CSA

Focus on these four lists first. Your reviewer identifies them for the enumeration part. Write five items when the question asks for five. CDA_Midterm_Reviewer(5).pdf

1. Methods of collecting primary data

  1. Observation

  2. Interviews

  3. Diaries

  4. Questionnaires

  5. Experiments

2. Sources of secondary data

  1. Government statistics

  2. Financial statements

  3. Existing databases

  4. Company records

  5. Published datasets

3. Characteristics of a strong research question

  1. Clear

  2. Focused

  3. Researchable

  4. Significant

  5. Feasible

Also know Aligned, the sixth characteristic in the reviewer.

4. Forms of qualitative data

  1. Interview transcripts

  2. Focus-group transcripts

  3. Open-ended survey responses

  4. Diaries and personal accounts

  5. Field notes

Other forms: observations, documents, photographs, and audio/video data.

Other lists to study after the four above

These are additional lists from the reviewer, not confirmed enumeration questions.

MODULE 1

Research pipeline

  1. Frame

  2. Design

  3. Collect

  4. Prepare

  5. Explore

  6. Analyze

  7. Interpret

  8. Report

Shorter research routine

Question → Measure → Prepare → Analyze → Interpret → Report

Variable roles

  1. Independent variable

  2. Dependent variable

  3. Mediator

  4. Moderator

  5. Controls

Measurement sequence

Construct → Variable → Indicators/items

Data structures

  1. Survey standard

  2. Panel

  3. Wide format

  4. Long format

Eight codebook elements

  1. Variable

  2. Construct

  3. Question text

  4. Type

  5. Scale

  6. Coding

  7. Missing-value indicator

  8. Source

Screening checks

  1. Duplicates

  2. Missing observations

  3. Impossible values

  4. Inconsistent coding

  5. Invalid categories

  6. Incorrect data types

  7. Suspicious patterns

Outlier-handling sequence

Detect → Investigate → Decide → Document

Interpretation considerations

  1. Direction

  2. Magnitude

  3. Uncertainty

  4. Practical meaning

  5. Design limits

MODULE 2

Internal secondary-data sources

  1. Sales records

  2. Employee records

  3. Accounting data

  4. CRM databases

  5. Administrative records

External secondary-data sources

  1. Government statistics

  2. Industry reports

  3. Academic databases

  4. International organizations

  5. Published datasets

Criteria for choosing a data source

  1. Measurement fit

  2. Population coverage

  3. Currency

  4. Bias

  5. Legal and ethical use

Source-audit checks

  1. Authority

  2. Purpose

  3. Methodology

  4. Population

  5. Definitions

  6. Time

  7. Coverage

  8. Consistency

Interview structures

  1. Structured

  2. Semi-structured

  3. Unstructured/in-depth

Questionnaire sections

  1. Introduction/consent

  2. Screening questions

  3. Main research variables

  4. Demographics/profile

  5. Closing statement

Response-scale formats

  1. Binary

  2. Multiple choice

  3. Frequency

  4. Likert-type agreement

Types of validity

  1. Content

  2. Construct

  3. Criterion-related

Types of reliability

  1. Internal consistency

  2. Test–retest

  3. Inter-rater

Common types of bias

  1. Selection bias

  2. Nonresponse bias

  3. Recall bias

  4. Social desirability bias

  5. Interviewer bias

  6. Observer bias

Ethical principles

  1. Informed consent

  2. Voluntariness

  3. Privacy and confidentiality

  4. Data minimization

  5. Appropriate storage

  6. Protection from harm

MODULE 3

Five requirements of research

  1. Question

  2. Method

  3. Evidence

  4. Analysis

  5. Interpretation

Research-question types

  1. Descriptive

  2. Comparative

  3. Relational

  4. Predictive

  5. Exploratory

Research-gap types

  1. Population

  2. Context

  3. Methodological

  4. Theoretical

  5. Evidence

Research philosophies

  1. Positivism

  2. Interpretivism

  3. Pragmatism

Research approaches

  1. Quantitative

  2. Qualitative

  3. Mixed

Research strategies

