Statistical treatment of data for quantitative research
Pmc5037948basic statistical tools in research and data analysiszulfiqar ali and s bala bhaskar1department of anaesthesiology, division of neuroanaesthesiology, sheri kashmir institute of medical sciences, soura, srinagar, jammu and kashmir, india1department of anaesthesiology and critical care, vijayanagar institute of medical sciences, bellary, karnataka, indiaaddress for correspondence: dr. 2016 october; 60(10): article has been cited by other articles in ctstatistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. 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 given. Finally, there is a summary of parametric and non-parametric tests used for data words: basic statistical tools, degree of dispersion, measures of central tendency, parametric tests and non-parametric tests, variables, varianceintroductionstatistics 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. 1] this requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. 3] variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value.
Statistical treatment of data for descriptive research
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. Hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [figure 1]. 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 female), it is called as a dichotomous (or binary) data. 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 scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[4] use a random sample of data taken from a population to describe and make inferences about the whole population. 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.
Statistical treatment of data for qualitative research
If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. 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 3curves showing negatively skewed and positively skewed distributioninferential statisticsin inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. 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]. 12]table 4illustration for null hypothesisparametric and non-parametric tests numerical data (quantitative variables) that are normally distributed are analysed with parametric tests. 13]two most basic prerequisites for parametric statistical analysis are:The assumption of normality which specifies that the means of the sample group are normally distributedthe assumption of equal variance which specifies that the variances of the samples and of their corresponding population are r, 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 tric tests the parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. It is based on random differences present in our r, the between-group (or effect variance) is the result of our treatment. Using a standard anova in this case is not appropriate because it fails to model the correlation between the repeated measures: the data violate the anova assumption of independence. These tests examine whether one instance of sample data is greater or smaller than the median reference testthis test examines the hypothesis about the median θ0 of a population.
If the observed value is equal to the reference value (θ0), it is eliminated from the the null hypothesis is true, there will be an equal number of + signs and − sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the on's signed rank testthere is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the on's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank -whitney testit is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the –whitney test compares all data (xi) belonging to the x group and all data (yi) belonging to the y group and calculates the probability of xi being greater than yi: p (xi > yi). The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test eere testin contrast to kruskal–wallis test, in jonckheere test, there is an a priori ordering that gives it a more statistical power than the kruskal–wallis test. 13]tests to analyse the categorical data chi-square test, fischer's exact test and mcnemar's test are used to analyse the categorical or nominal variables. The chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i. It is calculated by the sum of the squared difference between observed (o) and the expected (e) data (or the deviation, d) divided by the expected data by the following formula:a yates correction factor is used when the sample size is small. If the outcome variable is dichotomous, then logistic regression is res available for statistics, sample size calculation and power analysisnumerous statistical software systems are available currently. The commonly used software systems are statistical package for the social sciences (spss – manufactured by ibm corporation), statistical analysis system ((sas – developed by sas institute north carolina, united states of america), r (designed by ross ihaka and robert gentleman from r core team), minitab (developed by minitab inc), stata (developed by statacorp) and the ms excel (developed by microsoft).
A few are:Summaryit is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based ial support and cts of interestthere are no conflicts of nces1. Pmc free article] [pubmed]articles from indian journal of anaesthesia are provided here courtesy of wolters kluwer -- medknow s:article | pubreader | epub (beta) | printer friendly | paperwrite to conduct ments with tical treatment of tical treatment of rth kalla 123. This page on your website:Statistical treatment of data is essential in order to make use of the data in the right form. Raw data collection is only one aspect of any experiment; the organization of data is equally important so that appropriate conclusions can be drawn. This is what statistical treatment of data is all article is a part of the guide:Select from one of the other courses available:Experimental ty and ical tion and psychology e projects for ophy of sance & tics beginners tical bution in er 17 more articles on this 't miss these related articles:2branches of statistics. Statistical treatment of data is essential in all experiments, whether social, scientific or any other form.
Statistical treatment of data greatly depends on the kind of experiment and the desired result from the example, in a survey regarding the election of a mayor, parameters like age, gender, occupation, etc. Therefore the data needs to be treated in these reference important aspect of statistical treatment of data is the handling of errors. Of data and distribution trying to classify data into commonly known patterns is a tremendous help and is intricately related to statistical treatment of data. This is because distributions such as the normal probability distribution occur very commonly in nature that they are the underlying distributions in most medical, social and physical ore if a given sample size is known to be normally distributed, then the statistical treatment of data is made easy for the researcher as he would already have a lot of back up theory in this aspect. Care should always be taken, however, not to assume all data to be normally distributed, and should always be confirmed with appropriate tical treatment of data also involves describing the data. Therefore two distributions with the same mean can have wildly different standard deviation, which shows how well the data points are concentrated around the tical treatment of data is an important aspect of all experimentation today and a thorough understanding is necessary to conduct the right experiments with the right inferences from the data obtained.. Take it with you wherever you research council of ibe to our rss blakstad on tical analysis and research data processing - organizing information in ptive statistics - simple quantitative summary of a tics tutorial - help on statistics and es of central tendency - what is the middle value? Upprivacy us commence our look at data analysis g at a hypothetical research er that there are of approaching a research question and how we put together our research question will determine the methodology, data collection method, statistics, analysis and we will use to approach our research e of research females more likely to be nurses the proportion of males who are same as the proportion of females? There a relationship between gender the example in the box above, you can see are three different ways of approaching the research problem, which ned with the relationship between males and females in r research problem with another research problem - the n gender and smoking, there are 2 les (gender & smoker), with two or more categories in each, for example:You are looking for whether or not there is icance in the we proceed, you may want y refresh your knowledge and understanding of some basics, namely:Controlled double-blind tics, read chapter 9 of the accompanying book, and/or click on level (p level). Are many tests that we can use to data, and which particular one we use to analyse our data depends upon are looking for, and what data we collected (and how we collected it).
