Market research generally involves two different types of research: primary and secondary. Primary research is research you conduct yourself (or hire someone to do for you.) It involves going directly to a source – usually customers and prospective customers in your target market – to ask questions and
gather information. Examples of primary research are: - Interviews (telephone or face-to-face)
- Surveys (online or mail)
- Questionnaires (online or mail)
- Focus groups
- Visits to competitors' locations
When you conduct primary research, you’re typically gathering two basic kinds of information: - Exploratory. This research is general and open-ended, and typically involves lengthy interviews with an individual or small
group.
- Specific. This research is more precise, and is used to solve a problem identified in exploratory research. It involves more structured, formal interviews.
Primary research usually costs more and often takes longer to conduct than secondary research, but it gives conclusive results. Secondary research is a type of research that has already been compiled, gathered, organized and published by others. It includes reports and studies
by government agencies, trade associations or other businesses in your industry. For small businesses with limited budgets, most research is typically secondary, because it can be obtained faster and more affordably than primary research. A lot of secondary research is available right on the Web, simply by entering key words and phrases for the type of information you’re looking for. You can also obtain secondary research by reading articles in magazines, trade journals and industry
publications, by visiting a reference library, and by contacting industry associations or trade organizations. (Note: When you locate the research you want, check its publication date to be sure the data is fresh and not outdated.) One excellent source of secondary research data is government agencies; this data is usually available free of charge. On the other hand, data published by private companies may require permission, and sometimes a fee, for you to access it.
EntryReader's guideEntries A-ZSubject index
Primary Data SourceA primary data source is an original data source, that is, one in which the data are collected firsthand by the researcher for a specific research purpose or project. Primary data can be collected in a number of ways. However, the most common
techniques are self-administered surveys, interviews, field observation, and experiments. Primary data collection is quite expensive and time consuming compared to secondary data collection. Notwithstanding, primary data collection may be the only suitable method for some types of research. Primary Data Sources versus Secondary Data SourcesIn the conduct of research, researchers rely on two kinds of data sources—primary and secondary. The term primary
source is used broadly to embody all sources that are original. This can be contrasted with the term primary data source, ... - Descriptive Statistics
- Central Tendency, Measures of
- Cohen's d Statistic
- Cohen's f Statistic
- Correspondence Analysis
- Descriptive Statistics
- Effect Size, Measures of
- Eta-Squared
- Factor
Loadings
- Krippendorff's Alpha
- Mean
- Median
- Mode
- Partial Eta-Squared
- Range
- Standard Deviation
- Statistic
- Trimmed Mean
- Variability, Measure of
- Variance
- Distributions
- z Distribution
- Bernoulli Distribution
- Copula Functions
- Cumulative Frequency Distribution
- Distribution
- Frequency Distribution
- Kurtosis
- Law of Large Numbers
- Normal Distribution
- Normalizing Data
- Poisson Distribution
- Quetelet's Index
- Sampling Distributions
- Weibull
Distribution
- Winsorize
- Graphical Displays of Data
- Bar Chart
- Box-and-Whisker Plot
- Column Graph
- Frequency Table
- Graphical Display of
Data
- Growth Curve
- Histogram
- L'Abbé Plot
- Line Graph
- Nomograms
- Ogive
- Pie Chart
- Radial Plot
