45 Matching Annotations
  1. Jul 2020
  2. Jun 2020
  3. May 2020
    1. The magnitude of the Pearson correlation coefficient determines the strength of the correlation. Although there are no hard-and-fast rules for assigning strength of association to particular values, some general guidelines are provided by Cohen (1988):

      Magnitude of pearson correlation coeficient.

  4. www.csun.edu www.csun.edu
    1. Undergraduate students who have already applied for graduation may change their date by submitting this form. The $8.00 processing fee is temporarily waived until further notice. 

      Graduate date change.

  5. Apr 2020
    1. Graphically, interactions can be seen as non-parallel lines connecting means when we are working with the simple two-factor factorial with 2 levels of each main effect (adapted from Zar, H. Biostatistical Analysis, 5th Ed., 1999). Remember interactions are referring to the failure of a response variable to one factor to be the same at different levels of another factor. So when lines are parallel the response is the same. In the plots below you will see parallel lines as a consistent feature in all of the plots with no interaction. In plots depicting interactions, you notice that the lines cross (or would cross if the lines kept going).

      Main and interaction effects - graphs

  6. Mar 2020
    1. To solve this issue, we render figures at the root directory /figure/, which will be copied to _site/ by Jekyll. To refer to an image under _site/figure/, we need the leading slash (baseurl), e.g., <img src="/figure/foo.png">. This is an absolute path, so no matter where the HTML is rendered, this path always works.

      Fix the path issue in rstudio using knitr.

    1. Click on Advanced Options, and you’ll see some additional useful tools:

      Virtual waiting room.

    2. Done? Not quite. Once you’ve launched your meeting and you’re staring at everyone’s lovely faces, consider clicking on Manage Participants, and then click on the “More” button. You’ll see the following options, which you can use to control some of the chaos in your meeting:

      Prevent users to unmute themselves.

    3. You can also disable annotations and whiteboards, if you’re worried that people might try to draw unpleasant things during your call. Over in the Advanced “In Meeting” settings, you might want to consider disabling Zoom’s virtual background feature—a way that a person can replace their video background with any image they want, which could get unpleasant.

      Prevent user to change background.

    4. Zoom’s app doesn’t have every setting you can use to control the chaos of your meetings. You’ll have to visit its website to play with a few of its most helpful options. For example, within its basic settings, you can block private chats in your meetings, turn off the public chat entirely, and block file transfers—useful so trolls don’t try to spam your willing participants.

      Prevent private chat

    5. Finally, here’s the biggie. Go back to the primary Zoom meeting window and click on the up arrow next to Share Screen. From there, click on Advanced Sharing Options, where you’ll see this screen:

      Share screen

    1. sleep Student's Sleep Data

      Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

    2. Pulse Pulse Rates and Exercise

      Students in a Stat2 class recorded resting pulse rates (in class), did three "laps" walking up/down a nearby set of stairs, and then measured their pulse rate after the exercise. They provided additional information about height, weight, exercise, and smoking habits via a survey.

    3. HSAUR agefat Total Body Composision Data

      Dataset used for KSB

    1. Factors that affect power

      Factors that affect power.

    2. Cohen’s recommendations:  Jacob Cohen has many well-known publications regarding issues of power and power analyses, including some recommendations about effect sizes that you can use when doing your power analysis.  Many researchers (including Cohen) consider the use of such recommendations as a last resort, when a thorough literature review has failed to reveal any useful numbers and a pilot study is either not possible or not feasible.  From Cohen (1988, pages 24-27):

      Recommendations from Cohen about choosing the effect size when doing a power analysis.

    3. Obtaining the necessary numbers to do a power analysis

      Obtaining the necessary numbers to do a power analysis

    4. Power is the probability of detecting an effect, given that the effect is really there.  In other words, it is the probability of rejecting the null hypothesis when it is in fact false.  For example, let’s say that we have a simple study with drug A and a placebo group, and that the drug truly is effective; the power is the probability of finding a difference between the two groups.  So, imagine that we had a power of .8 and that this simple study was conducted many times.  Having power of .8 means that 80% of the time, we would get a statistically significant difference between the drug A and placebo groups.  This also means that 20% of the times that we run this experiment, we will not obtain a statistically significant effect between the two groups, even though there really is an effect in reality.

      Power analysis definition

    1. Descriptive Statistic

      R provides a wide range of functions for obtaining summary statistics. One method of obtaining descriptive statistics is to use the sapply( ) function with a specified summary statistic.

    1. This function is based on the standard normal distribution and creates confidence intervals and tests hypotheses for both one and two sample problems.

      Use this syntax to run a z-test in r

    1. The standard error (the standard deviation of the distribution of sample means) has this formula:

      Tex command for the standard deviation of the sample mean

    1. Now that you have identified the null and alternative hypotheses, you need to find evidence and develop a strategy for declaring your "support" for either the null or alternative hypothesis. We can do this using some statistical theory and some arbitrary cut-off points. Both these issues are dealt with next.

      The researcher does not always have to support the alternative hypothesis.

  7. Sep 2019
  8. May 2019
    1. Multiple comparisons: It is not good practice to test for significant differences among pairs of group means unless the ANOVA suggests some such differences exist. Nevertheless, I admit it is tempting to take another look at the comparison of G1 with G3 (ignoring the existence of G2 and perhaps assuming normality), but then you should use a Welch t test to account for the differences in sample variances, and you should not make claims about the result unless the P-value is as low as .01 or .02. Looking at that difference more carefully might prompt a subsequent experiment.

      Test for significance among pairs when the overall f test is not significant.

  9. Apr 2019
    1. There are two tests that you can run that are applicable when the assumption of homogeneity of variances has been violated: (1) Welch or (2) Brown and Forsythe test. Alternatively, you could run a Kruskal-Wallis H Test. For most situations it has been shown that the Welch test is best. Both the Welch and Brown and Forsythe tests are available in SPSS Statistics (see our One-way ANOVA using SPSS Statistics guide).

      ANOVA is robust against violation of the assumption of equal variances, but...

    2. However, platykurtosis can have a profound effect when your group sizes are small. This leaves you with two options: (1) transform your data using various algorithms so that the shape of your distributions become normally distributed or (2) choose the nonparametric Kruskal-Wallis H Test which does not require the assumption of normality.

      ANOVA is robust against violation of normality, but...

    1. Favorable changes occurred in z-scores for weight (one-tailed p < 0.04) for age and gender among children in the combined center- and home-based intervention compared to comparison children at posttest.

      Here is an example of a study that used an one-tailed test as opposed to a two-tailed test.

      Challenge for readers: Dig into the Methods section and find out why the researchers used an one-tailed test.

  10. Mar 2019
    1. From this data, it can be concluded that cholesterol concentration in the diet group was statistically significantly higher than the exercise group (U = 110, p = .014). Depending on the size of your groups, SPSS Statistics will produce both exact and asymptotic statistical significance levels. Understanding which one to use is explained in our enhanced guide.

      Phrasing results for the Mann-Whitney U-test.

    1. We performed some manipulation checks to examine the internal validity of the perceptual-cognitive skill tests and any learning effects as a result of watching the same video clips multiple times