aryanmishra4866

aryanmishra4866

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Aryan Mishra

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Project Management1English2Sociology1Psychology1Communications1Computer Science2Mathematics4
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Introduction: The Glasgow Norms Scott et al. (2019) are a set of ratings for 5500 English words on nine different psycholinguistic dimensions. In this task, you’ll be looking at the correlation between age of acquisition and valence. Age of acquisition (AOA) is a measure estimating the age at which a particular word is learned. Valence (VAL) is a measure indicating the pleasantness of the concept named by a word (on a scale of 1 to 9, with 9 being most pleasant).1 

I’ve randomly sampled 200 words from the full dataset, and extracted the words (Words), the mean age of acquisition for each word (AOA.M), and the mean valence for each word (VAL.M). 

 


RStudio
Part A: Write an R script that does the following things. To begin, load the data (it has a header and uses tab to separate the columns). sa4 is the variable you should store the data in.

1. Compute Pearson’s π‘Ÿ and Kendall’s 𝜏 for the correlation between age of acquisition and valence. Store these in sa4.r and sa4.tau, respectively.

2. Using lm(), construct a linear model for the relationship between age of acquisition and valence. Use valence as the dependent variable. Store your model in sa4.model.

3. Using the line formula 𝑦 = π‘Žπ‘₯+𝑏, calculate the expected valence for a word acquired at the age of 4 years old. Store this in four_years.expected.

4. Now calculate the actual valence for a word acquired at 4 years old, based on the data in the dataset. Since there’s no word acquired at exactly 4 years, get the words acquired between 3.9 years (inclusive) and 4.1 years (inclusive), and calculate the mean age of acquisition. Store this in four_years.actual.

5. Compute a statistical test to test the significance of Pearson’s π‘Ÿ. Store the result of the test in sa4.r.test.

6. Compute a statistical test to test the significance of Kendall’s 𝜏. Store the result of the test in sa4.tau.test.

7. Calculate π‘Ÿ 2 and store the result in sa4.rsquared.

 

Part B: Do the following using R.

1. Create a plot showing the relationship between age of acquisition and valence (recall that we’re treating valence as the dependent variable). Your plot should include a line of best fit.

2. Based on the plot, how would you characterize the relationship between age of acquisition and valence? Use the terminology about this correlation.

3. Report the results of your statistical test for Pearson’s π‘Ÿ.

4. Report the results of your statistical test for Kendall’s 𝜏.

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