Gaito provides no support for this inference.
So let us return to the more empirical approach that has been used to investigate
robustness. As we showed earlier, ANOVA and other tests of central tendency are highly
robust to things like skewness and non-normality. Since an ordinal distribution amounts to
some kind of nonlinear relation between the number and the latent variable, then in my
view the answer to the question of robustness with respect to ordinality is essentially
answered by the studies cited above showing robustness with respect to non-normality.
However, when it comes to correlation and regression, this proscription cannot be dealt
with quite so easily. The nature of regression and correlation methods is that they inher-
ently deal with variation, not central tendency (Cronbach 1957). We are no longer talking
about a distribution of means. Rather, the magnitude of the correlation is sensitive to
individual data at the extremes of the distribution, as these ‘‘anchor’’ the regression line.
So, conceivably, distortions in the distribution—skewness or non-linearity—could well
‘give the wrong answer’’.
If the Likert ratings are ordinal which in turn means that the distributions are highly
skewed or have some other undesirable property, then it is a statistical issue about whether
or not we can go ahead and calculate correlations or regression coefficients. It again
becomes an issue of robustness. If the distributions are not normal and linear. what happens
to the correlations? This time, there is no ‘‘Central Limit Theorem’’ to provide theoretical
confidence. However, there have been a number of studies that are reassuring. Pearson
(1931,1932a,b), Dunlap (1931) and Havlicek and Peterson (1976) have all shown, using
theoretical distributions, that the Pearson correlation is robust with respect to skewness and
nonnormality. Havlicek and Peterson did the most extensive simulation study, looking at
sample size from 5 to 60 (with 3,000–5,000 replications each), for normal, rectangular, and
ordinal scales (the latter obtained by adding and subtracting numbers at random). They
Likert scales, levels of measurement and the ‘‘laws’’ of statistics
then computed the proportions of observed correlations within each nominal magnitude,
e.g. for a nominal proportion of 0.05, the proportion of samples in this zone ranged from
.046 to .053. They concluded that ‘‘The Pearson r is rather insensitive to extreme violations
of the basic assumptions of normality and the type of scale’’.
I confirmed these results recently with some real scale data. I had available a data set
from 93 patients who had completed a quality of life measure related to cough consisting of
8, 10 point scales, on two occasions (Fletcher et al. 2010). The questions were of the form:
I have had serious health problems before my visit.
I have been unable to participate in activities before my visit.
and the responses were on a 10 point scale, with gradations:
0=no problem
2=mild problem
4=moderate problem
6=severe problem
8=very serious problem
10 =worst possible problem
Each response was made by inspecting a card that showed: (a) The number, (b) The
description, (c) A graphical ‘‘ladder’’, and (d) a sad to happy face.
Using the data set, I computed the Pearson correlation between each of the Time 1 scale
responses and each of the Time 2 responses, resulting in 64 correlations based on a sample
of 93 respondents. I then calculated the Spearman correlation based on ranks derived from
the 10 scale points. Finally, I then treated these 64 pairs of Spearman and Pearson cor-
relations as raw data, and computed the regression line, predicting the Spearman corre-
lation from the Pearson correlation. A perfect relationship would have a correlation
(Pearson) of 1.0 between the calculated Pearson and Spearman correlations, a slope of 1.0
and an intercept of 0.0.
To then create more extremely ordinal data sets, I first turned the raw data into 5 point
scales, by combining 0 and 1, 2 and 3, 4 and 5, 6 and 7, and 8, 9 and 10. Finally, to model a
very ordinal skewed distribution, I created a new 4—point scale, where 0 =1; 1 and
2=2; 3, 4, and 5 =3; and 6, 7, 8, 9, and 10 =4. Again I computed Pearson and
Spearman correlations and looked at the relation between the two (Table 1).
For the original data, the correlation between Spearman and Pearson coefficients was
0.99, the slope was 1.001, and the intercept was -.007. Even with the severely skewed
data, the correlation was still 0.987, the slope was 0.995, and the intercept was -.0003.
The means of the Pearson and Spearman correlations were within 0.004 for all conditions.
For this set of observations, the Pearson correlation and the Spearman correlation based
on ranks yielded virtually identical values, even in conditions of manifestly non-normal,
skewed data. Now it turns out that, when you have many tied ranks, the Spearman gives
slightly different answers than the Pearson, but this reflects error in the Spearman way of
dealing with ties, not a problem with the Pearson correlation. The Pearson correlation like
all parametric tests we have examined, is extremely robust with respect to violations of
4) You cannot use an intraclass correlation (or Generalizability Theory) to compute the
reliability because the data are nominal/ordinal and you have to use Kappa (or Weighted
Although this appears to be a special case of the previous section, there is a concise
answer to this particular question. Kappa was originally developed as a ‘‘Coefficient of
G. Norman
agreement for nominal scales’’ (Cohen 1960), and in its original form was based on
agreement expressed in a 2 92 frequency table. Cohen (1968) later generalized the for-
mulation to ‘‘weighted kappa’’, to be used with ordinal data such as Likert scales, where
the data would be displayed as agreement in a 7 97 matrix. Weighting accounted for
partial agreement (Observer 1 rates it 6; Observer 2 rates it 5). Although any weighting
scheme is possible, the most common is ‘‘quadratic’’ weights, where disagreement of 1 unit
is weighted 1, of 2 is weighted 4, of 3, 9, and so forth.
