Unit 1: Introduction Flashcards
Research and Statistics
It is a systematic inquiry to describe, explain, predict and control the observed
phenomenon.
Research
2 methods involved in research
Inductive and deductive methods
methods used to analyze the observed phenomenon
inductive
methods used to verify the observed phenomenon.
deductive
Types of research
- Qualitative vs. quantitative
- Descriptive vs. Inferential
- Experimental vs. Quasi-experimental
- Causal-comparative vs. Correlational
Explain qualitative vs. quantitative research
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.
Explain descriptive vs. inferential research
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.
Explain experimental vs. quasi-experimental
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.
Explain causal comparative vs. correlational
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.
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.
research design
Types of research designs
- Case study
- Experimental design
- Cross-sectional design
- Longitudinal design
Explain what case study is (rs design)
Focuses on a single case rather than dealing with a sample of a large population.
Explain what experimental design is (rs design)
- 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.
Explain what cross-sectional design is (rs design)
- 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.
Explain what longitudinal design is (rs design)
- 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.
Roles of statistics
validity
analysis
efficiency
Uses of statistics
- 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.
(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.
False Analogy
(Misuse of statistics)
Misleading labels on a graph.
Biased labelling
(Misuse of statistics)
Poor quality samples such as answers to leading questions
Biased samples
(Misuse of statistics) Misinterpretations of numbers due to flawed logic.
Cognitive biases
(Misuse of statistics)
Looking for patterns in data using brute force methods that try many statistical models until matches are found.
Data Dredging
(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.
Overcomplexity
(Misuse of statistics) Testing too many theories against data such that random patterns are sure to be found.
Overfitting
(Misuse of statistics)
A general term for an invalid interpretation of a valid statistic.
Prosecutor’s Fallacy
(Misuse of statistics) Basing analysis on a statistically insignificant number of samples.
Significance
(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.
Tyranny of Average
(Misuse of statistics)
It is the observation that processes, procedures and technologies require meaningful input to produce a meaningful result.
Garbage in - Garbage out
2 Fields of Statistics
- Statistical Methods of Applied Statistics
- Statistical Theory of Mathematical Statistics
refer to procedures and techniques used in the collection, presentation, analysis, and interpretation of data.
Statistical Methods of Applied Statistics
- 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.
Descriptive statistics
- 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.
Inferential Statistics
deals with the development and exposition of theories that serve as bases of statistical methods.
Statistical Theory of Mathematical Statistics
collection of all elements under consideration in a statistical study.
population
numerical characteristic of a population
parameter
part or subset of the population from which the information is collected.
sample
numerical characteristic of a sample
statistics
a characteristic or attribute of persons or objects which can assume different values or labels for different persons or objects under consideration.
variable
the process of determining the value or label of a particular variable for a particular experimental or sampling unit.
measurement
the individual or object on which a variable is
measured.
experimental or sampling unit
a numerical recording of information on a variable.
observation
a collection of observations.
data
Types of variables
Quantitative
Qualitative
A variable that yields categorical responses.
Qualitative variables
Examples of qualitative variables
Examples:
Political Affiliation: Democrat, Republican
Occupation: Teacher, Doctor, Engineer, etc.
Marital Status: Single, Married, Widowed, Separated
A variable that takes on numerical values representing an amount or quantity.
Quantitative variables
Examples of quantitative variables
Examples:
Weight (in kg),
Height (in meters),
No. of cars (0, 1, 2, …)
Quantitative variables may either be …
discrete or continuous
- a variable which can assume finite, or, at most, countably infinite number of values
- it is usually measured by counting/enumeration
discrete variables
- Can assume infinitely many values corresponding to a line interval or uncountably infinite number of values
continuous variables
Levels of measurement
- Nominal (Classificatory scale)
- Ordinal (Ranking scale)
- Interval
- Ratio
weakest level of measurement where numbers or symbols are used simply for categorizing subjects into different groups.
Nominal level (classificatory scale)
Examples of variables with nominal measurement
Sex
M-Male
F-Female
Marital status
1-Single
2-Married
3-Widowed
4-Separated
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.
Ordinal level (ranking scale)
Example of variables with ordinal measurement
Teaching ratings
1-poor
2-fair
3-good
4-excellent
Year level
1 - 1st yr
2 - 2nd yr
3 - 3rd yr
4 - 4th yr
- 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.
Interval
Examples of variables with interval measurement
Examples: IQ, Temperature (in Celsius)
level of measurement contains all the properties of the interval level, and in addition, it has a “true zero” point.
Ratio
examples of variables with ratio measurement
Examples: Age (in years), Number of correct answers in an exam
Levels or measurement for quantitative variables
Interval and Ratio
Levels of measurement for qualitative variables
Nominal and Ordinal
