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