# Tag Info

13

Antoine Tremblay has just released an advanced analysis toolbox: http://onlinelibrary.wiley.com/doi/10.1111/psyp.12299/abstract It's missing about half the features on your list, although fundamentally, spectral density is a simple task and LORETA is a stand-alone package anyways (although similar approaches, e.g. general CSD estimation, are implemented in ...

11

IQ scores in general: An IQ score is a normative score. The norm group is typically defined as the general population, and where the respondent is a child, the norm group is defined in terms of the general population of children of that same age. IQ scores typically have a mean of 100 and a standard deviation of 15. In order to get an estimate of ...

9

Answer based on your original depression example Note that this answer was originally written based on your initial example, where you asked: Assume, I have developed a new intervention for people with light depression. I want to compare the effectiveness of this intervention (E) with an existing intervention (C). For this, I recruit test subjects ...

9

To calculate $d'$ you need to know two things: the hit rate and the false alarm rate. The hit rate is the proportion of trials where the stimulus was present and the subject responded that the stimulus was present. The false alarm rate is the proportion of trials where the stimulus was not present, and the subject responded that the stimulus was present. ...

8

You can find an accessible overview of some of the issues in Whelan (2008) which contains further references discussing the issue. Note that from a statistical perspective, the sample mean and median are unbiased estimators of their population equivalents. That said, with outliers, skew and the like, the standard error of the sample mean can be quite a bit ...

8

Experimental psychologists seem quite happy working with single item ad hoc self-report scales, physiological measures, etc. with very little psychometrical assessment so even before talking about a full-fledged latent variable modeling approach, confirmatory factor analysis and the like you might want to wonder why they appear relatively unconcerned with ...

8

Some extremely large personality data sets can be downloaded at http://personality-testing.info/_rawdata/ in CSV format. They are not packaged in R, but I post the link because you indicate in your reply to USER:what that you are having trouble finding any data sets what so ever.

7

In general, from my extensive experience, using a four or a five point response scale is not going to change much the psychometric properties of a typical psychological self-report scale (e.g., reliability and factor loadings). I also imagine that if you were to measure a multi-item scale with a four point version and a five point version that the ...

7

Short answer IQ scores are distributed normally, because they follow the central limit theorem. Background When we measure IQ scores in sufficiently large populations, they will be normally distributed. This holds for healthy controls, as well as groups of people with ADHD or reading disabilities (Kaplan et al., 2000), and also in people with mild to ...

6

I did eventually find something which may be what I was thinking of, examining the normality & psychometric properties of Likert scales with differing numbers of points. They concluded that the 11-point scale was closest to interval-type data in scaling and normality. Reference: A comparison of psychometric properties and normality in 4-, 5-, 6-, and ...

6

The languageR package contains several interesting real-world datasets relevant to psycholinguistics. These datasets are presumably also described in the accompanying textbook.

6

It's an interesting question. Here are a bunch of thoughts that came to my mind for why researchers might focus on observed variables. Many researchers report reliability and observed relationships between variables. By adopting a few assumptions, the reader can estimate what the latent relationships would be (see for example, the formula for correction of ...

6

The F ratio statistic has a numerator and denominator degrees of freedom. Thus, you report: F (numerator_df, denominator_df) = F_value, p = ..., effect size = ... The numerator degrees of freedom relates to the factor of interest; the denominator degrees of freedom corresponds to the degrees of freedom for the error variance. The exact way that these ...

6

In Stevens' levels of measurement framework, the NASA-TLX is an ordinal scale, not an interval scale, because there is no way to know a priori how much "workload" each point corresponds to. In other words, we can only know how much workload a point corresponds to after observing the data from our sample. Indeed, Hart's own review of the NASA-TLX suggests ...

6

ANOVA and t-tests are statistical tests for significance and therefore quantitative. The other mentioned items are scales (adding numbers to a certain choice) and therefore they can be considered as ordinal scales, and hence as quantitative as they are based on numbers. The NASA one can be administered by using a sliding scale which can be considered to ...

