Quantitative analysis in social sciences for PhD Research Scholar – PhD Assistance

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IMPORTANCE OF QUANTITATIVE RESEARCH IN SOCIAL SCIENCES FOR PHD RESEARCH SCHOLAR An A c a d e m i c p re se nt at i on by Dr. Nancy Agnes, Head, Technical Operations, Phdassistance Group w w w. p h d a s s i s t a n c e . c o m Email: i nf o@p h da ss i st an ce .c om


TODAY'S OUTLINE In-Brief Introduction Pearson’s product-moment correlation coefficient Concluding remarks


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In order to identify the future research topics, we have reviewed the m a n a g e m e n t literature (recent peer-reviewed studies).


INTRODUCTION Q uantitative analysis is used in social sciences to determine the relationship between an independent variable ( a type of quantity that captures observed values of things that ca n be m a n i p u l a t e d ) a n d a dependent variable ( t h e observed results of manipulating the independent variable) in a given population.


For example, you m i g h t be interested in identifying the association between years of schooling ( t h e independent variable) a n d earnings pa id to g a r m e n t workers ( t h e dependent variable). To determine that association, you'd need to collect d a t a on workers' years of schooling a n d their pay. You m i g h t also be interested in finding the link between a balanced diet ( t h e independent variable) a n d schoolchildren's cognitive abilities.



In this situation, you'd need to gather d a t a on food intake, such as the n um b e r of meals consumed each day, as well as administer cognitive achievement exams in reading, math, a n d written language. PhD Disserta tion on Soc ia l Sc iences Resea rch Method olog y m u st conta in clear a c a d e m i c evidence a n d justification for all the research choices.


PEARSON’S PRODUCT-MOMENT CORRELATION COEFFICIENT The Pearson's p r o d u c t - m o m e n t correlation coefficient, often known as Pearson's rho, or simply Pearson's r, is a standard m e t h o d for determining the direction a n d degree of a correlation between two variables.


It is the product of the linear relationship between a dependent a n d independent variable. 3 The Pearson's correlation coefficient is defined as the product of two variables' standard deviations divided by their covariance. The standard deviation is a measure of how m u c h variation exists between the observed values of the two variables a n d the mean, whereas covariance is a measure of how m u c h the two variables vary together.


You'll need to employ hypothesis testing a n d confidence levels procedures to determine the relevance of such correlations. You w ill find the bestdissertationforfutureresearchers business a n d mana ge me nt.

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These topics are researched i n - d e p t h a t the University of Glasgow, UK, Sun Yat-sen University, University of St Andrews a n d m a n y more.


When utilising Pearson's r, the independent a n d dependent variables m u s t be measured continuously in interval or ratio scales. Even though they have no meaningful zero value, interval variables allow us to detect equally spaced values in intervals. Temperature measured on the Fahrenheit scale, where a zero degree does not m e a n there is no temperature, but rather a very cold temperature of around -17.78 degrees Celsius, is a popular example of a n interval variable.


In the social sciences, however, we rarely use interval variables a n d instead rely on ratio scales. P hD Qualitative Research Methodology S ervices & Help you to select a perfect research methodology work. The qualities of ratio scale variables are similar to those of interval variables, but they differ in that they have a definable zero point. Ratio scale variables include income, expenditures, revenues, costs, profits, a n d other continuous variables.


Ratio scales offer the a d v an t ag e of being able to be measured, compared, a n d ranked in terms of m a g n i t u d e differences, such as when two individuals, A a n d B, report monthly incomes of US $2000 a n d US $1000, respectively, in a dataset. The correlation coefficient's direction c an range from -1 to +1, passing through zero. Increases in the independent variable, x, are related with a n increase in the dependent variable, y, whereas increases in the independent variable are connected with a reduction in the dependent variable, y.


If your d a t a shows that workers with more years of education earn more money, you c a n conclude that there is a positive relationship between education a n d pay. The plotted points migrate from the lower l e ft -h a nd corner to the upper r i g h t - h a n d corner of the scattergram in this hypothetical scenario, as shown in Figure 1. Similarly, if your d a t a shows that higher alcohol use a m o n g workers is linked to premature death, you c an conclude that alcohol intake a n d life expectancy are negatively associated.


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You should be aware that the level of correlation between two variables, in either a positive or negative direction, m a y not be as obvious as you m i g h t think in m a n y circumstances. Figure 2 depicts the relationship between the n u mb e r of children in a household a n d the household head's age.


The Pearson's correlation coefficient ( r = -0.17) demonstrates a weak a n d negative relationship between the two variables, indicating that the relationship is not a straight line. Phd assistance has vast experience in developing dissertation topics for students pursuing the UK dissertation in both engineering a n d man age me nt. Order Now


The existence of exceptionally low a n d high values in the scattergram, which are referred to as outliers by academics. The inclusion of a few outliers in a dataset m i g h t lead to misleading results, especially when the sample size of your d a t a is small. Phdassistance experts has experience in handling Q ualitative & Q uantitative Research in Business a n d m a n a g e m e n t research with assured 2:1 distinction. Talk to Experts Now


CONCLUDING REMARKS I've b riefly a d d ressed q ua ntita tive resea rch a p p roa ches c o m m o n l y utilized in development research in this work.

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Some of these procedures are complicated a n d necessitate a high level of statistical, econometric, a n d study design knowledge. If you wa nt to conduct a study but don't have a background in Economics, Statistics, or Behavioural Sciences, you should seek advice from a quantitative research methods expert, keeping in m i n d the difficulties I've raised throughout this work.


Keep in m i n d that your methodological approach will be guided by your study aims. Data gathering will decide whether or not randomization is viable in terms of context a n d / o r ethical, budgetary, a n d time constraints. It is significant to select a social science research methodology, which exactly suits your research work, a n d you m u s t clearly state the reason why you have chosen a particular research method. If you found difficult in identifying research methodology, you c a n consult with Best PhD Research Methodology Help provider online to identify Best D issertation Research Methodology.


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