Surprisingly, if one proceeds to calculate an intraclass correlation with the same 7-point
scale data, the results are mathematically identical, as proven by Fleiss and Cohen (1973).
And if one computes an intraclass correlation from a 2 92 table, using ‘‘1’’ when there is
agreement and ‘‘0’’ when there is not, the unweighted kappa is identical to an ICC. Since
ICCs and G theory are much more versatile (Berk 1979), handling multiple observers and
multiple factors with ease this equivalence is very useful.
Parametric statistics can be used with Likert data, with small sample sizes, with unequal
variances, and with non-normal distributions, with no fear of ‘‘coming to the wrong
conclusion’’. These findings are consistent with empirical literature dating back nearly
80 years. The controversy can cease (but likely won’t).
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Journal, 234, 1271–1273.
Berk, R. A. (1979). Generalizability of behavioral observations: a clarification of interobserver agreement
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Boneau, C. A. (1960). The effects of violations of assumptions underlying the ttest. Psychological Bulletin,
57, 49–64.
Carifio, L., & Perla, R. (2008). Resolving the 50 year debate around using and misusing Likert scales.
Medical Education, 42, 1150–1152.
Cohen, J. J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Mea-
surement, 20, 37–46.
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Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 671–684.
Dunlap, H. F. (1931). An empirical determination of means, standard deviations and correlation coefficients
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Table 1 Relation between Pear-
son and Spearman correlations
for 64 pairs based on N=93
10 point scales
5 point scales
4 point scales
Slope 1.001 1.018 0.995
Intercept -0.007 -0.013 -0.0003
Correlation 0.990 0.992 0.987
Mean Pearson 0.529 0.521 0.485
Mean Spearman 0.523 0.517 0.488
Likert scales, levels of measurement and the ‘‘laws’’ of statistics
Fleiss, J. L., & Cohen, J. J. (1973). The equivalence of weighed kappa and the intraclass correlation
coefficient as measures of reliability. Educational and Psychological Measurement, 33, 613–619.
Fletcher, K. E., French, C. T., Corapi, K. M., Irwin, R. S. & Norman, G. R. (2010). Prospective measures
provide more accurate assessments than retrospective measures of the minimal important difference in
quality of life. Journal of Clinical Epidemiology (in press).
Gaito, J. (1980). Measurement scales and statistics: Resurgence of an old misconception. Psychological
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Havlicek, L. L., & Peterson, N. L. (1976). Robustness of the Pearson correlation against violation of
assumption. Perceptual and Motor Skills, 43, 1319–1334.
Hunter, J. E., & Schmidt, F. L. (1990). Dichotomozation of continuous variables: The implications for meta-
analysis. Journal of Applied Psychology, 75, 334–349.
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G. Norman
... We used independent t tests for the comparison of continuous results between groups and a chi-square test or Fisher's exact test for categorical results, where appropriate. We treated the sum of the Likert scales as continuous variables in accordance with Norman et al. [29]. ...
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease (COVID-19), was first identified in Wuhan, China, in December 2019. As of 20 October 2020, the virus had infected 8,202,552 people, with 220,061 deaths in US, and in countries around the world, over 38 million people have become infected and over one million have died. The virus usually spreads via respiratory droplets from an infected person. At the time of compiling this paper, while countries around the world are still striving to find a “pharmaceutical intervention (PI)”, including treatments and vaccines, they are left with only “non-pharmaceutical interventions (NPIs)”, such as physical distancing, wearing masks, and maintaining personal hygiene. In the US, all 50 states, the District of Columbia, and five US territories issued mandatory stay-at-home orders between March 1 and 31 May 2020 to lower the risk of virus transmission. This study empirically examined how social connectedness and anxiety interact with shelter-in-place compliance and advisories during the pandemic. The study collected information from 494 adults using an online survey during April and July 2020.
