PSY 2061 Stroop and Native Language Project
ANSWER
To analyze the data from your fictitious study using an independent t-test, you will need to follow these steps:
Step 1: State the Hypotheses
In your study, you want to compare the time it takes for native English speakers (NECW) and native Spanish speakers (SSCW) to state the color of font for words with incongruent color names. Your hypothesis is that native English speakers will take longer, so you have:
- Null Hypothesis (H0): There is no significant difference in the mean time it takes for native English speakers and native Spanish speakers to state the color of font for words with incongruent color names. In mathematical terms, H0: μ_NE = μ_NS, where μ_NE is the mean time for native English speakers and μ_NS is the mean time for native Spanish speakers.
- Alternative Hypothesis (H1): There is a significant difference in the mean time it takes for native English speakers and native Spanish speakers to state the color of font for words with incongruent color names. In mathematical terms, H1: μ_NE ≠ μ_NS.
Step 2: Collect and Organize Data
You have the data provided, including participant number (ParticN), time for native English speaker to read color words (NECW), age of participants in years (Age), and time for Spanish speaker to read color words (SSCW). Ensure that your data is complete, without missing values, and properly organized.
Step 3: Check Assumptions
Before conducting the t-test, you should check the assumptions:
- Independence: Ensure that the participants are independent of each other.
- Normality: Check if the data in each group (NECW and SSCW) follows a roughly normal distribution. You can do this using visual methods (histograms, Q-Q plots) or statistical tests (Shapiro-Wilk test, Anderson-Darling test).
- Homogeneity of Variance: Check if the variances of the two groups are roughly equal. You can use Levene’s test for this.
Step 4: Conduct the Independent t-test
Assuming that the assumptions are met, you can proceed with the independent t-test. You can use a statistical software like R, Python (with libraries like SciPy), or specialized statistical software like SPSS or Excel. Here’s a simplified example in Python using SciPy:
import scipy.stats as stats
# Sample data for NECW and SSCW
NECW = [23, 25, 30, 22, 28, 27, 29, 24, 26, 31]
SSCW = [20, 19, 21, 18, 22, 23, 20, 19, 21, 24]
# Perform the t-test
t_stat, p_value = stats.ttest_ind(NECW, SSCW)
# Display the results
print("t-statistic:", t_stat)
print("p-value:", p_value)
Step 5: Interpret the Results
- If the p-value is less than your chosen significance level (e.g., 0.05), you would reject the null hypothesis, indicating that there is a significant difference in the mean time it takes for native English speakers and native Spanish speakers to state the color of font for words with incongruent color names.
- If the p-value is greater than your chosen significance level, you would fail to reject the null hypothesis, suggesting that there is no significant difference.
Make sure to report the t-statistic, p-value, and your conclusion in your analysis. Additionally, you can calculate and report the effect size (e.g., Cohen’s d) to quantify the practical significance of any differences found.
QUESTION
Description
Use an independent t-test to analyze data from a fictitious study. This study compares the ability of native English speakers and native Spanish speakers to state the font color of words that are color names. Although some of the participants are nonnative English speakers, all participants are considered fluent in English at the time of the study. The tricky aspect of this task is that the words will be printed in colors that are incongruent to the color names. The independent variable is the native language, and the dependent variable is the time it takes to state the color of font for all the words. It is hypothesized the native English speakers will take longer to state the colors compared with the nonnative speakers.
The data from the hypothetical study is given below. The explanations for the variable names used in the table are participant number (ParticN), time for native English speaker to read color words (NECW), age of participants in years (Age), and time for Spanish speaker to read color words (SSCW).