I wouldn't if what you mean is using effect coding to produce them. I would remove and reenter each variable of interest. Note the change in R squared. Change in R squared / (1 - change in R squared) is the effect size (see Cohen. 1992). SPSS makes it even easier providing part, partial, and zero order correlations. The square of the part (semi partial) gives the change in R squared.
Ashley B.
asked 11/28/23R Code functions needed in Stats
Could someone please help me with this question. How do you code effect sizes in R?? What would the code look like or the functions. I am working on a mixed regression. Here is the hypothesis below. Thank you in advance!!
Mixed regression
Social and Emotional Learning (SEL) encompasses a range of skills, attitudes, and behaviors that individuals cultivate to comprehend and regulate their emotions, foster and sustain positive relationships, make responsible decisions, and adeptly navigate challenging situations. You hypothesized that these skills can be increased through the use of mindfulness meditation as it is an effective program used in cognitive behavioral therapy to reduce rumination and anxiety and allow people to focus on the moment. Your belief is that this approach will decrease overall stress for the child in the classroom, given their low SEL skills. The reasoning is that by enhancing focus and attention during class, they can better navigate the learning environment without being overly influenced by individual emotions. You designed the study to investigate the effect of mindfulness meditation on teenagers with low social and emotional learning (as rated by their teachers). You recruited 30 kids from a local school from a pool of students identified by the teachers as needing intervention. Each kid undergoes a pre-post design, where you measure their baseline (Pre) level of stress (cortisol measured in saliva) and then have each kid do 15 minutes of mindfulness meditation during their lunch using the Headspace app following a course designed for stressed-out teens every day for a month, and you check in with the kids regularly to ensure compliance. Then, you measure their stress (cortisol in saliva) at the end of the month (Post). You hypothesize that the intervention will reduce the kid's physiological stress levels (as indexed by cortisol). In addition, you ask their parents to rate their kid's general levels of school-related stress at the beginning of the study. Parents are given a Likert scale rating from 1= low school stress to 7 = extreme school stress. You hypothesize that those with more extreme school-related stress will benefit more from the intervention than those with lower levels of school stress.
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