Are just a few of the more common ones may come across in research test is used to test for n 2 independent groups on a continuous measure, e. Actually compares converts the scores on the the two then evaluates whether the medians two groups differ an rank test is used to demonstrate onship between two ranked ntly used to compare judgements by of judges on two objects, or the scores of a group of subjects is a shows the association between les (x and y), which are not normally about the details just remember is an acceptable method for parametric data when there are less than more than 9 paired test is used to compare the more than two samples, when either the data l or the distribution is not there are only two groups then it is lent of the mann-whitney u-test, so you may as well use test would normally be used when to determine the significance of difference among three or more is a very brief look at - for more information on statistical tests, read chapter 9 of common statistical. Is one of a number of tests (ancova - analysis ance - and manova - multivariate analysis of variance) that to describe/compare the relationship among a number of are two different types of chi-square tests - involve categorical data (pallant 2001). Type of chi-square test compares the frequency what is expected in theory against what is actually second type of chi-square test is known as -square test with two variables or the chi-square test is the most common nonparametric test for -sampled repeated measures design of research study, and is as the wilcoxon matched-pairs a very brief look at some of the more common statistical tests for is of data obtained from quantitative research - more details are given r 9 of the accompanying book. There are, of course, many others, and statistics book will have details of the selection of the appropriate test for your research in order to p-value, you need to base the selection of four major factors,Level of data (nominal, ordinal, ratio, or interval). All these statistical tests may look cated, but if ever you are involved in quantitative research and have statistical analysis, don't worry because help is at is a computer package for is known as one of a number of computer packages that can do just calculation that you want, using any statistical we finish this section, we just need to remind be careful when you are looking at research that uses tions of /data/statistical look for these . Researchers should reflect on their study and discuss anything that did it perfect, for example:It is easy to tie yourself up into knots doing statistics as part of your research, or when reading , so remember two things:1. Statistics by themselves are meaningless, sion of statistics which makes them meaningful time has come for you which statistical test you will be using for your own ch. As we keep mentioning, if all this is new to you, do not hesitate the advice of an experienced quantitative researcher and/or a statistician. At as early a stage as on to the icon the example of a quantitative research study proposal:When you are satisfied have the correct statistical test(s), and you can justify it/them, ctive learning evaluation ntly asked is data analysis?
Of variables - of variables - is of quantitative is of quantitative data - standard of statistical ng a statistical tical test tics in research of statistical of statistical that you have looked at the distribution of your data and perhaps conducted some descriptive statistics to find out the mean, median, or mode, it is time to make some inferences about the data. As previously covered in the module, inferential statistics are the set of statistical tests we use to make inferences about data. These statistical tests allow us to make inferences because they can tell us if the pattern we are observing is real or just due to do you know what kind of test to use? The decision of which statistical test to use depends on the research design, the distribution of the data, and the type of variable. Below is a table listing just a few common statistical tests and their tests look for an association between for the strength of the association between two continuous for the strength of the association between two ordinal variables (does not rely on the assumption of normally distributed data). For the strength of the association between two categorical ison of means: look for the difference between the means of for the difference between two related for the difference between two independent the difference between group means after any other variance in the outcome variable is accounted sion: assess if change in one variable predicts change in another how change in the predictor variable predicts the level of change in the outcome how change in the combination of two or more predictor variables predict the level of change in the outcome -parametric: used when the data does not meet assumptions required for parametric on rank-sum for the difference between two independent variables—takes into account magnitude and direction of on sign-rank for the difference between two related variables—takes into account the magnitude and direction of if two related variables are different—ignores the magnitude of change, only takes into account this link for a printable pdf version of this table: common statistical ng a statistical of experiments > research tative research vs. Tative research is statistical: it has numbers attached to it, like averages, percentages or quotas. The answers are free-form and don’t have numbers associated with them, so that makes them ts (click to skip to that section):What is qualitative research? Of qualitative research of qualitative ages and disadvantages of qualitative research -qualitative research methods tative research research is qualitative research? Research(qr) is way to gain a deeper understanding of an event, organization or culture.
