Chapter 1: The Where, Why, and How of Data Collection

A set of vocabulary flashcards defining fundamental concepts in business statistics, data collection, sampling methods, and data measurement levels.

Business Statistics

A collection of procedures and techniques used to convert data into meaningful information in a business environment.

Descriptive Statistics

The category of business statistics involving procedures and techniques designed to describe data, such as charts, graphs, and numerical measures.

Statistical Inferential Procedures

Procedures that allow a decision maker to reach a conclusion about a set of data based on a subset of that data.

Estimation

An inferential procedure used when it is impractical to obtain or work with all data in a large data set, allowing decision makers to analyze a subset of the data.

Hypothesis Testing

An inferential procedure where claims about products and services are tested using information taken from samples.

Experiment

A process that produces a single outcome whose result cannot be predicted with certainty.

Experimental Design

A plan for performing an experiment in which the variable of interest is defined and one or more factors are identified to be manipulated, changed, or observed so that the impact on the variable of interest can be measured or observed.

Closed-End Questions

Questions that require the respondent to select from a short list of defined choices.

Open-End Questions

Questions that provide the respondent with greater flexibility in answering, though responses can be more difficult to analyze.

Direct Observation

A data collection technique that requires researchers to actually observe the data collection process and record data based on what takes place.

Structured Interviews

Interviews in which the questions are scripted.

Unstructured Interviews

Interviews that begin with one or more broadly stated questions, with further questions being based on the responses.

Bias

An effect that alters a statistical result by systematically distorting it, as opposed to a random error which balances out on average.

Internal Validity

A characteristic of an experiment in which data are collected in such a way as to eliminate the effects of variables within the experimental environment that are not of interest to the researcher.

External Validity

A characteristic of an experiment whose results can be generalized beyond the test environment so that the outcomes can be replicated when the experiment is repeated.

Population

The set of all objects or individuals of interest or the measurements obtained from all objects or individuals of interest.

Sample

A subset of the population.

Census

An enumeration of the entire set of measurements taken from the whole population.

Convenience Sampling

A nonstatistical sampling method where sample selection is based on convenience rather than probability.

Statistical Sampling

Sampling methods (also called probability sampling) that allow every item in the population to have a known or calculable chance of being included in the sample.

Simple Random Sampling

A method of selecting items from a population such that every possible sample of a specified size has an equal chance of being selected.

Systematic Random Sampling

A statistical sampling technique that involves selecting every $k\text{th}$ item in the population after a randomly selected starting point between 1 and $k$.

Stratified Random Sampling

A statistical sampling method in which the population is divided into subgroups called strata so that each population item belongs to only one stratum, and sample items are selected from each stratum using simple random sampling.

Cluster Sampling

A method by which the population is divided into groups, or clusters, that are each intended to be minipopulations, from which a simple random sample of $m$ clusters is selected.

Quantitative Data

Measurements whose values are inherently numerical.

Qualitative Data

Data whose measurement scale is inherently categorical.

Time-Series Data

A set of data values observed at successive points in time.

Cross-Sectional Data

A set of data values observed at a fixed point in time.

Nominal Data

The lowest form of data, generated by assigning codes to categories where the order of the categories is arbitrary.

Ordinal Data

Rank data where elements can be rank-ordered on the basis of some relationship among them, with assigned values indicating this order.

Interval Data

Data where the distance between two items can be measured on a scale with ordinal properties ($>$, $<$, or $=$), but lacking a true zero point.

Ratio Data

The highest level of measurement, containing all the characteristics of interval data plus a true zero point where zero means 'none'.

Big Data

The wealth of new data that organizations collect in many and varied forms.