Chapter 1: Introduction to Statistic
1. Population vs. Sample:
- Population: All individuals of interest in a study (e.g., all adults in Hong Kong).
- Sample: A subset of individuals selected from the population (e.g., research
participants).
2. Variables and Data:
- Variable: A characteristic that changes or has different values for different
individuals (e.g., age, gender, height).
- Data: Measurements or observations of a variable (e.g., scores, raw scores).
- Data Set: A collection of measurements or observations.
3. Parameters vs. Statistics:
- Parameter: A numerical value that describes a population (e.g., average age of all
adults in Hong Kong).
- Statistic: A numerical value that describes a sample (e.g., average age of a sample of
adults).
- Sampling Error: The natural discrepancy between a sample statistic and the
population parameter.
4. Descriptive vs. Inferential Statistics:
- Descriptive Statistics: Summarize, organize, and simplify data (e.g., mean, median,
mode).
- Inferential Statistics: Use sample data to make generalizations about the population
(e.g., hypothesis testing).
5. Constructs (hypothetical constructs) and Operational definition
- Internal attributes or characteristics that cannot be directly observed (see next
slide)Useful for describing and explaining behavior
e.g., IQ, memory, love, anxiety
- Operational definition: Two components: (1) describes a set of operations for
measuring a construct; (2) defines the construct in terms of the resulting
measurements
6. Scales of Measurement
- Nominal: Categories only (e.g., gender, country).
- Ordinal: Ordered categories (e.g., rank in class, Olympic medals).
- Interval: Ordered categories with equal intervals and arbitrary zero ,complete
absence(e.g., temperature, IQ).
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Ratio: Ordered categories with equal intervals and a true zero (e.g., height, weight,
time).
7. Data Structures
6.1. Descriptive Research:
- One group with one or more variables measured for each individual.
- Goal: Describe individual variables (e.g., census data).
6.2.
6.3.
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Correlational Method:
One group with two variables measured for each individual.
Goal: Describe the relationship between variables (e.g., correlation between
chocolate consumption and Nobel prizes).
Limitation: Cannot establish cause-and-effect relationships.
Experimental and Non-experimental Methods:
Compare two or more groups of scores.
One variable defines the groups (e.g., boys vs. girls), and the second variable is
measured (e.g., test scores).
Experimental: Can establish cause-and-effect relationships. Random assign,
independent variable is manipulated
Non-experimental: Cannot establish cause-and-effect relationships.
Technique to control other variables
Random sample, matching, holding variable constant
7. Order of Mathematical Operations
1. Any calculation contained within parentheses is done first.
2. Squaring (or raising to other exponents) is done second.
3. Multiplying and/or dividing is done third. A series of multiplication and/or division
operations should be done in order from left to right.
4. Summation using the o notation is done next.
5. Finally, any other addition and/or subtraction is done.