Cross-Section and Time-Series Data Application in Research

Categories: Research

Research MethodologyPaper 1Elisa and Guido applied the cross-section and time series data which have been examined through a panel data multiple regression to investigate their hypotheses. A descriptive and multivariate analysis have been shown to proof the level of profitability of the selected banks. Three different models were tested in the analysis to show the different measures of profitability and they are based on dependent variables as explained in brief summary and synopsis on page 2 of this critique. There are several ratios used to measure the profitability of banks (Sufian and Habibullah, 2009; Ben Naceur and Omran, 2011).

Nevertheless, the authors considered three ratios which is Return on Equity (ROE), Return on Assets (ROA) and Net Interest Margin (NIM) which act as the dependent variable and some internal factors are also considered which act as independent variables. The explanation for the dependent and independent variables mentioned are Most of the previous studies on bank profitability, such as Short (1979), Bourke (1989), Molyneux and Thorton (1992), Demirguc-Kunt and Huizinga (1999), Athanasoglou et al.

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(2006), Garcia-Herrero et al. (2009) and Goddard et al. (2004a), used linear models to assess the impact of different factors that maybe significant in explaining profitability. Therefore, the authors have used the same model and have proven to be the best method used for this purpose.In short, the researchers have used the Pooled Least Square regression model (OLS) method for the multivariate analysis and the panel regression technique by using the internal determinants. The researchers proved in their research that European banks present similar reply to cyclical movements and this was the reason why the OLS method has been applied (Elisa and Guido, 2015).

