1. SCIENTIFIC METHOD
Theory:
Definition: A cyclical process using empirical (evidence-based) data to answer questions objectively (Newton,
1687).
Purpose: Avoids biases (e.g., believing in ESP without proof).
Steps:
1. Question: Identify issue (e.g., "Does music improve study performance?").
2. Hypothesis: Testable prediction (e.g., "Music increases test scores vs. no music").
3. Observations/Measurements: Collect data (e.g., test scores).
4. Analysis/Conclusions: Use statistics (e.g., compare means).
5. Publication: Share results; ask new questions (e.g., "Does music type matter?").
Key Terms: Empirical, cyclical, objectivity.
Example:
Question: "Does meditation reduce anxiety?"
Hypothesis: "Meditation lowers DASS anxiety scores."
Measurement: DASS subscale (0–21).
Analysis: Compare group means.
Example esame:
Q: What ensures the scientific method's objectivity?
o A) Intuition B) Empirical evidence C) Opinions D) Guesses
o Answer: B. Empirical evidence avoids bias.
Phrase: "The scientific method uses empirical data for objective conclusions."
2. FORMULATING HYPOTHESES
Theory:
Research Question (RQ): Broad issue (e.g., "Does exercise affect mood?").
Hypothesis: Specific, testable statement (e.g., "Exercise increases mood scores on a 1–7 scale").
Steps:
1. Identify Independent Variable (IV) (predictor, e.g., exercise) and Dependent Variable (DV) (outcome, e.g.,
mood).
2. Classify variables:
Continuous: Numerical (e.g., test scores, hours).
Categorical: Groups (e.g., exercise: yes/no).
3. Choose a design:
Experimental: Manipulate IV, random assignment.
Non-experimental: Observe variables.
4. Select analysis:
Two continuous variables: Correlation (Pearson's r).
Categorical + continuous: Mean differences (Cohen's d).
Two categorical: Contingency table.
Key Terms: Testable, IV, DV.
Decision Tree for Analysis:
Variables?
├── 2 continuous → Correlation (r)
├── 1 categorical, 1 continuous → Cohen’s d
└── 2 categorical → Contingency table
Example:
RQ: "Does gender affect lecture preference?"
Hypothesis: "Females prefer recorded lectures more than males."
IV: Gender (categorical). DV: Preference (categorical).
Analysis: Contingency table.
Example esame:
Q: What is the DV in "Caffeine improves focus"?
o A) Caffeine B) Focus C) Time D) Age
o Answer: B. DV is the outcome measured.
Phrase: "Hypotheses predict how the IV affects the DV."
, 3. TYPES OF DATA
Theory:
Variables: Concepts with multiple values (e.g., test score, gender).
Types:
o Nominal: Categories, no order (e.g., gender: male/female/non-binary).
o Ordinal: Ordered, unequal intervals (e.g., rankings: 1st, 2nd, 3rd).
o Interval: Equal intervals, no true zero (e.g., temperature in °C).
o Ratio: Equal intervals, true zero (e.g., age, weight).
Measurement Properties:
o Magnitude: The More attributes = the larger the number.
o Intervals: Equal differences = equal attribute changes.
o Rational zero: Zero = absence of attribute.
Key Terms: Nominal, ratio, measurement.
Example:
Variable: Drinking frequency.
o Nominal: Yes/No.
o Ordinal: Never/Sometimes/Often.
o Ratio: Drinks per week (0, 1, 2+).
Example esame:
Q: What type of data is "postal code"?
o A) Nominal B) Ordinal C) Interval D) Ratio
o Answer: A. Postal codes are categories without order.
Phrase: "Data types determine appropriate analyses."
4. DESCRIBING DATA
Theory:
Central Tendency: Typical score.
o Mean: Average.
o Median: Middle score when ordered.
o Mode: Most frequent score.
Variability: Spread of scores.
o Range: Max – min.
o Interquartile Range (IQR): 75th – 25th percentile.
o Standard Deviation (SD): Average distance from the mean.
Histogram: Shows distribution, overlap, and outliers.
Key Terms: Outlier (≥2 SDs from mean), skewness.
Formulas (see Section 17 for vertical format):
Mean: Sum all scores, divide by several scores.
SD: Measures spread; calculated via deviations from the mean.
Excel:
o Mean: =AVERAGE (A1:A5)
o SD: =STDEV.P(A1:A5)
Example:
Scores: 2, 3, 3, 4, 8.
Mean:
1. Sum: 2 + 3 + 3 + 4 + 8 = 20.
2. Divide: 20 ÷ 5 = 4.
Median: Ordered: 2, 3, 3, 4, 8 → 3.
Mode: 3 (appears twice).
SD (simplified, see Section 17 for steps):
1. Result: ≈2.1.
Interpretation: Most scores are within 2.1 of the mean (4).
Example esame:
Q: What's the mean of 1, 2, 3, 4?
o A) 2 B) 2.5 C) 3 D) 4
o Answer: B. Sum: 1 + 2 + 3 + 4 = 10. Divide: 10 ÷ 4 = 2.5.
Phrase: "SD shows how spread out scores are."
