Business Research Methodology Report: Descriptive vs Inferential

This report provides a comprehensive overview of business research methodology, specifically focusing on descriptive and inferential data analysis techniques. The report begins by defining descriptive analysis, highlighting its use in summarizing data characteristics through measures of central tendency, dispersion, and graphical representations like histograms and charts. It discusses the strengths of descriptive analysis, such as tabulation and graphical visualization, while also acknowledging its limitations in terms of data objectivity and scope. The report then transitions to inferential data analysis, explaining its role in generalizing population data, conducting hypothesis testing, and making predictions. It details methods like ANOVA, regression, and t-tests, emphasizing the importance of dependent and independent variables. The strengths of inferential analysis, including its capacity for market research and probability-based conclusions, are contrasted with its limitations in experimental and quasi-experimental research. The report concludes by providing references to support the analysis.

Running head: BUSINESS RESEARCH METHODOLOGY
Business Research Methodology
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BUSINESS RESEARCH METHODOLOGY
Descriptive analysis are applied to show the characteristics or features of data. This
analysis provides the summary measure of the data. It is a study which provides information
with help of quantitative information. Moreover this analysis conduct some graphical
representation. In such analysis provides measure of central tendency, measure of dispersion,
five numbers summary and cross tabulation. The descriptive analysis is conduct different
hypothesis test among the observations and their differences. In this analysis a researcher can
apply both the quantitative and qualitative data (Nassaji 2015).
In the measure of central tendency, mean, media and mode are defined. With the help
of this measure the normality or symmetricity of the data also tested. The measure of
dispersion provides about the homogeneity or variability of the data. With the help of
frequency distribution histogram, pie chart and bar chart can be drawn.
The strength of the descriptive data analysis are tabulation, graphical representation
and statistical parameter. In the tabulation data can be simply listed according to their
measurement. In general the arrangement of matrix or cross tabulation is major strength of
this analysis. The graphical representation is the easy visualized method of data analysis. The
graphical representation helps to recognize about the pattern of the data. The statistical
parameter means measure of central tendency and dispersion are the main parameter of
descriptive data analysis (Bland et al. 2015).
The limitations of descriptive data analysis are means and tools study and data
limitation, data should be objective and positive nature, The common and useful approach of
descriptive data analysis are case studies, correlation method and survey method. The
descriptive data analysis is limited to actual data measurement. The major limiting factor of
this analysis is that it does not depends the entire population or sampling.

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BUSINESS RESEARCH METHODOLOGY
Inferential data analysis is a statistical data analysis technique which provides some
generalization about the population data. The inferential data analysis has been applied when
the population size is large (Balakrishnan and Kundu 2013). This kind of analysis can be
applied in prediction and population mean differences. There are method which is applied in
the inferential data analysis. These are analysis of variance (ANOVA), regression analysis,
analysis of covariance (ANCOVA), t-test and so on. In inferential data analysis data can be
divided by two parts that is dependent and independent. The inferential analysis also applied
in the difference between two groups. Moreover it is applied in the trend and time series
forecasting.
In inferential data analysis hypothesis testing the conclusion or result is given based
on their test statistic, critical value and significance level. If the critical value is higher than
the alpha level of significance than the hypothesis is significant otherwise not.
The strength of the inferential data analysis is it is analyze the whole population with
respect of population parameter. In this analysis the hypothesis test can be conducted. The
market research is the major strength of inferential data analysis. Moreover this analysis
provides the language of probability (Hayes and Scharkow 2013).
The limitation of inferential data analysis is it convert the data in to general condition
format. Moreover it is limited in the experimental and quasi experimental research.

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BUSINESS RESEARCH METHODOLOGY
References
Balakrishnan, N. and Kundu, D., 2013. Hybrid censoring: Models, inferential results and
applications. Computational Statistics & Data Analysis, 57(1), pp.166-209.
Bland, M. D., Whitson, M., Harris, H., Edmiaston, J., Connor, L. T., Fucetola, R., ... & Lang,
C. E. (2015). Descriptive Data Analysis Examining How Standardized Assessments Are
Used to Guide Post–Acute Discharge Recommendations for Rehabilitation Services After
Stroke. Physical therapy, 95(5), 710-719.
Hayes, A.F. and Scharkow, M., 2013. The relative trustworthiness of inferential tests of the
indirect effect in statistical mediation analysis: Does method really matter?. Psychological
science, 24(10), pp.1918-1927.
Nassaji, H., 2015. Qualitative and descriptive research: Data type versus data analysis.