Objectives This study aimed to investigate predictors of cycling performance in U23 cyclists by comparing traditional approaches to a novel method-the compound score. Thirty male U23 cyclists (N=30, age 20.1±1.1 yrs, body mass 69.0 ± 6.9 kg, height 182.6 ± 6.2 cm, V O 2max 73.8 ±2.5 .min-1) participated in this study. Design and Methods Power output information was derived from laboratory and field-testing during pre-season and mean maximal power outputs (MMP) from racing season. Absolute and relative 5-min MMP, 5-min MMP after 2,000 kJ (MMP 2,000KJ), allometric scaling and the compound score were compared to the race score and podium (top 3) performance during a competitive season. Positive and negative predictive values were calculated for all significant performance variables for the likelihood of a podium performance. Results The absolute 5-min MMP of the field test revealed the highest negative predictive capacity (82.4%, p=0.012) for a podium performance. The compound score of the 5-min MMP 2,000KJ demonstrated the highest positive and average predictive capacity (83.3%, 78.0%, p=0.007-respectively). The multi-linear regression analysis revealed a significant predictive capacity between performance variables and the race score (R 2 = 0.55, p=0.015). Conclusions Collectively the results of the present study reveal that the compound score, alongside absolute power, was able to predict the highest positive and average likelihood for a podium performance. These findings can help to better understand performance capacity from field data to predict future cycling success.
Natural interaction between multiple users within a shared virtual environment (VE) relies on each other's awareness of the current position of the interaction partners. This, however, cannot be warranted when users employ noncontinuous locomotion techniques, such as teleportation, which may cause confusion among bystanders. In this paper, we pursue two approaches to create a pleasant experience for both the moving user and the bystanders observing that movement. First, we will introduce a Smart Avatar system that delivers continuous full-body human representations for noncontinuous locomotion in shared virtual reality (VR) spaces. Smart Avatars imitate their assigned user's real-world movements when close-by and autonomously navigate to their user when the distance between them exceeds a certain threshold, i.e., after the user teleports. As part of the Smart Avatar system, we implemented four avatar transition techniques and compared them to conventional avatar locomotion in a user study, revealing significant positive effects on the observers' spatial awareness, as well as pragmatic and hedonic quality scores. Second, we introduce the concept of Stuttered Locomotion , which can be applied to any continuous locomotion method. By converting a continuous movement into short-interval teleport steps, we provide the merits of non-continuous locomotion for the moving user while observers can easily keep track of their path. Thus, while the experience for observers is similarly positive as with continuous motion, a user study confirmed that Stuttered Locomotion can significantly reduce the occurrence of cybersickness symptoms for the moving user, making it an attractive choice for shared VEs. We will discuss the potential of Smart Avatars and Stuttered Locomotion for shared VR experiences, both when applied individually and in combination.
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Chilean avocado orchards have been affected by branch canker and dieback, mainly associated with pathogens species of the family Botryosphaeriaceae. Producers often relate water stress to these phenomena; however, predisposing factors for these diseases in Chilean orchards have not been reported. Therefore, the objective of this study was to identify the main climate, planting, and management variables associated with branch canker and dieback in Chilean avocado orchards. Multivariable statistical analyses (PCA and PLS-DA) were performed to analyze 76 variables possibly associated with the incidence of these diseases during two consecutive growing seasons. Our findings indicated that branch canker and dieback are mainly conditioned by orchard variables such as plant age, canopy volume, trunk diameter, leaf area index, and planting density. Variables associated with aged orchards, such as high age, high canopy volume, and high trunk diameter, appeared to be more associated with high incidence levels than were younger orchards with higher planting densities and high tree vigor levels. In addition, abiotic stresses, such as frost and overirrigation, were associated as secondary factors affecting these diseases’ development in avocado orchards. Moreover, this study allowed us to determine that branch canker and dieback can develop indistinctly in the different Chilean agroclimatic zones.
Jury trials are considered a key element of the American criminal justice system, even as many question the continued legitimacy of the jury system and its ability to be fair. Using data from actual jurors collected at both the beginning of jury service and after deliberations, this research examines whether jurors’ views of the fairness of the jury system affect perceptions of evidence presented to them at trial. By surveying jurors at the beginning of jury service, this research uses measurements of jurors’ views which are not affected by the court process itself. We find that jurors who enter jury service with a stronger belief in the fairness of juries are more likely to favor the defense after hearing the evidence. Policy implications are discussed, including using caution with harsher methods to improve juror rate of appearance because of the potential effects on jurors’ views.
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This research aimed to find out the impacts of knowledge management on family business voluntary succession among family-owned SMEs in Thailand. Since family businesses, mainly SMEs, are vital to the country's economic system, they play a crucial role in economic development and employment. However, only a small proportion of family businesses could survive for subsequent generations of successors. Thus, the empirical findings of the recent study would help family businesses or SMEs in Thailand figure out family business succession. There are 382 respondents who are at least second generation family firms who participated in this survey via a Google Form online channel. The sampling methods are snowball and convenience sampling. The empirical results of this study found that knowledge management plays key roles in family businesses, both directly and indirectly. On one hand, knowledge management is a mediator variable between family business succession preparation and voluntary succession, which means the indirect effect of KM. On the other hand, the total effect of voluntary succession in family businesses is higher. Moreover, the moderate effect of technology adoption on knowledge management and succession planning is found as well.