- Residual Plot
- Scatterplot
- U-Shaped Curve
- Hypothesis Testing
- p Value
- Alternative Hypotheses
- Beta
- Critical Value
- Decision Rule
- Hypothesis
- Nondirectional Hypotheses
- Nonsignificance
- Null Hypothesis
- One-Tailed Test
- Power
- Power Analysis
- Significance Level, Concept of
- Significance Level,
Interpretation and Construction
- Significance, Statistical
- Two-Tailed Test
- Type I Error
- Type II Error
- Type III Error
- Important Publications
- “Coefficient Alpha and the Internal Structure of Tests”
- “Convergent and Discriminant Validation by the Multitrait–Multimethod Matrix”
- “Meta-Analysis of Psychotherapy Outcome
Studies”
- “On the Theory of Scales of Measurement”
- “Probable Error of a Mean, The”
- “Psychometric Experiments”
- “Sequential Tests of Statistical Hypotheses”
- “Technique for the Measurement of Attitudes, A”
- “Validity”
- Aptitudes and Instructional Methods
- Doctrine of Chances, The
- Logic of Scientific Discovery, The
- Nonparametric Statistics for the Behavioral Sciences
- Probabilistic Models for Some Intelligence and Attainment Tests
- Statistical Power Analysis for the
Behavioral Sciences
- Teoria Statistica Delle Classi e Calcolo Delle Probabilità
- Inferential Statistics
- Q-Statistic
- R2
- Association, Measures of
- Coefficient of Concordance
- Coefficient of Variation
- Coefficients of Correlation, Alienation, and Determination
- Confidence Intervals
- Margin of Error
- Nonparametric Statistics
- Odds
Ratio
- Parameters
- Parametric Statistics
- Partial Correlation
- Pearson
Product-Moment Correlation Coefficient
- Polychoric Correlation Coefficient
- Randomization Tests
- Regression Coefficient
- Semipartial Correlation Coefficient
- Spearman Rank Order Correlation
- Standard Error of Estimate
- Standard Error of the Mean
- Student's t Test
- Unbiased Estimator
- Weights
- Item Response Theory
- b Parameter
- Computerized Adaptive Testing
- Differential Item Functioning
- Guessing Parameter
- Mathematical Concepts
- Congruence
- General Linear Model
- Matrix Algebra
- Polynomials
- Sensitivity Analysis
- Weights
- Yates's Notation
- Measurement Concepts
- z Score
- Ceiling Effect
- Change Scores
- False Positive
- Gain Scores, Analysis of
- Instrumentation
- Item Analysis
- Item-Test Correlation
- Observations
- Percentile Rank
- Psychometrics
- Random Error
- Raw Scores
- Response Bias
- Rubrics
- Sensitivity
- Social Desirability
- Specificity
- Standardized Score
- Survey
- Test
- True Positive
- Organizations
- American Educational Research Association
- American Statistical Association
- National Council on Measurement in Education
- Publishing
- Abstract
- American Psychological Association Style
- Discussion Section
- Dissertation
- Literature Review
- Methods Section
- Proposal
- Purpose Statement
- Results Section
- Qualitative Research
- Case Study
- Content Analysis
- Discourse Analysis
- Ethnography
- Focus Group
- Interviewing
- Narrative Research
- Naturalistic Inquiry
- Naturalistic Observation
- Qualitative Research
- Think-Aloud Methods
- Reliability of Scores
- Coefficient Alpha
- Correction for Attenuation
- Internal Consistency Reliability
- Interrater Reliability
- KR-20
- Parallel
Forms Reliability
- Reliability
- Spearman–Brown Prophecy Formula
- Split-Half Reliability
- Standard Error of Measurement
- Test–Retest Reliability
- True Score
- Research Design Concepts
- Aptitude-Treatment Interaction
- Cause and Effect
- Concomitant Variable
- Confounding
- Control Group
- Interaction
- Internet-Based Research Method
- Intervention
- Matching
- Natural
Experiments
- Network Analysis
- Placebo
- Replication
- Research
- Research Design Principles
- Treatment(s)
- Triangulation
- Unit of Analysis
- Yoked Control Procedure
- Research Designs
- A Priori Monte Carlo Simulation
- Action Research
- Adaptive Designs in Clinical Trials
- Applied Research
- Behavior Analysis Design
- Block Design
