2: Descriptive Statistics And Inferential Statistics

Question 1

Measures of ______.

1. Mean
2. Median
3. Mode

Answer 1

Central Tendency

Question 2

Measures of Central Tendency:

Arithmetic ____ is the sum of all values over the total value numbers.

Answer 2

Mean

Question 3

Measures of Central Tendency:

The middle number.

Answer 3

Median

Question 4

Measures of Central Tendency:

Most occuring number.

Answer 4

Mode.

Question 5

It provides insights into how spread out or scattered the data points in a data set are.

Answer 5

Measures of Variations

Question 6

Measures of __________:

1. Range
2. Variance
3. Standard Deviation (SD)
4. Interquartile Range
5. Coefficient of Variation

Answer 6

Variations

Question 7

Measures of Variations:

Simplest measure of variation. Difference between maximum and minimum values in a data set. Cons are sensitive to outliers and may not provide a complete set of data dispersion.

Answer 7

Range

Question 8

Measures of Variations:

Average squared difference from the mean, quantifying individual data points deviations.

Answer 8

Variance

Question 9

Measures of Variations:

Square root of variance, showing average data dispersion, higher values indicating more variability.

Answer 9

Standard Deviation

Question 10

Measures of Variations:

The range between 25th and 75th percentiles, less affected by outliers.

Answer 10

Interquartile Range

Question 11

Measures of Variations:

Standard deviation relative to the mean, used for comparing variability in different datasets.

Answer 11

Coefficient of Variation

Question 12

________ is the number of distances of deviation from the mean:

To bring it back, get the square root of Standard Deviation.

Answer 12

Variance

Question 13

• Most common variation to describe data.
• Most confusing concept.
• Understanding it, is essential to understand statistics.

Answer 13

Standard Deviation (SD)

Question 14

In Normal Distribution, this rule approximates number of values in SD; 68%, 95%, and 99.7%

Answer 14

Empirical Rule

Question 15

The probability that the study results are due to chance.

To know this is the first step in avoiding common errors in statistical interpretation.

It puts number on uncertainty but cannot eliminate uncertainty.

It is the probability that the null hypothesis is true.

Answer 15

P-Value

Question 16

A __________ is merely a statement of fact, which can be true or false.

Answer 16

Hypothesis

Question 17

In _________ hypothesis testing, one takes the hypothesis of interest and translates it with “not” into a null hypothesis, and then looks for evidence to reject the null.

Answer 17

Classical

Question 18

According to Dictionary of Epidemiology, ______ is the probability that a test statistic would be as extreme as or more extreme than observed if the null hypothesis were true.

Answer 18

P-Value

Question 19

The near-impossibility for a truly random ______ is the first limitation and threat to the accuracy of P-Value and the ability to generalize results to a larger population.

Answer 19

Sample

Question 20

The distribution of the test statistic _, has a mean of 0 and standard deviation of 1.

Answer 20

Z

Question 21

True or False.

A nonsignificant P value is good evidence of a true hypothesis.

Answer 21

False.

Absence of evidence is not evidence of absence. Other evidence is needed to appropriately accept the null hypothesis.

Question 22

Two types of Parameters:

Answer 22

1. Metric Level (Quantitative Data)
2. Categorical Parameters (Qualitative Data)

Question 23

“Everything is related to everything else.” (Meehl, 1990b)

Answer 23

Crud Factor.

“But the notion that the correlation arbitrarily paired trait variables will be, while not literally zero, of such miniscule size as to be of no importance, is surely wrong.”

Question 24

It is a way to understand and quantify the relationship between 2 or more variables.

Answer 24

Regression

Question 25

Karl Popper, a philosoher of science has the one of the most significant contribution in philosohy and science. "You can never prove a theory beyond all doubt, but you can disprove it through evidence." It is a process of representing our theories as null hypothesis and subjecting them to challenge.

Answer 25

Popperian Principle/Falsifiablitiy

Question 26

2 major types of sampling:

Answer 26

Probability Sampling and Non-probability Sampling. Probability Sampling - each member of the population has an equal chance of being selected for the survey. Non-probability Sampling - does not involve random sampling.

Question 27

Types of Probability Sampling: (3)

Answer 27

1. Random Sampling - most basic form. Randomly choosing. 2. Stratified Random Sampling - separating the population and randomly choosing the same ratio. For example, your population is undergraduate and graduate students in a university. If your university has 70% undergraduate, and 30% graduates, your sample will have a similar ratio. 3. Cluster Sampling - used when a population is spread over a large geographic region. For example, if you need a sample from all over the Philippines, you will randomly choose 10 provinces, and choose random people in each selected province.

