📝 Exercise: Dani’s Mood

The dataset we’ll use is fictitious but reflects real-life events. Dani wants to measure how his son’s sleeping habits affect his mood. He recorded his grumpiness, his sleeping habits, and his son’s sleeping habits over several days. The dataset is saved as parenthood.csv and contains the following variables:

• dani.sleep: Dani’s sleeping hours
• baby.sleep: Baby’s sleeping hours
• dani.grump: Dani’s grumpiness (0–100 scale)
• day: Number of recorded days

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Dataset Location

Navigate to: Data Library > lsj-data > Parenthood

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Exercise Instructions

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1️⃣ Check the Data

• Load the Dataset: Open the parenthood.csv file in Jamovi.
• Adjust Variable Types:
  • Ensure dani.sleep, baby.sleep, and dani.grump are specified as continuous variables.
  • The day variable is nominal (integer).

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Descriptive Statistics

• Summarize each variable (dani.sleep, baby.sleep, dani.grump):
  • Mean, Standard Deviation, Minimum, and Maximum.

Graphical Representation

• Create histograms or boxplots to visualize each variable.
• Create scatterplots between variables (e.g., dani.sleep vs dani.grump).

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2️⃣ Write the Hypotheses

Identify the Dependent Variable (DV) and Independent Variable(s) (IV):

• DV: dani.?
• IV (simple regression): dani.?
• IVs (multiple regression): dani.? and baby.sleep

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Write the Hypotheses:

• Null Hypothesis (H₀): The IV(s) have no effect on Dani’s ?.
• Alternative Hypothesis (H₁): The IV(s) influence Dani’s ?.

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3️⃣ Check Correlation Between Variables

Correlation Matrix

• Generate a correlation matrix for the following variables:
  • dani.sleep, baby.sleep, and dani.grump.
• Include the following correlation coefficients:
  • Pearson: Measures linear correlation.
  • Spearman: Measures rank correlation.

Graphical Representation

• Create a scatterplot matrix to visualize the relationships between variables.

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4️⃣ Simple Regression Analysis

Test Relationship Between Dani’s Sleep and Grumpiness

1. Perform a simple regression where:
   • DV: dani.?
   • IV: dani.?
2. Report the Results:
   • Write the regression equation:
     [ \text{Dani’s ?} = b_0 + b_1(\text{Dani’s ?}) + \epsilon ]
   • Report the following statistics:
     • R²: Proportion of variance explained.
     • Regression coefficient (b1): Effect of Dani’s sleep on grumpiness.
     • p-value: Statistical significance.
3. Interpretation:
   • Is the relationship significant?
   • What does this tell us about the impact of Dani’s sleep on his grumpiness?

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5️⃣ Multiple Regression Analysis

Test the Combined Effect of Dani’s and Baby’s Sleep

1. Perform a multiple regression where:
   • DV: dani.grump
   • IVs: dani.sleep and baby.sleep
2. Report the Results:
   • Write the regression equation:
     [ \text{Dani’s grumpiness} = b_0 + b_1(\text{Dani’s sleep}) + b_2(\text{Baby’s sleep}) + \epsilon ]
   • Report the following statistics:
     • R²: Proportion of variance explained.
     • Regression coefficients (b1, b2): Effects of Dani’s and Baby’s sleep.
     • p-values: Statistical significance for each predictor.
3. Interpretation:
   • Which predictor is most influential?
   • Does Baby’s sleep significantly impact Dani’s grumpiness when controlling for his own sleep?

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6️⃣ Write Up the Results

Provide a brief summary of findings:

1. Correlation Analysis: Summarize correlations between the variables.
2. Simple Regression: Report the effect of Dani’s sleep on grumpiness.
3. Multiple Regression: Report the combined effect of Dani’s and Baby’s sleep.

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🔙 Return to Day 4