Unit 1: Introduction

Question 1

It is a systematic inquiry to describe, explain, predict and control the observed
phenomenon.

Answer 1

Research

Question 2

2 methods involved in research

Answer 2

Inductive and deductive methods

Question 3

methods used to analyze the observed phenomenon

Answer 3

inductive

Question 4

methods used to verify the observed phenomenon.

Answer 4

deductive

Question 5

Types of research

Answer 5

1. Qualitative vs. quantitative
2. Descriptive vs. Inferential
3. Experimental vs. Quasi-experimental
4. Causal-comparative vs. Correlational

Question 6

Explain qualitative vs. quantitative research

Answer 6

Qualitative Research
- Research aimed at explaining complex phenomena through verbal descriptions
- Focuses on narrative descriptions and pattern seeking in language to reach conclusions.

Quantitative Research
- Research aimed at testing hypothesis with numerical values
- Usually focuses on statistical maneuvers to reach conclusions.

Question 7

Explain descriptive vs. inferential research

Answer 7

Descriptive Research
- Research aimed at describing the characteristics of a population without generalizing or testing statistical hypotheses - Focuses on details or characteristics of a population without making inferences beyond those studied.

Inferential Research
- Research aimed at generalizing to a larger population with data collected from sample of the population.
- Attempts to generalize from a sample which provides data to a larger population of interest via the use of statistical maneuvers.

Question 8

Explain experimental vs. quasi-experimental

Answer 8

True Experimental Research
- Research involving the use of manipulated independent variable (an intervention) coupled with random assignment of subjects to groups.

Quasi-experimental Research
- Research involving the use of manipulated independent variable (an intervention) without random assignment of participants to groups.

Question 9

Explain causal comparative vs. correlational

Answer 9

Causal Comparative Research
- Research aims at identifying causality among variables. It allows researchers to identify the cause and effect among variables. The researcher usually measures the impact each variable has before predicting the causality. In this
research, it can be highlighted that the researcher manipulates the variables.

Correlational Research
- Research attempts to identify association among variables. However, it cannot predict causality among variables. It can be highlighted that, contrary to causal comparative, the researcher does not attempt to manipulate variables.

Question 10

set of logical procedures that (when followed) enables one to
obtain evidence to determine the degree to which a theoretical hypothesis (or set of hypotheses) is/are correct.

Answer 10

research design

Question 11

Types of research designs

Answer 11

• Case study
• Experimental design
• Cross-sectional design
• Longitudinal design

Question 12

Explain what case study is (rs design)

Answer 12

Focuses on a single case rather than dealing with a sample of a large population.

Question 13

Explain what experimental design is (rs design)

Answer 13

• An experiment is a process where a certain degree of control over a given set of variables is exercised by the researcher when investigating.
• Experiments are used to test new hypothesis or existing theories with the end in view of confirming or refuting them.

Question 13

Explain what cross-sectional design is (rs design)

Answer 13

• It is the most common research design used by social scientists.
• It gathers data from a cross-section of a population.
• Is based on observations made at one point in time.

Question 14

Explain what longitudinal design is (rs design)

Answer 14

• In a longitudinal study, researchers repeatedly examine the same observations to detect changes that might occur over a period of time.
• The data are collected on a number of variables without any influence or intervention from the researcher.

Question 15

Roles of statistics

Answer 15

validity
analysis
efficiency

Question 16

Uses of statistics

Answer 16

• measure things
• examine relationships
• make predictions
• test hypothesis
• construct concepts and develop theories
• explore issues explain activities or attitudes
• describe what is happening
• present information
• make comparisons to find similarities and differences
• draw conclusions about populations based only on sample results.

Question 17

(Misuse of statistics)
A comparison or analogy that is technically valid but that has little or no practical meaning. Implies that a comparison has been designed to be misleading.

Answer 17

False Analogy

Question 18

(Misuse of statistics)
Misleading labels on a graph.

Answer 18

Biased labelling

Question 19

(Misuse of statistics)
Poor quality samples such as answers to leading questions

Answer 19

Biased samples

Question 20

(Misuse of statistics) Misinterpretations of numbers due to flawed logic.

Answer 20

Cognitive biases

Question 21

(Misuse of statistics)
Looking for patterns in data using brute force methods that try many statistical models until matches are found.

Answer 21

Data Dredging

Question 22

(Misuse of statistics) Graphs and data visualizations that are too complex to be interpreted by the audience. This may prevent data from being challenged and validated.

Answer 22

Overcomplexity

Question 23

(Misuse of statistics) Testing too many theories against data such that random patterns are sure to be found.

Answer 23

Overfitting

Question 24

(Misuse of statistics) A general term for an invalid interpretation of a valid statistic.

Answer 24

Prosecutor's Fallacy

Question 25

(Misuse of statistics) Basing analysis on a statistically insignificant number of samples.