Theory:
Definition: A cyclical process using empirical (evidence-based) data to answer questions objectively (Newton,
1687).
Purpose: Avoids biases (e.g., believing in ESP without proof).
Steps:
1. Question: Identify issue (e.g., "Does music improve study performance?").
2. Hypothesis: Testable prediction (e.g., "Music increases test scores vs. no music").
3. Observations/Measurements: Collect data (e.g., test scores).
4. Analysis/Conclusions: Use statistics (e.g., compare means).
5. Publication: Share results; ask new questions (e.g., "Does music type matter?").
Key Terms: Empirical, cyclical, objectivity.
Example:
Question: "Does meditation reduce anxiety?"
Hypothesis: "Meditation lowers DASS anxiety scores."
Measurement: DASS subscale (0–21).
Analysis: Compare group means.
Example esame:
Q: What ensures the scientific method's objectivity?
o A) Intuition B) Empirical evidence C) Opinions D) Guesses
o Answer: B. Empirical evidence avoids bias.
Phrase: "The scientific method uses empirical data for objective conclusions."
2. FORMULATING HYPOTHESES
Theory:
Research Question (RQ): Broad issue (e.g., "Does exercise affect mood?").
Hypothesis: Specific, testable statement (e.g., "Exercise increases mood scores on a 1–7 scale").
Steps:
1. Identify Independent Variable (IV) (predictor, e.g., exercise) and Dependent Variable (DV) (outcome, e.g.,
mood).
2. Classify variables:
Continuous: Numerical (e.g., test scores, hours).
Categorical: Groups (e.g., exercise: yes/no).
3. Choose a design:
Experimental: Manipulate IV, random assignment.
Non-experimental: Observe variables.
4. Select analysis:
Two continuous variables: Correlation (Pearson's r).
Categorical + continuous: Mean differences (Cohen's d).
Two categorical: Contingency table.
Key Terms: Testable, IV, DV.
Decision Tree for Analysis:
Variables?
├── 2 continuous → Correlation (r)
├── 1 categorical, 1 continuous → Cohen’s d
└── 2 categorical → Contingency table
Example:
RQ: "Does gender affect lecture preference?"
Hypothesis: "Females prefer recorded lectures more than males."
IV: Gender (categorical). DV: Preference (categorical).
Analysis: Contingency table.
Example esame:
Q: What is the DV in "Caffeine improves focus"?
o A) Caffeine B) Focus C) Time D) Age
o Answer: B. DV is the outcome measured.
Phrase: "Hypotheses predict how the IV affects the DV."
, 3. TYPES OF DATA
Theory:
Variables: Concepts with multiple values (e.g., test score, gender).
Types:
o Nominal: Categories, no order (e.g., gender: male/female/non-binary).
o Ordinal: Ordered, unequal intervals (e.g., rankings: 1st, 2nd, 3rd).
o Interval: Equal intervals, no true zero (e.g., temperature in °C).
o Ratio: Equal intervals, true zero (e.g., age, weight).
Measurement Properties:
o Magnitude: The More attributes = the larger the number.
o Intervals: Equal differences = equal attribute changes.
o Rational zero: Zero = absence of attribute.
Key Terms: Nominal, ratio, measurement.
Example:
Variable: Drinking frequency.
o Nominal: Yes/No.
o Ordinal: Never/Sometimes/Often.
o Ratio: Drinks per week (0, 1, 2+).
Example esame:
Q: What type of data is "postal code"?
o A) Nominal B) Ordinal C) Interval D) Ratio
o Answer: A. Postal codes are categories without order.
Phrase: "Data types determine appropriate analyses."
4. DESCRIBING DATA
Theory:
Central Tendency: Typical score.
o Mean: Average.
o Median: Middle score when ordered.
o Mode: Most frequent score.
Variability: Spread of scores.
o Range: Max – min.
o Interquartile Range (IQR): 75th – 25th percentile.
o Standard Deviation (SD): Average distance from the mean.
Histogram: Shows distribution, overlap, and outliers.
Key Terms: Outlier (≥2 SDs from mean), skewness.
Formulas (see Section 17 for vertical format):
Mean: Sum all scores, divide by several scores.
SD: Measures spread; calculated via deviations from the mean.
Excel:
o Mean: =AVERAGE (A1:A5)
o SD: =STDEV.P(A1:A5)
Example:
Scores: 2, 3, 3, 4, 8.
Mean:
1. Sum: 2 + 3 + 3 + 4 + 8 = 20.
2. Divide: 20 ÷ 5 = 4.
Median: Ordered: 2, 3, 3, 4, 8 → 3.
Mode: 3 (appears twice).
SD (simplified, see Section 17 for steps):
1. Result: ≈2.1.
Interpretation: Most scores are within 2.1 of the mean (4).
Example esame:
Q: What's the mean of 1, 2, 3, 4?
o A) 2 B) 2.5 C) 3 D) 4
o Answer: B. Sum: 1 + 2 + 3 + 4 = 10. Divide: 10 ÷ 4 = 2.5.
Phrase: "SD shows how spread out scores are."