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It is the most consistent regression estimation because of its general quality of minimised bias and variance (Koutsoyiannis, 2003 and Greene, 2004).Paper 2 Mitchell and Joseph have collected their data based on a combination of qualitative and quantitative research so that they are able to complete their research affluently in accordance with the definition of their research. Steward and Shamdasani (1990) support the use of qualitative research when investigating topics that are considered to be sensitive and socially undesirable such as aberrant consumer behaviour. Twenty-two in-depth interviews were conducted by the researchers which included interviews conducted on those who had criminal records. The interviews were conducted to identify consumer aberrant behaviours in UK which had helped in designing a questionnaire. The interviews helped to validate some items taken from other scales, (e.g. Muncy and Vitell 1989, 1992; Vitell et al. 1991) as well as to create a new set of items. The Consumer Ethics Index on a five-point Likert scale was created based on the results of the interviews. Once the questionnaire was designed, it was given out to 240 respondents based on a population of an age and gender quota of the total population in UK. The researchers have kept the information provided by the respondents highly confidential as it was important to protect the anonymity of the respondents and to remove psychological barriers among respondents.Contrast Both journals have used different techniques in their research methodology. Journal 1 used data from Bankscope database to measure profitability. The profitability is based on hypothesis which has been explained in the literature on pages 3 to 4. Ratio analysis was also calculated as indicators for bank profitability and the linear regression model showed the relationship between bank profitability and bank-specific determinants. The author has used these determinants well in explaining the bank profitability with the use of statistics, correlation matrix and regression analysis which will be described in the findings.Journal 2 used a combination of quantitative and qualitative research methods such as in-depth interviews to construct questionnaires to validate their claims on unethical attitudes and behaviours of UK consumers. The data collected was highly sensitive and thus the respondents were assured of their confidentiality and anonymity of the responses. The authors have carefully selected their target audience for the questionnaires as it represented an age and gender quota sample of the entire UK population. However more scope can be given to other quotas and a wider participant pool so that better results are derived for further research.Journal 1's data is reliable as it is taken from financial information of the banks concerned given by Bankscope and it represents true figures. The data is from consolidated accounts which have been audited before being published in the company's financial statements. As such, the data is valid and the research provides accurate information on the findings which will be explained on the next page. As for Journal 2, the data being researched is taken from questionnaires in which there could be some bias in the information given or unreliable data given by the chosen respondents. The sample data taken is also too small as it was meant to represent the entire population of United Kingdom. The findings of the research are explained on the next page.Discussion of ResultsJournal 1 The descriptive analysis of the results for all the variables is shown on Table II. The researchers have evidenced that there exists a difference between mean and standard deviation which shows the great differences among the profitability of banks (Elisa and Guido; 2016) as shown on Table II. The researchers have further evidenced that there is a large variation in the data set of Size and Loan because the sample includes banks with very different sizes and loans (Elisa and Guido; 2016). These results are consistent with prior evidence (Pasiouras and Kosmidou, 2007; Staikouras et al., 2008; Molyneux and Thorton, 1992; Bikker and Hu, 2002; Goddard et al., 2004a; Gul et al.,2011). The correlation coefficient for the variables measured by the authors is presented in Table III. The results confirm that no collinearity problem occurs between the independent variables, as multicollinearity can be considered a problem when the correlation is above 0.80 (Kennedy, 2008). The authors have claimed that the correlation between each of the variables is not elevated and the highest degree of correlation found is very satisfactory (Elisa and Guido, 2016). The empirical analysis of the authors show that all independent variables have a statistically significant relationship with probability measures included in the model as displayed in Table IV. The authors have further claimed that the models discussed have performed reasonably well, with most variables remaining stable across the various regressions tested (Elisa and Guido, 2016). The positive relationship between size and profitability is related with economies of scale (Hauner, 2005; Pasiouras and Kosmidou, 2007; Staikouras et al., 2008), as such banks with larger assets have more control of the market. Table IV Regression AnalysisJournal 2The data collected is interpreted based on ethical decisions of consumers as shown in Table 1 on the next page which also shows the descriptive analysis of the results for all the variables. The table illustrates the active or passive behaviours, perceived illegality of the behaviours, and the perceived consequences. In short it explains about the unethical behaviours possibly committable and the perceived consequences to it. The authors have suggested that perceived illegality and severity of consequence both play an important role in deciding with aberrant behaviours among consumers. According to the authors, the aberrant behaviours of consumers are dependent upon which party would suffer due to their behaviours. Apart from that, consumers make their decisions based on fact of who is in fault and is more likely to accept passive unethical behaviour. Previous studies (e.g. Vitell and Muncy 1992; Rawwas et al. 1996 and Al-Khatib 1997) suggested that the perceived rightness or wrongness of a situation can influence a consumer's decision act, yet none has empirically tested this hypothesis and looked at how many times unethical actions are committed. Therefore, according to the analysis in Table 1, the authors believe that unethical behaviours of consumers depend on the beliefs of these consumers over their behaviours and the likelihood of benefits gained from these behaviours. ContrastJournal 1ConclusionJournal 1hhhJournal 2The authors have managed to create a consumer aberrant behavioural index which can be used as a benchmark for further investigations. Some of the aberrant behaviours mentioned in Table 1 might incur a loss to certain industries. For example, industries such as software and recording might suffer millions of dollars if the aberrant behaviours are concerned with their industries such as recording a new movie and releasing the piracy videos or downloading a computer software without purchasing it when required. The authors further mentioned that retailers should promote their activities and offer fair deals to consumers so that consumers will act ethically in return. The development of the consumer ethics model is still at the beginning stage and it will be good to explore the impacts on environmental changes and cultural values and to test consumer ethics models under different cultural settings as mentioned by the authors. There is also no method to measure the frequency on the occurrences of the aberrant behaviours mentioned in Table 1 which leaves space for further research.RecommendationsJournal 1Journal 2The study of unethical behaviours among consumers is focused on a small sample data which represented the entire UK population. The sample data is insufficient to come up with proper solutions to different aberrant behaviours. The sample data might be bias in certain circumstances and as such there should be some kind of detectors in position to detect or some kind of validity measures available to check the accuracy of each consumer to the aberrant behaviours while filling up the questionnaires.ContrastThere should be more scope given to further this research taking sample data from consumers outside of UK so that the overall consumer aberrant behaviours can be analysed and proper solutions to manage each behaviour can be determined. This would enable Multi-National corporations to be ready when venturing into international markets and place their promotional messages well to their target consumers.23 Paragraph 20 " Overall ConclusionIn conclusion, Behrends has attempted to explore recruitment activities in a sub-set of German SMEs. The importance of employees, and recruitment in particular, to these firms along with the general absence of knowledge-intensive firms from discussion of HRM in SMEs (Scase, 1995) suggest that this was a worthy endeavour. Unfortunately fundamental difficulties with Behrends' conceptualisation and execution of his research mean that the reported results must be treated with extreme caution.The reviewer concludes with a summary of the article's research focus and his endorsement of those aims. He finishes with a reminder of the weaknesses that he believes to be evident in the research approach adopted by Behrends.24 Overall Comments..So this reviewer has adopted quite a critical, i.e. negative, stance. Critical reviews do not necessarily have to take such a strong counter-position. Your own aim in a critical review should be to evaluate both strengths and limitations, with as many externally sourced references as possible, to present a justified analysis of the article's value in the relevant field of research.

Updated: May 03, 2023
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Cross-Section and Time-Series Data Application in Research. (2019, Aug 20). Retrieved from

Cross-Section and Time-Series Data Application in Research essay
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