6

It's important to distinguish between measures and analyses, because only analyses can be quantitative or qualitative, not measures. Measures are, essentially, systematic processes by which we acquire our data, and analyses are processes we use to look at the data. As a rule of thumb, the difference is not hard to find and is given in the name: ...

6

The APA style manual does not provide specific guidelines for linear mixed models. Additionally, a review of studies using linear mixed models reported that the psychological papers surveyed differed 'substantially' in how they reported on these models (Barr, Levy, Scheepers and Tily, 2013). It depends greatly on your study, in other words. Normatively ...

5

Murphy & Cleveland (1995) mention, that a good way to reduce rater errors in general is to inform raters of the existence and nature of these errors and then to simply urge to avoid them. While this reduces rater errors, it also decreases the accuracy of ratings, though. These findings come from the literature on performance assessment, where halo is ...

5

Determining sample size for an animal experiment is no different than in research with human subjects. What you need to know is the effect size, the significance level and the power (which is the probability that the test detects a significant effect assuming that there is one). The tricky part is getting the effect size (for an interesting discussion have a ...

5

How many trials do you have per condition? With a small number of trials in the deviant condition, and a small number of participants, these things can happen. The ISI would not cause this per se, however, have you considered looking at effects of the previous trial? You can analyze the baseline intervals as a function of what type the previous trial was, ...

5

Not entirely sure what specific stats you'd be interested in, but Wikipedia has plenty on prevalences of specific mental disorders. For anxiety disorders, which include obsessive compulsive disorder: A review that pooled surveys in different countries up to 2004 found overall average prevalence estimates for any anxiety disorder of 10.6% (in the 12 months ...

5

The numbers inside the parentheses are the degrees of freedom for the F-statistic. The second number is the within-group degrees of freedom. When you have the same number of subjects in all conditions, then the second number will be the number of subjects - the number of cells (conditions) in your design.

5

I'm not sure I can give you the data purely for physical attractiveness, but what has been intensively researched is passionate love, which includes physical attraction. Passionate love is usually assumed to include sexual desire, and correlates quite well with rated attraction. As Hatfield writes: Generally, passionate love is associated with the terms “...

5

General reporting recommendations such as that of APA Manual apply. One should report exact p-value and an effect size along with its confidence interval. In the case of likelihood ratio test one should report the test's p-value and how much more likely the data is under model A than under model B. Example: The data is 7.3, 95% CI [6.8,8.1] times more ...

5

Short answer In psychophysical tests, often %correct rates are determined. Hence, training effects are often measured by determining correct rates. The ultimate outcome measures can be wildly variable, as they are dependent on the physical characteristics of the stimulus (visual, auditory, tactile, gustatory etc). Background Learning curves can be measured ...

5

Short answer From an ethical standpoint, not including interim evaluations may be bad practice. Background I will start off with a more extreme case than in your question example, just for illustrative purposes, namely that of a clinical intervention study. If it appears that the treatment group (say, experimental medicine Y, instead of the standard of care ...

5

gjacob is correct that optional stopping is a common research degree of freedom, and one that has a considerable and unfortunate intuitive basis. Yet, depending on the context of your research, AliceD's concerns are also important. There is, however, a middle ground between not checking at all, and p-hacking: sequential analysis. There is a Bayesian version ...

5

I wrote a paper that focuses on this question (Anglim & Grant, 2014; pre-print is https://osf.io/g8kbj/download). In short, if you're interested in estimating how well facets predict an outcome, then you should include all facets as predictors. Likewise, if you want to estimate the incremental prediction of facets over domains (e.g., 30 facets over the ...

5

There are various loosely-defined 'camps' among reform-minded methodologists, so you probably want to try to follow a representative of each. Andrew Gelman is a prominent landmark in this area, and runs a really nice frequently-updated blog at statmodeling.stat.columbia.edu with frequent paper critiques. The JASP crew also have a blog, https://www....

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