Validity and Reliability

Validity and Reliability

Validity

The validity of data needs to be carefully checked (. Classifying the data can help the researcher reach important conclusions and uncover the results that led to such conclusions.

Researchers may check for validity in several ways. These include comparing findings of one instrument with findings from other instruments and conducting joint observations or collaborative marking of the same tests. Checking validity could also include returning draft reports to respondents for accuracy checks, considering opposing explanations for the issue or question, and conducting multiple observations of the same event. The researcher can also enhance respondent validity by asking the participants to check their interpretations of the knowledge provided or observed (Kim 2006).

Another option to insure validity when seeking data is to use a pre-designed measurement such as an existing instrument previously tested and found valid. Ensuring validity can be difficult and should be taken seriously and carefully and to show the impact of the collected data on the business. Utilizing such methods add certainty that the data collected is valid and useful for planning and decision making processes (Gibbs 2002).

Critics of questionnaires and interviews focus on poorly created questions (Smith, 1991). The researcher for the proposed study will consider measures to ensure the quality of data. Questions will focus on required assessment. Kim (2006) stressed that the elements of language, difficulty level, and frame of reference should enhance communications between the researcher and the participants. Researchers should have an awareness of the participants' vocabulary to ensure that questions are not oversimplified or too difficult.

Data supplied by a research are used to test the validity of the normative study. Comparisons of optimal and actual order quantities as well as the profit generated by each order quantity provide strong support for the adequacy of the study, that is, support that the study provides normative guidelines that neither replicate standard ordering practice nor deviate from acceptable practice (Bausell 2005).

If data validation programs and messages that are specific tests will be given to different/individual errors. Detailed description of inputs (with the document format or video format takeover), outputs (table or respective video format heads) and files, databases (detailed description of the records, entities, and access keys hierarchy/scale fields). Enter the organization and access, type records, the correct type and extent of data fields. They develop the necessary coding. Procedures Manual: collecting and coding data, making corrections where errors, evaluation and dissemination of results, etc. Phasing implementation components, methods validation, commissioning, defining responsibilities for test files, checkpoints, data processing procedures for the conversion of the old system also, etc (Creswell 2005).

New system design methodology called by many implementation procedures or application/software development, this phase shift means the existing construction and testing programs, the establishment of the first versions of files or databases nuclei and verification, under restricted conditions, the manual procedures designed functionality.

Validity is an index of the value or quality of an empirical science. More research is valid; its results will be more reliable or ...