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In many studies included in meta-analyses, the independent variable measure, the dependent variable measure, or both, have been artificially dichotomized, attenuating the correlation from its true value and resulting in (a) a downward distortion in the mean correlation and (b) an upward distortion in the apparent real variation of correlations across studies. We present (a) exact corrections for this distortion for the case in which only one of the variables has been dichotomized and (b) methods for making approximate corrections when both variables have been artificially dichotomized. These approximate corrections are shown to be quite accurate for most research data. Methods for weighting the resulting corrected correlations in meta-analysis are presented. These corrections make it possible for meta-analysis to yield approximately unbiased estimates of mean population correlations and their standard deviations despite the initial distortion in the correlations from individual studies. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The purpose of this study was to determine empirically effects of the violation of assumptions of normality and of measurement scales on the Pearson product moment correlation coefficient. The effects of such violations were studied separately and in combination for samples of varying size from 5 to 60. Monte Carlo procedures were used to generate populations of scores for four basic distributions: normal, positively skewed, negatively skewed, and leptokurtic. Samples of varying sizes were then randomly selected from specific populations. Results of the study were based on distributions of rs which were calculated on 5,000 sets of samples of n=5 or n=15 and 3,000 sets of samples of n=30 and n=60. Results indicated that the Pearson r is insensitive to rather extreme violations of the basic assumptions of normality and type of measurement scale. Failure to meet the basic assumptions separately or in combinations had little effect upon the obtained distributions of rs.
Reviews the book, The Nature of Intelligence edited by Lauren B. Resnick (see record 1977-04938-000 ). This book contains 20 chapters written by psychologists at the forefront of current intelligence research. Part 1 It contains papers that delineate many of the major issues in the study of intelligence, as seen from a psychometric point of view. The papers further discuss how these issues can be addressed from the viewpoint of cognitive psychology. Part 2 contains papers that show how simulation can be used to identify basic abilities and to model intellectual development. Part 3 of contains papers that place the construct of intelligence in a broader perspective than that attained either by differential or standard experimental psychology. Part 4 contains papers attempting to dissect intelligence into its basic components. Part 5 examines the use of ordinal scales of intellectual development, presents a factor model for research on intelligence, and considers some implications of intelligence research for education. Estes's paper seems to capture particularly well how far the field has progressed, while at the same time capturing the salient limitations of current research and approaches to it. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
There has been an increase lately in the use of the frequently less powerful nonparametric statistical techniques particularly when the data do not meet the assumptions for t or analysis of variance. By means of empirical analysis and a mathematical frame of reference it is demonstrated that the probability values for both t, and by generalization for F are scarcely influenced when the data do not meet the required assumptions. One exception to this conclusion is the situation where there exists unequal variances and unequal sample sizes. In this case the probability values will be quite different from the nominal values. 22 refs.
S. S. Stevens (1946) stated that there is a relationship between psychological measurement scales and statistical procedures such that parametric techniques require the presence of at least interval scale data. This idea was incorporated into numerous statistics books, but has been attacked and shown to be fallacious. This problem is reviewed, and measurement scales and statistical aspects are considered. The misconception was previously and is presently based on a confusion between measurement theory and statistical theory. For statistical tests of null hypothesis, "the numbers do not know where they came from." (19 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
"No man can be acquainted with all of psychology today." The past and future place within psychology of 2 historic streams of method, thought, and affiliation—experimental psychology and correlational psychology—is discussed in this address of the President at the 65th annual convention of the APA. "The well-known virtue of the experimental method is that it brings situational variables under tight control… . The correlation method, for its part, can study what man has not learned to control or can never hope to control… . A true federation of the disciplines is required. Kept independent, they can give only wrong answers or no answers at all regarding certain important problems… . Correlational psychology studies only variance among organisms; experimental psychology studies only variance among treatments. A united discipline will study both of these, but it will also be concerned with the otherwise neglected interactions between organismic and treatment variables. Our job is to invent constructs and to form a network of laws which permits prediction." (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Establishes the property that if Vij = (c-j)2 (Vij denotes the disagreement weight in the weighted Kappa formula) and if the variables can be scaled 1 and 2, then irrespective of the marginal distributions, weighted Kappa is identical with the intraclass correlation coefficient in which the mean differences between the raters is included as a component of variability. A discussion of this property is presented along with an example. (PsycINFO Database Record (c) 2012 APA, all rights reserved)