The study on the impact of national culture on organizational performance and leadership styles required the researcher to conduct comprehensive research to establish the prevailing facts on the subject. However, this meant that a quantitative research model was imperative. The study required gathering data, evaluating and analyzing information to acquire accurate results. Data was collected from the respondents considering the diverse responses stipulated in the research questionnaire. The questionnaire was composed of 54 research questions. Each of the questions had at least two sets of responses (yes or no) while others had five different responses.
Given that there were 19 respondents, the amount of data collected was overwhelming since each person had different views regarding each question. Both genders were included in the research and this fostered the study. The resources allocated for the research were limited. For instance, time allocated was a challenging constraint in realizing the study results. In response to a large amount of data, it became essential to employ a tool that would facilitate accuracy while observing the research timeline. In this respect, the use of computer data analyzers was inevitable.
The availability of Microsoft Office 2007 helped put the information in a single database. To start with, the data that was on hardcopy questionnaires was entered into the computer in their respective formats, text, and numbers depending on the question asked. In this regard, Microsoft Excel 2007 was instrumental in facilitating data entry. The workbook had 55 sheets each for every question, one for the combined information (base worksheet), Independent variables, Test outcomes, key constituents, Discriminant measurement, and Performance regressions. The base worksheet is composed of seven columns namely the serial number, Question number, Overall percentage, and the remaining four for statistical analysis, as well as 49 rows for each of the questions starting from Q6 to Q54. Indeed, this was necessary given that the filled questionnaires were received depending on the capacity of the respondent to fill and return them. When a questionnaire was received, the data was entered into the database and the researcher did any necessary rectification. The strategy helped in identifying any ensuing mistakes or data gaps and instant rectification was done on such entries before storing the entries in the database. Inherently, the measure ensured that the data stored was accurate and would generate perfect results.
Once all the questionnaires were returned and the primary information captured electronically, it was necessary to organize data according to specific criteria. The initial step was to sort the data according to gender. This was facilitated by the ‘Sort and filter’ Excel tool. The data sorted according to gender was then pasted into different sheets. For instance, one for female respondents and another for male respondents, and they were labeled accordingly. There were 5 females and 14 male respondents respectively. Using Excel formulas, the total number of respondents divided the total number of each gender. From the study, 100% was the benchmark used to give the percentage of each gender. The results obtained showed 73.68 % male and 26.31% female respondents respectively. By ‘filtering’ the data according to names, it was observed that all the respondents gave both the first name and surname.
The information on the combined gender was then filtered according to occupational positions. All the respondents had filled their respective positions. The filtering was also done for every sheet with the results being entered in the base worksheet. When all the sheets were completed and the results were made available in the base worksheet, the data was ready for analysis both qualitatively and quantitatively.
The qualitative data included gender, names, occupational positions, dates, service duration, whether the company encouraged innovative culture, and the adopted national culture. However, these posed some hindrances in achieving the goals of the stakeholders through the researcher analyzed it manually. The question of whether the leader’s efforts meant to help employees in accomplishing their tasks as well as development plans was sufficient and rewarding (question 33). Nevertheless, it was demanding as the researcher devised a rating on the opinions expressed. The researcher allocated points to the opinions on a scale of 0 to 5. An answer to affirmative action was allocated 5 points while one in the negative confirmation was allocated zero. To realize this, the researcher utilized content and logical analysis techniques that required the integration of knowledge and skills in analyzing information that does not have numbers. Using the Excel formulas, the aggregate was found and incorporated in the tool that would give the final analysis of the data (SPSS). The activity consumed time allocated to the research given that every question was handled separately.
The filtering was then done for the remaining questions to identify the percentages of the response. The researcher found it prudent to start with questions that required the ‘yes’ or ‘no’ responses. Literature shows that it could be very easy to analyze and compute the percentages for each gender as well as the combined genders (Minitab, 2013). Frequency distribution was employed to determine the proportion of respondents who chose a variety of responses. Fortunately, only two respondents hardly responded to particular questions. At least 14 respondents answered every question making it easy to compute the results and this was realized through using the filtering tool. It was possible to establish the questions that were not responded to by the research respondents. Via the same tool, it was possible to establish which respondent failed to respond to which question when the results were tabulated in the base worksheet.
