An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this Thesis or Dissertation Abstract The use of spatiotemporal analytical tools to generate risk maps and risk scores that facilitate early detection of health and environmental threats is increasingly popular in many countries and international organizations around the blogger.com: Kaushi Kanankege interpretation of qualitative data collected for this thesis. Analysis of qualitative data Qualitative data analysis can be described as the process of making sense from research participants‟ views and opinions of situations, corresponding patterns, themes, categories and regular similarities (Cohen et al., ). Nieuwenhuis (
Step 7: Data analysis techniques for your dissertation | Lærd Dissertation
Try out PMC Labs and tell us what you think. Learn Analytical tools dissertation. Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, analytical tools dissertation, Jammu and Kashmir, India.
Statistical methods involved in carrying out a study include planning, designing, analytical tools dissertation, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings, analytical tools dissertation. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data.
The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is analytical tools dissertation. Finally, there is a summary of parametric and non-parametric tests used for data analysis.
Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population, analytical tools dissertation. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.
Variable is a characteristic analytical tools dissertation varies from one individual member of population to another individual. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ]. Quantitative or numerical data are subdivided into discrete and continuous measurements.
Discrete numerical data are recorded as a whole number such as 0, 1, analytical tools dissertation, 2, 3,… integeranalytical tools dissertation, whereas continuous data can assume any value.
Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit.
Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature. A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, analytical tools dissertation, interval and ratio scales [ Figure 1 ].
Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist as in gender male and femaleit is called as a dichotomous or binary data, analytical tools dissertation.
The various causes of analytical tools dissertation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables. Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.
Interval analytical tools dissertation are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.
Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property.
For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length, analytical tools dissertation. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.
Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample analytical tools dissertation data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if analytical tools dissertation and inferential statistics are illustrated in Table 1.
The extent to which the observations cluster around a central location is described by the central tendency and the analytical tools dissertation towards the extremes is described by the degree of dispersion. The measures of central tendency are mean, median and mode. Mean may be influenced profoundly analytical tools dissertation the extreme variables.
For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months analytical tools dissertation of septicaemia. The extreme values analytical tools dissertation called outliers.
The formula for the mean is. Median[ 6 ] is defined as the middle of a distribution in a ranked data with half of the variables in the sample above and half below the median value while mode is the most frequently occurring variable in a distribution.
Range defines the spread, or variability, of a sample. If we rank the data and after ranking, group the observations into percentiles, we can get better analytical tools dissertation of the pattern of spread of the variables, analytical tools dissertation.
In percentiles, we rank the observations into equal parts. The median is the 50 th percentile. Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:. where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population.
The variance of a sample is defined by slightly different formula:. where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. Each observation is free to vary, except the analytical tools dissertation one which must be a defined value.
The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, analytical tools dissertation, the square root of variance is used. The square root of the variance is the standard deviation SD. where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:.
where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample, analytical tools dissertation. An example for calculation of variation and SD is illustrated in Table 2. Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point. It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1.
In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail. In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis plural hypotheses is a proposed explanation for a phenomenon.
Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects. Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 where 0 indicates impossibility and 1 indicates certainty. Alternative hypothesis H 1 and H a denotes that a statement between the variables is expected to be true. The P value or the calculated probability is the probability of the event occurring by chance if the null hypothesis is true.
The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ]. If P value is less than the arbitrarily chosen value known as α or the significance levelanalytical tools dissertation, the null hypothesis H0 is rejected [ Table 4 ]. However, if null hypotheses H0 is incorrectly rejected, this is known as a Type I error.
Numerical data quantitative variables that are normally distributed are analysed with parametric tests, analytical tools dissertation. The assumption of normality analytical tools dissertation specifies that the means of the sample group are normally distributed.
The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal. However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used.
Non-parametric tests are used to analyse ordinal and categorical data. The parametric tests assume that the data are on a quantitative numerical scale, with a normal distribution of the underlying population. The samples have the same variance homogeneity of variances.
The samples are randomly drawn from the population, analytical tools dissertation the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance ANOVA and repeated measures ANOVA. Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups.
It is used in three circumstances:. To test if a sample mean as an estimate of a population mean differs significantly from a given population mean this is a one-sample t -test. The formula for one sample t -test is. To test if the population means estimated by two independent samples differ significantly the unpaired t -test.
The formula for unpaired t -test is:. To test if the population means estimated by two dependent samples differ significantly the paired t -test.
A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment. where d is the mean difference and SE denotes the standard error of this difference.
Qualitative Data Analysis 101 Tutorial: 6 Analysis Methods + Examples
, time: 25:25(PDF) Analytical Tools in Research Sample pages .pdf | L N Pattanaik - blogger.com
The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies SC tools are based on Fuzzy Logic (FL), Artificial Neural Network (ANN) and Probabilistic Reasoning (PR) techniques such as Genetic Algorithm (GA). These tools aim to exploit the tolerance for imprecision, partial truth and uncertainty similar to human decisions for real life research blogger.comted Reading Time: 14 mins interpretation of qualitative data collected for this thesis. Analysis of qualitative data Qualitative data analysis can be described as the process of making sense from research participants‟ views and opinions of situations, corresponding patterns, themes, categories and regular similarities (Cohen et al., ). Nieuwenhuis (
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