Ensure Research Validity a Complete Guide How to Avoid Common Pitfalls in Academic Studies

How to Ensure Research Validity is a Complete Guide How to Avoid Common Pitfalls in Academic Studies. To ensure the validity of research, it is necessary to plan carefully with clear questions and methods, use valid and reliable instruments, employ appropriate samples, maintain transparency and consistency of data, and consider the study’s limitations.

How to Ensure Research Validity A Complete Guide to Avoiding Common Pitfalls in Academic Studies

Avoiding common errors, such as imprecise research questions, insufficient literature reviews, or biased samples, increases the accuracy and credibility of the results, making them more useful and reliable. Choose measurement tools, such as surveys or questionnaires, that are proven to be reliable and valid for measuring your intended concepts.

How Ensuring Validity

It is very easy to slip into invalidity; it is both insidious and pernicious as it can enter at every stage of a piece of research. The attempt to build out invalidity is essential if the researcher is to be able to have confidence in the elements of the research plan, data acquisition, data processing analysis, interpretation and its ensuing judgements.

Design Stage

At the design stage threats to validity can be minimized by:

• Choosing an appropriate time scale.
• Ensuring that there are adequate resources for the required research to be undertaken.
• Selecting an appropriate methodology for answering the research questions.
• Selecting appropriate instrumentation for gathering the type of data required.
• Using an appropriate sample (e.g. one which is representative, not too small or too large).

Types of Validity

• Demonstrating internal, external, content, concurrent and construct validity ‘operationalizing,’ the constructs fairly.
• Ensuring reliability in terms of stability (consistency, equivalence, split-half analysis of test material).
• Selecting appropriate foci to answer the research questions.
• Devising and using appropriate instruments (for example, to catch accurate, representative, relevant and comprehensive data (king, Morris and Fitz-gibbon, 1987)); ensuring that read ability levels are appropriate; avoiding any ambiguity of instructions, terms and questions; using instruments that will catch the complexity of issues; avoiding leading questions; ensuring that the level of test is appropriate—e.g.Neither too easy nor too difficult; avoiding test items with little discriminability; avoiding making the instruments too short or too long; avoiding too many or too few items for each issue.

• Avoiding a biased choice of researcher or research team (e.g. Insiders or outsiders as re searchers).

Data Collection

There are several areas where invalidity or bias might creep into the research at the stage of data gathering; these can be minimized by:

• Reducing the Hawthorne effect.

Reactivity Effects

• Minimizing reactivity effects (respondents behaving differently when subjected to scrutiny or being placed in new situations, for example, the interview situation—we distort people’s lives in the way we go about studying them (Lave and Kvale, 1995:226)).
• Trying to avoid dropout rates amongst respondents.
• Taking steps to avoid non-return of questionnaires.
• Avoiding having too long or too short an interval between pretests and post-tests.
• Ensuring inter-rater reliability.
• Matching control and experimental groups fairly.
• Ensuring standardized procedures for gathering data or for administering tests.
• Building on the motivations of the respondents.
• Tailoring the instruments to the concentration span of the respondents and addressing other situational factors (e.g. Health, environment, noise, distraction, threat).
• Addressing factors concerning the researcher (particularly in an interview situation); for example, the attitude, gender, race, age, personality, dress, comments, replies, questioning technique, behavior, style and non-verbal communication of the researcher.

Data Reporting

At the stage of data analysis there are several areas where invalidity lurks. these might be minimized by:

• Using respondent validation.
• Avoiding subjective interpretation of data (e.g. Being too generous or too ungenerous in the award of marks), i.e. Lack of standardization and moderation of results.
• Reducing the halo effect, where the researcher’s knowledge of the person or knowledge of other data about the person or situation exerts an influence on subsequent judgements.
• Using appropriate statistical treatments for the level of data (e.g. Avoiding applying techniques from interval scaling to ordinal data or using incorrect statistics for the type, size, complexity, sensitivity of data).
• Recognizing spurious correlations and extraneous factors which may be affecting the data (i.e. Tunnel vision).
• Avoiding poor coding of qualitative data.
• Avoiding making inferences and generalizations beyond the capability of the data to support such statements.
• Avoiding the equating of correlations and causes.
• Avoiding selective use of data.
• Avoiding unfair aggregation of data (particularly of frequency tables).
• Avoiding unfair telescoping of data (degrading the data).

Type I and Type II Errors

• Avoiding Type I and/or Type II errors.

A Type I error is committed where the researcher rejects the null hypothesis when it is in fact true (akin to convicting an innocent person (Mitchell and Jolley, 1988:121)). this can be addressed by setting a more rigorous level of significance (e.g. ρ<0.01 rather than ρ<0.05). A Type II error is committed where the null hypothesis is accepted when it is in fact not true (akin to finding a guilty person innocent (Mitchell and Jolley, ibid.)).

Boruch (1997:211) suggests that a Type II error may occur if:

(a) the measurement of a response to the intervention is insufficiently valid.

(b) the measurement of the intervention is insufficiently relevant.

(c) the statistical power of the experiment is too low.

(d) the wrong population was selected for the intervention.

Building a Comprehensive Validity Framework

A Type II error can be addressed by reducing the level of significance (e.g. ρ<0.20 or ρ<0.30 rather than ρ<0.05). Of course, the more one reduces the chance of a Type I error the more chance there is of committing a Type II error, and vice versa. In qualitative data a Type I error is committed when a statement is believed when it is, in fact, not true, and a Type II error is committed when a statement is rejected when it is in fact true.

Key Takeaways for Valid Research

At the stage of data reporting invalidity can show itself in several ways. the researcher must take steps to minimize this by, for example:

• Avoiding using data very selectively and unrepresentatively (for example, accentuating the positive and neglecting or ignoring the negative).
• Indicating the context and parameters of the research in the data collection and treatment, the degree of confidence which can be placed in the results, the degree of context-freedom or context-boundedness of the data (i.e. The level to which the results can be generalized).
• Presenting the data without misrepresenting their message.
• Making claims which are sustainable by the data.
• Avoiding inaccurate or wrong reporting of data (i.e. Technical errors or orthographic errors).
• Ensuring that the research questions are answered. releasing research results neither too soon or too late.

Having identified the realms in which invalidity lurks, the researcher can take steps to ensure that, as far as possible, invalidity has been minimized in all areas of the research.

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