- Case-Only Design
- Causal-Comparative Design
- Cohort Design
- Completely Randomized Design
- Crossover Design
- Cross-Sectional Design
- Double-Blind Procedure
- Ex Post Facto
Study
- Experimental Design
- Factorial Design
- Field Study
- Group-Sequential Designs in Clinical Trials
- Laboratory Experiments
- Latin Square Design
- Longitudinal Design
- Meta-Analysis
- Mixed Methods Design
- Mixed Model
Design
- Monte Carlo Simulation
- Nested Factor Design
- Nonexperimental Design
- Observational Research
- Panel Design
- Partially Randomized Preference Trial Design
- Pilot Study
- Pragmatic Study
- Pre-Experimental Designs
- Pretest–Posttest
Design
- Prospective Study
- Quantitative Research
- Quasi-Experimental Design
- Randomized Block Design
- Repeated Measures Design
- Response Surface Design
- Retrospective Study
- Sequential Design
- Single-Blind Study
- Single-Subject
Design
- Split-Plot Factorial Design
- Thought Experiments
- Time Studies
- Time-Lag Study
- Time-Series Study
- Triple-Blind Study
- True Experimental
Design
- Wennberg Design
- Within-Subjects Design
- Zelen's Randomized Consent Design
- Research Ethics
- Animal Research
- Assent
- Debriefing
- Declaration of Helsinki
- Ethics in the Research Process
- Informed Consent
- Nuremberg Code
- Participants
- Recruitment
- Research Process
- Clinical Significance
- Clinical Trial
- Cross-Validation
- Data Cleaning
- Delphi Technique
- Evidence-Based Decision
Making
- Exploratory Data Analysis
- Follow-Up
- Inference: Deductive and Inductive
- Last Observation Carried Forward
- Planning Research
- Primary Data Source
- Protocol
- Q Methodology
- Research Hypothesis
- Research Question
- Scientific Method
- Secondary Data Source
- Standardization
- Statistical
Control
- Type III Error
- Wave
- Research Validity Issues
- Bias
- Critical Thinking
- Ecological Validity
- Experimenter Expectancy
Effect
- External Validity
- File Drawer Problem
- Hawthorne Effect
- Heisenberg Effect
- Internal Validity
- John Henry Effect
- Mortality
- Multiple Treatment Interference
- Multivalued Treatment Effects
- Nonclassical Experimenter Effects
- Order Effects
- Placebo Effect
- Pretest Sensitization
- Random Assignment
- Reactive Arrangements
- Regression to the Mean
- Selection
- Sequence
Effects
- Threats to Validity
- Validity of Research Conclusions
- Volunteer Bias
- White Noise
- Sampling
- Cluster Sampling
- Convenience Sampling
- Demographics
- Error
- Exclusion Criteria
- Experience Sampling Method
- Nonprobability Sampling
- Population
- Probability
Sampling
- Proportional Sampling
- Quota Sampling
- Random Sampling
- Random Selection
- Sample
- Sample Size
- Sample Size Planning
- Sampling
- Sampling and Retention of Underrepresented Groups
- Sampling Error
- Stratified Sampling
- Systematic Sampling
- Scaling
- Categorical Variable
- Guttman Scaling
- Interval Scale
- Levels of
Measurement
- Likert Scaling
- Nominal Scale
- Ordinal Scale
- Rating
- Ratio Scale
- Thurstone Scaling
- Software Applications
- Databases
- LISREL
- MBESS
- NVivo
- R
- SAS
- Software, Free
- SPSS
- Statistica
- SYSTAT
- WinPepi
- Statistical Assumptions
- Homogeneity of Variance
- Homoscedasticity
- Multivariate Normal Distribution
- Normality Assumption
- Sphericity
- Statistical Concepts
- Autocorrelation
- Biased Estimator
- Cohen's Kappa
- Collinearity
- Correlation
- Criterion Problem
- Critical Difference
- Data Mining
- Data Snooping
- Degrees of Freedom
- Directional Hypothesis
- Disturbance Terms
- Error Rates
- Expected
Value
- Fixed-Effects Model
- Inclusion Criteria
- Influence Statistics
- Influential Data Points
- Intraclass Correlation
- Latent Variable
- Likelihood
Ratio Statistic
- Loglinear Models
- Main Effects
- Markov Chains
- Method Variance
- Mixed- and Random-Effects Models
- Models
- Multilevel
Modeling
- Odds
- Omega Squared
- Orthogonal Comparisons
- Outlier
- Overfitting
- Pooled Variance
- Precision
- Quality Effects Model
