Data Collection, Analysis & Sampling Methods

5 Questions

Match the data analysis technique with its description:

Histograms = Visual representation of the distribution of a dataset Regression analyses = Examines the relationship between variables Percentiles = Divides a dataset into 100 equal parts Standard deviations = Measures the dispersion of data points

Match the data validity concept with its definition:

Data validity = How well data represents the actual phenomenon under investigation Triangulation = Combining diverse data from multiple sources to validate findings Pretesting = Exposing surveys to individuals for benchmarking future results Reliability = Assesses consistency of measurements over time or similar situations

Match the statistical software package with its primary usage:

R = Statistical computing and graphics SPSS = Statistical analysis for social sciences SAS = Statistical analysis and data management MATLAB = Numerical computing and algorithm development

Match the sampling method with its description:

Cluster sampling = Divides population into clusters and randomly selects some clusters for sampling Systematic sampling = Selects units following an ordered sequence, like male-female alternation Stratified sampling = Divides population into subgroups and samples from each subgroup Multistage sampling = Combines different sampling methods in stages to select the final sample

Match the survey technique with its purpose:

Inter-rater agreement = Ensures consistency among coders working on transcriptions Representative sample = Aims to accurately represent the total population with smaller samples Agreed-upon definitions = Ensures consistent application of key constructs throughout the work Randomization in selection = Gives every member of a population an equal chance of being selected

Study Notes

Data Collection and Survey Methods

Data collection is the process of gathering information from various sources to answer specific questions or solve problems. A common approach to data collection involves surveys, which can take different forms depending on the context and purpose of the study. Surveys can be administered through structured questionnaires, semi-structured interviews, observation, focus groups, case studies, and other methods. They fall into two general categories: quantitative and qualitative research methods.

Quantitative research focuses on measuring variables numerically using statistical techniques. It aims to collect objective, empirical evidence based on numerical data and uses mathematical models to test hypotheses. On the other hand, Qualitative research emphasizes understanding people's thoughts, experiences, behaviors, attitudes, opinions, beliefs, motivations, emotions, and actions within their real-life contexts. This type of research often relies on open-ended questions rather than closed-ended ones found in conventional polling or market surveys. For example, a qualitative method could involve participants sharing stories and narratives while being recorded and later transcribed by researchers who analyze them.

Data Analysis Techniques

Once collected, data needs to be analyzed to extract meaningful insights and make informed decisions. Commonly used data analysis techniques include descriptive statistics such as averages, medians, percentiles, standard deviations, histograms, box plots, and scatterplots. Inferential statistics like hypothesis testing, correlation coefficients, regression analyses, analysis of variance (ANOVA), and principal components analysis help draw conclusions beyond what one might observe with just basic summary measures. Statistical software packages like R, SAS, SPSS, Stata, and MATLAB facilitate this process.

Data Validity & Reliability

Ensuring the accuracy and trustworthiness of the collected data is crucial for any project. Data validity refers to how well it represents the actual phenomenon under investigation. To improve data validity, investigators may engage in pretesting where they expose surveys or tests to individuals whose answers can serve as benchmarks against which future results can be compared. Another technique involves triangulation, which means combining diverse types of data obtained from multiple sources to validate each other's findings.

Reliability concerns consistency; it assesses whether a measure provides consistent measurements over time or when applied to similar situations. One way to enhance reliability is by using agreed-upon definitions of key constructs, making sure they apply consistently across all parts of the work. This helps ensure interrater agreement among coders working together on transcriptions.

Sampling Methods

In many instances, it's impractical to gather information from every possible unit relevant to a given population. Therefore, we sample units randomly or systematically from that population to represent the whole group. Randomization—including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling—is designed so that every member of the target population has an equal chance of being selected. Systematic sampling also includes non-random selection patterns that follow ordered sequences, such as alternating male-female gender order.

Sampling requires careful planning and thoughtful choices about what would constitute a representative segment of your total population to give you accurate results from smaller samples. Choosing appropriate samples is essential because samples represent much larger populations without necessarily including every single individual within those groups.