Question 28

Types of Non-probability Sampling: (10) Note. For reading purposes only lol

Answer 28

1. Expert Sampling a. Description: Experts in a particular field are selected to participate in the study. b. Example: Conducting interviews with renowned psychologists on a specific psychological theory. 2. Availability Sampling a. Description: Participants are selected based on their availability at a particular time or place. b. Example: Surveying attendees at a specific event. 3. Quasi-Random Sampling a. Description: Participants are selected using a method that appears to be random, but there may be some level of researcher discretion. b. Example: Drawing names from a hat after assigning numbers to participants. 4. Homogeneous Sampling a. Description: Selecting participants who share similar characteristics or experiences related to the research topic. b. Example: Studying only psychology majors to understand their academic performance. 5. Heterogeneous Sampling a. Description: Selecting participants who have a wide range of characteristics or experiences related to the research topic. b. Example: Studying students from different majors to explore their career aspirations. 6. Volunteer (Self-selection) Sampling a. Description: Participants voluntarily choose to participate in the study. b. Example: Responding to an advertisement for a research study. 7. Quota Sampling a. Description: The researcher sets specific quotas for different subgroups to ensure representation. Participants within those quotas are then selected using convenience or judgmental methods. b. Example: Ensuring equal representation of different age groups in a survey. 8. Snowball Sampling a. Description: Initial participants (seeds) are identified and then asked to refer others who meet the inclusion criteria. b. Example: Researching a stigmatized group where direct access is challenging. 9. Purposive (Judgmental) Sampling a. Description: Participants are selected based on specific characteristics or qualities that are relevant to the research question. b. Example: Selecting individuals with a particular expertise for an expert panel. 10. Convenience Sampling a. Description: Participants are chosen based on their easy accessibility or availability to the researcher. b. Example: Surveying people passing by in a mall.

Question 29

It states that the larger the sample, the more likely the distribution of the means will be normal.

Answer 29

Central Limit Theorem

Question 30

Generally an analysis in determining relationships between two variables. Under this analysis, there are specific analyses to use: 1. Scatter plots: A graphical representation of the relationship between two continuous variables. 2. Correlation analysis: Measures the strength and direction of association between two continuous variables. 3. T-tests: Compares means of two groups to determine if there's a significant difference. 4. Chi-square tests: Examines the association between two categorical variables.

Answer 30

Bivariate Analysis

Question 31

A table in which each cell represents a unique combination of values. It provides you with a visual view of comparative data but not a statistical significance.

Answer 31

Cross Tabulation (Cross Tab)

Question 32

It is a test that looks at each cell in a cross-tabulation and measures the difference between what was observed and what would be expected in the general population. It is one of the most important statistics when you are assessing the relationship between ordinal and/or nominal measures. It cannot be used if any cell has an expected frequency of zero, or a negative integer. It also provides p-value, like T-test.

Answer 32

Chi-Square

Question 33

It compares the means between two values. For more than two groups, use ANOVA. It involves means, therefore the dependent variable must be a ratio variable (e.g. exam score). The independent variable is nominal or ordinal (e.g. study technique).

Answer 33

T-test

Question 34

It measures the strength of association between two variables, and reveals whether the correlation is negative or positive.

Answer 34

Correlation Coefficients

Question 35

It is the number of values that can vary in the estimation of a parameter. It is calculated depending on the statistical test or analysis you are using.

Answer 35

Degrees of Freedom

Question 36

Correlation coefficients is used whenever we want to test the strength of a relationship. There are many tests to measure correlation; which one to use depends on what variables you are examining. _______ variables: Phi, Cramer's V, Lambda, Goodman and Kruskal's Tau.

Answer 36

Nominal

Question 37

Correlation coefficients is used whenever we want to test the strength of a relationship. There are many tests to measure correlation; which one to use depends on what variables you are examining. _______ variables: Gamma, Sommers D, Spearman's Rho.

Answer 37

Ordinal

Question 38

Correlation coefficients is used whenever we want to test the strength of a relationship. There are many tests to measure correlation; which one to use depends on what variables you are examining. _______ variables: Pearson r

Answer 38

Ratio

Question 39

A statistical test that measures the means of more than two groups. The dependent variable must be ratio. Independent variable must be nominal or ordinal. It shows there are significant differences between groups but it does not illustrate where the significance lies.

Answer 39

ANOVA

Question 40

If ANOVA cannot show where the significance lies, we use this test to determine where the significance lies.

Answer 40

Post Hoc Comparison

Question 41

It looks at the relationship between more than two variables. Examples: 1. Multiple Regression: Examines the relationship between a dependent variable and two or more independent variables. 2. Factor Analysis: Reduces a large number of variables into a smaller set of underlying factors. 3. Principal Component Analysis (PCA): Similar to factor analysis, it identifies underlying patterns in the data. 4. Cluster Analysis: Groups observations or variables based on similarity. 5. MANOVA (Multivariate Analysis of Variance): Examines the differences in means across multiple dependent variables.

Answer 41

Multivariate Analysis

Question 42

It is better than doing multiple ANOVA tests to reduce potential Type 1 errors.

Answer 42

MANOVA

Question 43

It examines the relationship between one effect variable called dependent or outcome variable, and one or more predictors called independent variables.

Answer 43

Multiple Linear Regression