On the other hand, to establish the relationships between the independent and dependent variable data, the researcher needed to come up with a design that would warrant the accuracy. By using the collected data obtained after filtering and calculation of percentages, the researcher generated tables. The tables are important to the reader as they summarize the content of the respective parts. Data was copied and pasted to maintain integrity as opposed to re-typing where mistakes are likely to emerge. The final data in the base worksheet formed the basis for comparing the relationships between different factors. Having attained the percentages of each response in every question, the percentages were deemed critical for comparison purposes. The data obtained at this stage was still large enough to be used for manual evaluation. Further, it was cumbersome to achieve descriptive statistics using Excel since one could hardly obtain variability and central tendency measures. This necessitated the utilization of the software that would enable the reader to have an in-depth understanding of the data collected. The SPSS offered the researcher the opportunity to analyze such data.
The data in the base worksheet was transferred into the SPSS Data Editor consisting of two viewers. The Variable viewer facilitated the definition of different fields (variables) such as names, gender, and occupation. In essence, the fields indicated the 54 questions in the questionnaire. The Data viewer helped the researcher to enter and view data for every variable. These were the percentages and points generated using Excel. For every respondent, all the data was contained in a single row to represent a ‘case’. The ‘case’ is a representation of all the information in a single questionnaire. By having the data in Excel, it was easier to copy and paste the data into the SPSS since every row of the Excel data represented a single questionnaire (participant’s response). To simplify the process, the entire base worksheet was copied into the SPSS after verifying that the data was imported as desired using two-sample ‘cases’ from the base worksheet. Once all the data was imported into the SPSS, it was ready for analysis.
SPSS offers the user an option in analyzing data using the Analyze alternative (SPSS, N.d). The option is located above the Data Editor on the menu bar. Depending on the output a researcher desires, the drop-down menu in the ‘Analyze option’ offered different options for analyzing data. In this study, the researcher chose to use the option for Descriptive Statistics. The move was necessary considering that some questions were qualitative as opposed to being quantitative. This activity generated the tables presented in the study (Figures 2, 3, 4, 5, 6, and 7). The researcher also desired to have regression analysis. It was hence necessary to use the Graphs option to obtain a linear representation of the data collected so that the researcher would compare the different sets of data. The measure allowed the researcher to establish whether the output was accurate by comparing the Excel analysis with the SPSS output. This was achieved by running a full check of SPSS Data View versus case numbers displays on all the 19 files and the outputs. The results indicated that there were no loopholes in the data hence the output was accurate.
A short session on the ethical consideration
The research was essential to the completion of this study. Thus, a formal request authorization was sought from the organization’s management and the responsible authority where the survey was conducted. The researcher took all the responsibilities encountered during the research process to ensure the integrity, dignity, and well-being of the involved study subjects. To make certain the completion of the research tasks within the required period, the researcher recognized and effectively balanced any subjectivity. This was realized through providing precise research accounts and abiding by the law to develop indispensable expertise. Considerations and observance were thus given to a variety of ethical issues and guidelines including the participants’ informed consents, power, and confidentiality.
The respondents were granted a holistic indulgence into the study. To accomplish this, the researcher requested every research participant’s permission to ensure abidance by his or her respective informed consent. A request for the participants understanding to be involved in the research was also made clear. For instance, the time factor, the significance of the topic researched, activities to be undertaken, and any risk to be encountered were revealed to the research respondents. The participants were free to make independent decisions on whether to take part or not to be involved in the research. This implied that the participants were informed about their indefinite voluntary participation in the study before taking part. Further, the researcher ensured that the participants were aware of the research details, nature, and their respective rights to pull out from the study at any moment.
The researcher observed honesty and the participants were not compelled to take part in this research. However, to ensure that the participants were not banished while participating in the research, the organization’s management permission was sought and the unwilling participants were exempted from the study. Besides, the participants’ information was not to be disclosed to guarantee confidentiality and preserve their anonymity. The information acquired from the study participants were to be securely stored and protected whereas the study finding hardly divulged the respondents’ identification.
References
Minitab, (2013).Analyzing Survey Data with Minitab: Frequency Distributions, Cross Tabulation and Hypothesis Testing. Web.
SPSS, (N.d).Chapter Four: Univariate Statistics. Web.