Answer 25

Significance

Question 26

(Misuse of statistics) Overuse of averages in statistical analysis and decision making. In particular, it refers to a situation in which an average is relatively meaningless due to the shape of a data distribution.

Answer 26

Tyranny of Average

Question 27

(Misuse of statistics) It is the observation that processes, procedures and technologies require meaningful input to produce a meaningful result.

Answer 27

Garbage in - Garbage out

Question 28

2 Fields of Statistics

Answer 28

1. Statistical Methods of Applied Statistics 2. Statistical Theory of Mathematical Statistics

Question 29

refer to procedures and techniques used in the collection, presentation, analysis, and interpretation of data.

Answer 29

Statistical Methods of Applied Statistics

Question 30

- methods concerned with the collection, description, and analysis of a set of data without drawing conclusions or inferences about a larger set. - the main concern is simply to describe the set of data such that otherwise obscure information is brought out clearly. - conclusions apply only to the data on hand.

Answer 30

Descriptive statistics

Question 31

- methods concerned with making predictions or inferences about a larger set of data using only the information gathered from a subset of this larger set. - the main concern is not merely to describe but actually predict and make inferences based on the information gathered. - conclusions are applicable to a larger set of data which the data on hand is only a subset.

Answer 31

Inferential Statistics

Question 32

deals with the development and exposition of theories that serve as bases of statistical methods.

Answer 32

Statistical Theory of Mathematical Statistics

Question 33

collection of all elements under consideration in a statistical study.

Answer 33

population

Question 34

numerical characteristic of a population

Answer 34

parameter

Question 35

part or subset of the population from which the information is collected.

Answer 35

sample

Question 36

numerical characteristic of a sample

Answer 36

statistics

Question 37

a characteristic or attribute of persons or objects which can assume different values or labels for different persons or objects under consideration.

Answer 37

variable

Question 38

the process of determining the value or label of a particular variable for a particular experimental or sampling unit.

Answer 38

measurement

Question 39

the individual or object on which a variable is measured.

Answer 39

experimental or sampling unit

Question 40

a numerical recording of information on a variable.

Answer 40

observation

Question 41

a collection of observations.

Answer 41

data

Question 42

Types of variables

Answer 42

Quantitative Qualitative

Question 43

A variable that yields categorical responses.

Answer 43

Qualitative variables

Question 44

Examples of qualitative variables

Answer 44

Examples: Political Affiliation: Democrat, Republican Occupation: Teacher, Doctor, Engineer, etc. Marital Status: Single, Married, Widowed, Separated

Question 45

A variable that takes on numerical values representing an amount or quantity.

Answer 45

Quantitative variables

Question 46

Examples of quantitative variables

Answer 46

Examples: Weight (in kg), Height (in meters), No. of cars (0, 1, 2, …)

Question 47

Quantitative variables may either be ...

Answer 47

discrete or continuous

Question 48

- a variable which can assume finite, or, at most, countably infinite number of values - it is usually measured by counting/enumeration

Answer 48

discrete variables

Question 49

- Can assume infinitely many values corresponding to a line interval or uncountably infinite number of values

Answer 49

continuous variables

Question 50

Levels of measurement

Answer 50

1. Nominal (Classificatory scale) 2. Ordinal (Ranking scale) 3. Interval 4. Ratio

Question 51

weakest level of measurement where numbers or symbols are used simply for categorizing subjects into different groups.

Answer 51

Nominal level (classificatory scale)

Question 52

Examples of variables with nominal measurement

Answer 52

Sex M-Male F-Female Marital status 1-Single 2-Married 3-Widowed 4-Separated

Question 53

level of measurement that contains the properties of the nominal level, and in addition, the numbers assigned to categories of any variable may be ranked or ordered in some low-to-high-manner.

Answer 53

Ordinal level (ranking scale)

Question 54

Example of variables with ordinal measurement

Answer 54

Teaching ratings 1-poor 2-fair 3-good 4-excellent Year level 1 - 1st yr 2 - 2nd yr 3 - 3rd yr 4 - 4th yr

Question 55

- level of measurement which has the properties of the nominal and ordinal levels, and in addition, the distances between any two numbers on the scale are of known sizes. - must have a common and constant unit of measurement. Furthermore, the unit of measurement is arbitrary and there is no “true zero” point.

Answer 55

Interval

Question 56

Examples of variables with interval measurement

Answer 56

Examples: IQ, Temperature (in Celsius)

Question 57

level of measurement contains all the properties of the interval level, and in addition, it has a “true zero” point.

Answer 57

Ratio

Question 58

examples of variables with ratio measurement

Answer 58

Examples: Age (in years), Number of correct answers in an exam

Question 59

Levels or measurement for quantitative variables

Answer 59

Interval and Ratio

Question 60

Levels of measurement for qualitative variables

Answer 60

Nominal and Ordinal