Chapter 2 Statistical data collection

Statistical Data: A sequence of observation, made on a set of objects included in the sample drawn from population is known as statistical data. BILL $

Statistical Data: (1) Ungrouped Data: Data which have been arranged in a systematic order are called raw data or ungrouped data. (2) Grouped Data: Data presented in the form of frequency distribution is called grouped data.

Collection of Data: The first step in any enquiry (investigation) is collection of data. The data may be collected for the whole population or for a sample only. It is mostly collected on sample basis. Collection of data is very difficult job. The enumerator or investigator is the well trained person who collects the statistical data. The respondents (information) are the persons whom the information is collected.

Types of Data: There are two types (sources) for the collection of data. (1) Primary Data (2) Secondary Data

(1) Primary Data: The primary data are the first hand information collected, compiled and published by organization for some purpose. They are most original data in character and have not undergone any sort of statistical treatment. Example: Population census reports are primary data because these are collected, complied and published by the population census organization.

(2) Secondary Data: The secondary data are the second hand information which are already collected by some one (organization) for some purpose and are available for the present study. The secondary data are not pure in character and have undergone some treatment at least once. Example: Economics survey of England is secondary data because these are collected by more than one organization like Bureau of statistics, Board of Revenue, the Banks etc…

Methods of Collecting Primary Data: Primary data are collected by the following methods: Personal Investigation: The researcher conducts the survey him/herself and collects data from it. The data collected in this way is usually accurate and reliable. This method of collecting data is only applicable in case of small research projects. Through Investigation: Trained investigators are employed to collect the data. These investigators contact the individuals and fill in questionnaire after asking the required information. Most of the organizing implied this method. Collection through Questionnaire: The researchers get the data from local representation or agents that are based upon their own experience. This method is quick but gives only rough estimate. Through Telephone: The researchers get information through telephone this method is quick and give accurate information.

Methods of Collecting Secondary Data: The secondary data are collected by the following sources: Official: e.g. The publications of the Statistical Division, Ministry of Finance, the Federal Bureaus of Statistics, Ministries of Food, Agriculture, Industry, Labor etc… Semi-Official: e.g. State Bank, Railway Board, Central Cotton Committee, Boards of Economic Enquiry etc… Publication of Trade Associations, Chambers of Commerce etc… Technical and Trade Journals and Newspapers. Research Organizations such as Universities and other institutions.

Difference between Primary and Secondary Data: The difference between primary and secondary data is only a change of hand. The primary data are the first hand data information which is directly collected form one source. They are most original data in character and have not undergone any sort of statistical treatment while the secondary data are obtained from some other sources or agencies. They are not pure in character and have undergone some treatment at least once.

For Example: Suppose we interested to find the average age of MS students. We collect the age’s data by two methods; either by directly collecting from each student himself personally or getting their ages from the university record. The data collected by the direct personal investigation is called primary data and the data obtained from the university record is called secondary data.

Editing of Data: After collecting the data either from primary or secondary source, the next step is its editing. Editing means the examination of collected data to discover any error and mistake before presenting it. It has to be decided before hand what degree of accuracy is wanted and what extent of errors can be tolerated in the inquiry. The editing of secondary data is simpler than that of primary data.

Levels of Measurement Data may be classified into four classes or levels of measurement: nominal, ordinal, interval, and ratio. Nominal level: Data that is classified and counted.  EXAMPLES: eye colour, gender, religious affiliation.

Levels of Measurement Mutually exclusive: An individual, object, or measurement is included in only one category. Exhaustive: Each individual, object, or measurement must appear in one of the categories. Nominal data have no particular order or rank and are mutually exclusive.

Marital Status 2000 (population 15 years and older) StatusNumber Single (never married)7,285,560 Married (legally, separated, common- law) 14,614,564 Divorced1,452,000 Widowed1,527,075 Total24,879,199 These data are a nominal level of measurement because it can only be classified into classes and the order of the marital status is not important. The classes are also mutually exclusive and exhaustive.

Levels of Measurement Ordinal level: involves data arranged in some order, but the differences between data values cannot be determined or are meaningless.  EXAMPLE: During a taste test of 4 soft drinks, Mountain Dew was ranked number 1, Sprite number 2, Seven-up number 3, and Orange Crush number 4.

Levels of Measurement Interval level is similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point.  EXAMPLE: Temperature on the Celsius scale. 0 degrees does not represent the absence of temperature, just that it is cold!

Levels of Measurement Ratio level is the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.  EXAMPLES: Monthly income of surgeons, or distance traveled by manufacturer’s representatives per month.

Golden Rules 1.Understand the goals of the project. 2.Keep the survey simple. 3.Field/Pilot-test the survey. 4.Consider the demographic characteristics of the respondents. 5.Visual appeal.

Golden Rule #1 Understand the Project Goals A comprehensive understanding of the project goals paves the way for successful surveying. Without that understanding, it is impossible to appropriately design and to maximize the power of the survey.

Golden Rule #2 Keep It Simple Avoid the temptation to add a couple of questions just in case you did not cover all the bases. Only ask questions that fit the scheme of your project. In this case, oftentimes, less is more. The design focus should be quality not quantity.

Golden Rule #3 Field/Pilot Test Pre-testing the survey enhances clarity, removes word meaning obscurity, establishes approximate time to completion, proper sequencing, question transitioning, and helps assure that questions are not too difficult.

Golden Rule #4 Respondent Demographic Characteristics It is important to know what and how demographic characteristics may impact the interpretation and meaning of certain questions. Variations truly exist from one setting to the next.

Golden Rule #5 Visual Appeal Appearance means everything. If the survey appears complex and difficult to follow with the eyes, respondents will be less likely to do a good job. It should appear to be short, organized, and easy to answer.

Questionnaire Design

Questionnaires in Clinical Research Much of the data in clinical research is gathered using questionnaires or interviews. The validity of the results depends on the quality of these instruments. –Good questionnaires are difficult to construct; bad questionnaires are difficult to analyze. Difficult to design for several reasons: –Each question must provide a valid and reliable measure. –The questions must clearly communicate the research intention to the survey respondent. –The questions must be assembled into a logical, clear instrument that flows naturally and will keep the respondent sufficiently interested to continue to cooperate.

Quality aims in survey research Goal is to collect information that is: Valid: measures the quantity or concept that is supposed to be measured Reliable: measures the quantity or concept in a consistent or reproducible manner Unbiased: measures the quantity or concept in a way that does not systematically under- or overestimate the true value Discriminating: can distinguish adequately between respondents for whom the underlying level of the quantity or concept is different

Steps to design a questionnaire: 1.Write out the primary and secondary aims of your study. 2.Write out concepts/information to be collected that relates to these aims. 3.Review the current literature to identify already validated questionnaires that measure your specific area of interest. 4.Compose a draft of your questionnaire. 5.Revise the draft. 6.Assemble the final questionnaire.

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