  1. Survey

  2. Case study

  3. Experiment

  4. Ethnography

  5. Archival analysis

Research-design purposes

  1. Exploratory

  2. Descriptive

  3. Explanatory

  4. Predictive

Time horizons

  1. Cross-sectional

  2. Longitudinal

Probability-sampling techniques

  1. Simple random

  2. Systematic

  3. Stratified

  4. Cluster

Non-probability-sampling techniques

  1. Convenience

  2. Purposive

  3. Quota

  4. Snowball

Feasibility constraints

  1. Participant access

  2. Data availability

  3. Cost

  4. Time

  5. Technical skills

  6. Software and infrastructure

MODULE 4

Levels of measurement

  1. Nominal

  2. Ordinal

  3. Interval

  4. Ratio

Categorical-variable types

  1. Nominal

  2. Ordinal

Numerical-variable types

  1. Discrete

  2. Continuous

Measures of central tendency

  1. Mean

  2. Median

  3. Mode

Measures of dispersion

  1. Range

  2. Interquartile range (IQR)

  3. Variance

  4. Standard deviation

Distribution shapes

  1. Symmetric

  2. Positively skewed

  3. Negatively skewed

  4. Multimodal

Main chart types

  1. Bar chart

  2. Histogram

  3. Boxplot

  4. Scatterplot

  5. Line chart

  6. Stacked bar

Time-pattern concepts

  1. Trend

  2. Seasonality

  3. Structural break

Checks before comparing groups

  1. Sample size per group

  2. Mean or median per group

  3. Spread within each group

  4. Distribution shape per group

Parts of reporting a result

  1. What was measured and its level

  2. Valid sample size (n)

  3. Summary statistic

  4. Comparison or pattern

  5. Uncertainty or limitation

  6. Interpretation in relation to the research question

Module 4 outlier sequence

Detect → Investigate → Justify → Document

MODULE 5

Qualitative-analysis sequence

Raw excerpt → Code → Category → Theme

Types of codes

  1. Descriptive

  2. Process

  3. Emotion/value

  4. Interpretive

Coding approaches

  1. Deductive

  2. Inductive

Coding cycles

  1. First-cycle coding

  2. Second-cycle coding

Five qualitative-codebook fields

  1. Code

  2. Definition

  3. What it includes

  4. What it excludes

  5. An example

Analytical aids

  1. Analytic memos

  2. Case summaries

  3. Matrices

  4. Diagrams and concept maps

Credibility-building practices

  1. Prolonged engagement

  2. Triangulation

  3. Negative-case analysis

  4. Peer discussion

  5. Transparent coding

Trustworthiness criteria

  1. Credibility

  2. Transferability

  3. Dependability

  4. Confirmability

Types of triangulation

  1. Participant

  2. Method

  3. Researcher

  4. Data source

  5. Theoretical

Parts of reporting a theme

  1. Interpretive claim

  2. Pattern across the data

  3. Supporting quotations

  4. Variation or negative case

  5. Connection to the research question

MODULE 6

Standards of an analysis-ready dataset

  1. Clean

  2. Consistent

  3. Complete enough

  4. Traceable

Basic dataset structure

  1. One row per observation

  2. One column per variable

  3. One header row

Possible actions for missing data

  1. Leave missing

  2. Exclude a case for a specific analysis

  3. Investigate the source

  4. Recode documented missing codes

  5. Impute only when methodologically justified

Causes of lookup failure

  1. Extra spaces

  2. Numbers stored as text

  3. Duplicated keys

  4. Misspelled values

  5. Records missing from one table

Recommended workbook tabs

  1. 01_Raw

  2. 02_Dictionary

  3. 03_Clean

  4. 04_Checks

  5. 05_Analysis

  6. 06_Report

Transformation-log details

  1. Step

  2. Variable

  3. Action

  4. Reason

  5. Number of records affected

JOURNAL CRITIQUE

Seven prompts

  1. Problem/objective

  2. Method fit

  3. Findings versus questions

  4. Strengths and limitations

  5. Conclusions

  6. One realistic improvement or extension

  7. Final verdict

Writing pattern

Claim → Evidence → Course concept

Your study priority: Memorize the first four lists before spending time on the additional lists.