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Protocol for Collection of Cross-Sectional Data

Cross-Sectional Data

What is Cross-Sectional Data?

In statistics and econometrics, cross-sectional data, also known as a cross section of a sample population, is data obtained by analyzing a large number of subjects (such as people, companies, nations, or regions) at the same time. It’s also possible that the study ignores variations in time. The most common method of analyzing cross-sectional data is to compare differences among selected subjects. Essentially, cross sectional data is information gathered from all participants at the same time. During cross-sectional analysis, time is not taken into account as a study variable. However, it is also true that in a cross-sectional sample, not all participants provide details at the same time. Participants’ cross-sectional data were obtained in a shorter time span. Field cycle is another name for this time span. Time only causes variation in the outcomes, but it is not biased.

Cross-Sectional DataHow do I collect Cross sectional data?

Self-administered questionnaires may be used to obtain cross-sectional data. Researchers may create a survey analysis using one or more of these instruments. A Focus Group Discussion

(FGD), Key Informant Interviews (KIIs), can also be used to collect Cross sectional data.

How do I measure my variables in Cross sectional data collection?

The scale of the variable measured drastically affects the type of analytical techniques that can be used on the data, and what conclusions can be drawn from the data. There are four scales of measurement, nominal, ordinal, interval, and ratio. The least amount of information is contained in nominal scale data, while the most amount of information can be obtained from ratio scale data.

Nominal

Numbers are used as identifiers to mark objects or groups of objects in nominal scales. Except as identifiers, the allocated numbers have no additional significance. It’s worth noting that the order of the identifiers has no bearing here, and the distinction between them is irrelevant. In practice, numbers are sometimes used to describe nominal scale variables instead of letters, but the numbers should not be considered as ordinal, interval, or ratio scale variables. For males, use 1 and for females, use 0.

Ordinal

Ordinal scales extend nominal scales by assigning numbers to items to represent a rank ordering on a given attribute. Since, unlike nominal scale variables, order matters in these variables, the difference in responses is not consistent across the scale or across individuals who respond to the query. For example, frequency of use can be measured as: 1 = use often; 2 = use sometimes; 3 = never use.

Interval

Ordinal scale variables are used to establish interval scales. Numbers are allocated to items in an interval scale such that variations (but not ratios) between them can be meaningfully represented. Temperature (in Celsius or Fahrenheit) is an interval scale variable since the variation between measurements is constant across measurements and is the same anywhere along the scale. Since interval scale variables do not have an absolute zero, ratios of interval scale variables have little significance. The Kelvin temperature scale, on the other hand, is a ratio scale variable since its zero value is absolute zero, which means that nothing can be measured at that temperature.

Ratio

Ratio scales have all of the characteristics of interval scale variables plus one more: they have an absolute “zero” stage. A ratio scale, for example, is traffic density (measured in vehicles per kilometer). When there are no vehicles in a chain, the density of the link is zero. Other ratio scale variables include the number of vehicles in a queue, a person’s height, the distance traveled, the rate of accidents, and so on.

What is Protocol for Collection of cross-sectional Data?

Cross sectional data obtained from primary source can be used for this situation Analysis, academic research, among others. This can obtain from the respondents (vary based on the researcher). Ethical clearance should be gotten by the researcher from relevant institutions where needed. A recognizance survey should be conducted to ensure validity and reliability of the survey instrument.  An updated sampling frame is required for random selection of respondents. Trained enumerators usually from the survey areas should be retrained and contracted for data collection.

Author

Daniel PETER
Ph.D. Agricultural Economics (in view)
Research and Business Development Expert

G- Consulting International Services

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