Depending on what type of phenomenon you are studying, qr can give you a broad understanding of events, data about human groups, and broad patterns behind events and people. While traditional lab-based research looks for a specific “something” in the testing environment, qualitative research allows the meaning, themes, or data to emerge from the ative research uses non-statistical methods to gain understanding about a population. In other words, you’re not dealing with the numbers you’d find in quantitative research. For example, let’s say your research project was to answer the question “why do people buy fast food? Another major difference between qualitative and quantitative research is that qr is usually performed in a natural setting (as opposed to a lab). Of qualitative of the different qualitative research methods have several characteristics (merriam):Findings are judged by whether they make sense and are consistent with the collected s are validated externally by how well they might be applicable to other situations. This is tough to do; rich, detailed descriptions can help to bolster external is usually collected from small, specific and non-random gh qualitative research doesn’t have the same structure as a formal lab-testing environment, there are certain requirements you must meet in order for your qualitative study to be called “research. If you don’t take careful notes, you could miss something of vital rules you must follow include selecting the people or events you want to observe, having a plan on how you’re going to get into the “world” you want to observe, and deciding ahead of time what types of data you’re going to of qualitative research pological researchers study people in their natural environment, sometimes immersing themselves in foreign cultures for years. This type of research is invaluable when it would be inappropriate or impossible to put people in a laboratory setting or even conduct a simple interview. Observing people in their natural setting helps to eliminate these this research method, you use your own experiences to address a cultural, political, or social issue.
For example, a group of immigrant women researchers conducted a study on how they navigated the us academy as immigrant women faculty (ngunjiri et. This type of research attempts to expose problems, evaluate the problems and find their root causes. For example, critical social research could attempt to uncover cases of juvenile crime, racism, or suicide. The main difference between this type of research and other qualitative types is that there is always “a problem” that needs “fixing” going into the study. Traditional research uncovers problems or issues with interviews, data collection and other qr l inquiry is a research method used in philosophy to answer ethical questions such as is it ethical to eat animals? While anthropological research involves all of the cultures on the planet, classic ethnographic research provides a detailed description of an entire culture outside of the country of origin of the researcher(ingold, 2008). It may also include video footage, interviews with experts in the area being studied, conducting surveys or attending public discussion ed theory ed theory is often categorized as a qualitative research method, but technically it can be applied to either quantitative research or qualitative research; it’s a general research method involving a set of rigorous procedures. The “grounded” part refers to the fact that your theory needs to be grounded in research. In reality, it’s a type of qr that’s poorly understood, with many researchers claiming they used it, when in fact they did not. These questions identify core concepts, which lead you to identifying links between your questions and your data.
For a detailed outline of the grounded theory process, see odis simmon’s outline listic research is research that doesn’t manipulate anything in the environment. Care should be taken with naturalistic research, as even your presence can alter the environment–taking away the “naturalistic” component. It’s a common safeguard to have two or more researchers observing the same thing so that any differences in viewpoint can be ipant observer this type of research, you participate in the activity and record observations. It differs from naturalistic research because you actually participate in the activity you are researching. A fairly famous example of this type of research was undertaken by leon festinger, henry riecken and stanley schachter, who infiltrated a ufo religion called “the seekers. For example: a study of hurricane katrina survivors perceptions, understandings, and perspectives of the ages and disadvantages of qualitative research ative research is not part of statistical analysis. That’s because the results can’t be tested to see if they are statistically significant (i. That doesn’t mean this type of research is useless: in many studies, getting hard numbers is inappropriate or just qualitative research can’t be used to estimate statistics for a population, why use it at all? Is based on the participant’s views of the world, rather than a world created by a be used to figure out how people interpret constructs like iq or fear, which can be hard to study focus can be shifted in the middle of research, if necessary. In a traditional lab setting, this would usually null-and-void the important case can be used to vividly paint a picture in a of the main disadvantages to qualitative research is that your data usually can’t be generalized outside of your research.
This may make it more difficult to get your results collection takes a lot longer than in a traditional lab own personal biases and other idiosyncrasies are more likely to affect the -qualitative research method qualitative and quantitative research methods have their limitations. There is a recent trend towards a multi-method research approach which uses both types to:Quantify phenomena and make sure it’s statistically a broader picture of the tative research is quantitative research? In an experimental design, subjects are usually measured both before and after a treatment and you’re looking for causality. For example, you might be analyzing a treatment for a small number of cancer of data collection instrument. Typical methods for data collection are surveys or questionnaires with closed-ended questions, using data from another source (for example, a government database) or an experiment with a control group and an experimental of analysis tool (i. For example, you might choose to report your results using confidence intervals and test statistics from t tests or f tests with significance levels (alpha levels) and p research al research. Researching through archives: rare books, historical records and other historical data like school t analysis. Computer modeling is one of the research methods gradually becoming more popular especially, where ethical constraints prevent actual experiments or observation. Qualitative research and case study applications in education: revised and expanded from case study research in festinger, henry riecken and stanley schachter. You prefer an online interactive environment to learn r and statistics, this free r tutorial by datacamp is a great way to get started.