- Random-Effects Models
- Regression Artifacts
- Regression
Discontinuity
- Residuals
- Restriction of Range
- Robust
- Root
Mean Square Error
- Rosenthal Effect
- Serial Correlation
- Shrinkage
- Simple Main Effects
- Simpson's Paradox
- Sums of Squares
- Statistical Procedures
- Accuracy in Parameter Estimation
- Analysis of Covariance (ANCOVA)
- Analysis of Variance (ANOVA)
- Barycentric Discriminant Analysis
- Bivariate Regression
- Bonferroni Procedure
- Bootstrapping
- Canonical Correlation Analysis
- Categorical
Data Analysis
- Confirmatory Factor Analysis
- Contrast Analysis
- Descriptive Discriminant Analysis
- Discriminant Analysis
- Dummy Coding
- Effect Coding
- Estimation
- Exploratory Factor Analysis
- Greenhouse–Geisser Correction
- Hierarchical Linear Modeling
- Holm's Sequential Bonferroni Procedure
- Jackknife
- Latent Growth Modeling
- Least Squares, Methods of
- Logistic Regression
- Mean Comparisons
- Missing
Data, Imputation of
- Multiple Regression
- Multivariate Analysis of Variance (MANOVA)
- Pairwise Comparisons
- Path Analysis
- Post Hoc Analysis
- Post Hoc Comparisons
- Principal Components
Analysis
- Propensity Score Analysis
- Sequential Analysis
- Stepwise Regression
- Structural Equation Modeling
- Survival Analysis
- Trend Analysis
- Yates's
Correction
- Statistical Tests
- F Test
- t Test, Independent Samples
- t Test, One Sample
- t Test, Paired Samples
- z Test
- Bartlett's Test
- Behrens–Fisher t′ Statistic
- Chi-Square Test
- Duncan's Multiple Range Test
- Dunnett's Test
- Fisher's Least Significant Difference Test
- Friedman Test
- Honestly Significant Difference (HSD) Test
- Kolmogorov-Smirnov Test
- Kruskal–Wallis Test
- Mann–Whitney U Test
- Mauchly Test
- McNemar's
Test
- Multiple Comparison Tests
- Newman–Keuls Test and Tukey Test
- Omnibus Tests
- Scheffé Test
- Sign Test
- Tukey's Honestly Significant Difference (HSD)
- Welch's t Test
- Wilcoxon Rank Sum Test
- Theories, Laws, and Principles
- Bayes's Theorem
- Central Limit Theorem
- Classical Test Theory
- Correspondence
Principle
- Critical Theory
- Falsifiability
- Game Theory
- Gauss–Markov Theorem
- Generalizability Theory
- Grounded Theory
- Item Response
Theory
- Occam's Razor
- Paradigm
- Positivism
- Probability, Laws
of
- Theory
- Theory of Attitude Measurement
- Weber–Fechner Law
- Types of Variables
- Control Variables
- Covariate
- Criterion Variable
- Dependent Variable
- Dichotomous Variable
- Endogenous
Variables
- Exogenous Variables
- Independent Variable
- Nuisance Variable
- Predictor Variable
- Random Variable
- Significance Level, Concept of
- Significance Level, Interpretation and Construction
- Variable
- Validity of Scores
- Concurrent Validity
- Construct Validity
- Content Validity
- Criterion
Validity
- Face Validity
- Multitrait–Multimethod Matrix
- Predictive Validity
- Systematic Error
- Validity of Measurement
locked icon Sign in to access this contentSign in Get a 30 day FREE TRIALWatch videos from a variety of sources bringing classroom topics to life Read modern, diverse business cases Explore hundreds of books and reference titles
sign up today!
Researchers in the health and social sciences can obtain their data by getting it directly from the subjects they're interested in. This data they collect is called primary data. Another type of data that may help researchers is the data that has already been gathered by someone else. This is called secondary data.
Survey data is defined as the resultant data that is collected from a sample of respondents that took a survey. This data is comprehensive information gathered from a target audience about a specific topic to conduct research.
A questionnaire can is a research instrument that consists of a set of questions to collect information from a respondent. A survey is a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest.
SURVEYS are the most common way to gather primary research.
|
