The careful definition of variables of importance is a fundamental step in any research process. Indeed, the definition and operationalization of key variables are indispensable processes in any scientific research as they enable the researchers to have a closer understanding of the relevant variables, ultimately enabling them to measure the variables (Wimmer & Dominick, 2005).
Most qualitative research processes depend on observations, and such observations cannot be realized if the researcher does not clearly state what is to be observed. According to the hypothesis and research questions, service satisfaction, morale and the desire to quit service forms the dependent variables, while payment levels and tenure conditions form the independent variables. Other independent variables include family stabilization and occupational stress.
Some of the above variables are interrelated to each other. In any research process, it is fundamental to elaborate on the operational definitions and conceptualized relationships between the independent and dependent variables (Wimmer & Dominick, 2005).
The payment level is related to turnover and job satisfaction. The tenure conditions are all related to the dependent variables of turnover, morale, and job satisfaction. Tenure conditions include pay, occupational stress, performance appraisal, the stability of base conditions, levels of training available for the military personnel, and openings for promotion. In this interrelationship, one independent variable is capable of influencing another independent variable to negatively or positively influence a dependent variable. For instance, performance appraisal has the ability to positively impact occupational stress, which in turn curtails turnover levels and boost job satisfaction and morale. The following schema depicts the expected interrelationships.
Data gathering strategies need to be carefully defined in any research process. The choice of data gathering strategies will have a direct bearing on the validity and reliability levels achieved by the study results. In this perspective, it is important to choose appropriate data collection procedures. According to Trochim (2006), qualitative data sources include participant observation, direct observation, focus groups, unstructured questionnaires, documents and texts, case studies and in-depth interviews. This study aims at utilizing three data gathering strategies – focus groups, unstructured questionnaires, and documents.
Focus groups are a qualitative research technique that utilizes structured group discussions and group interviews to learn or obtain details about a defined topic (Mariampolski, 2001; McNamara, 2006). Focus group interviews are specifically useful when the researcher needs to explore the approaches and attitudes of a particular segment of people, and draw out distinct issues previously unknown to him or her.
Open-ended interviews are a form of interviews in which the question and answer groupings are not pre-set or programmed beforehand. This technique must rely on social communication between the respondent and researcher to come up with information (Schensul, Lecompte, & Schensul, 1999).
The authors depicted open-ended interviews as a way to comprehend the multifaceted behavior of individuals without enforcing any form of categorization that can limit the scope of inquiry. The above data gathering techniques are good at measuring the variables mentioned above in addition to measuring the attitudes and opinions of the army personnel. Documents and texts are often used to access valuable information that may not be easily accessible using other means. The following table shows the data gathering strategies for the study and related details.
The above data gathering strategies will enable the researcher to come up with huge amounts of contextually burdened but richly detailed data (Bryne, 2001). The data may also be subjective depending on actual data collection practices. In this respect, the researcher must come up with comprehensive data analysis strategies to ensure the reliability and validity of the study results. The role of the researcher becomes fundamentally important since the clarity and applicability of research findings will sorely depend on his or her ability to use an appropriate data analysis strategy. The resulting qualitative data must be cleaned to eliminate any chances of mistakes or poor quality data.
It must then be identified, coded, and categorized into distinct patterns. There exist many software packages that can assist the researcher in the coding process, data management, and analysis. However, the data interpretation process remains a key prerogative of the researcher. According to Warden (2007), the qualitative analysis should be able to explain the meaning of the analyzed data
The data originating from focus group discussions and unstructured interviews must then be pared down to differentiate major categories or themes that inarguably answer key research questions. The process of paring and sieving data is known as thematic analysis. According to Bryne (2001), this process of data reduction is important in any qualitative research process as it enables the researcher to communicate key research findings simply and efficiently. In qualitative analysis, theories surface as data continues to be collected. These theories should be examined, refined and reexamined against all incoming information until all explanations become repetitive (Warden, 2007).
A data analysis strategy such as constant comparison, also known as grounded theory, can be effectively used to come up with the above categories. In this strategy, indicators of categories in the data sets are identified, named, and coded. These codes are then compared to find consistencies and analogous meanings between them that can be effectively used to answer key research questions (Ratcliff, n.d.).
Using different research approaches in any research process has the ability to generate data that may lead to conflicting interpretations if necessary care is not taken. For instance, using different data collection procedures as reflected in this study can occasion conflicting interpretations, ultimately watering down the study findings. The data collection techniques – focus groups, unstructured interviews, and documents – are concerned with assisting the researcher to collect data that is different in context.
As such, the researcher should take a leading role when it comes to data analysis as crucial research findings will often depend on his ability to interpret the different categories forming from the data sets (Bryne, 2001). The process of paring, coding, and categorizing must also be comprehensive and repeated many times until explanations are repetitive (Warden, 2007).
As already mentioned, findings must be summarized into key concepts and categories with an express objective of answering the research questions. In this study, key research findings can be summarized into two broad categories, namely the influences that positively impact the U.S army and influences that negatively impact the army. Some preferred statements of findings for the study would include: ‘U.S. army frequent transfers: what the government should know’ and ‘key remedies to morale and turnover problems in the US Army.’
Reference List
Bryne, M. (2001). Data analysis strategies for qualitative research – Research Corner. Web.
Mariampolski, H. (2001). Qualitative Market Research: A Comprehensive Guide. Sage Publications, Inc. Web.
McNamara, C. (2006). Basics of Conducting Focus Groups. Web.
Ratcliff, D (n.d.). 15 methods of data analysis in qualitative research. Web.
Schensul, S.L., Lecompte, M.D., & Schensul, J.J. (1999). Essential ethnographic methods: observations, interviews, and questionnaires. Rowman Altamira. Web.
Trochim, W.M.K. (2006). Qualitative methods. Web.
Warden, B.A. (2007). Introduction to qualitative data analysis. Web.
Wimmer, R.D., & Dominick, J.R. (2005). Mass media research: an introduction. Cengage Learning. Web.