How to structure quantitative research questions. STEP TWO Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in Whether you are trying to create a descriptive, comparative or relationship-based research question, you will need to identify the different types of variable that you are trying to measure, manipulate and/or control.
If you are unfamiliar with the different types of variable that may be part of your study, the article, Types of variable, should get you up to speed. It explains the two main types of variables: categorical variables (i.e., nominal, dichotomous and ordinal variables) and continuous variables (i.e., interval and ratio variables). It also explains the difference between independent and dependent variables, which you need to understand to create quantitative research questions. To provide a brief explanation; a variable is not only something that you measure, but also something that you can manipulate and control for. Operational definition (What is it? When is it used?) What is it?
An operational definition, when applied to data collection, is a clear, concise detailed definition of a measure. The need for operational definitions is fundamental when collecting all types of data.
Learning Math. Notessp2013. General Intros. Onlinestatbook's channel. Probability and statistics. Introduction to Descriptive Statistics. Variables. Variables Author(s) Heidi Ziemer Prerequisites none Learning Objectives Define and distinguish between independent and dependent variables Define and distinguish between discrete and continuous variables Define and distinguish between qualitative and quantitative variables Independent and dependent variables Example #1: Can blueberries slow down aging?
1. Example #3: How bright is right? 1. Basic Statistics: About Incidence, Prevalence, Morbidity, and Mortality - Statistics Teaching Tools - New York State Department of Health. What is incidence?
Independent variables are the conditions within a study the experimenter directly manipulates. Independent Variables (IV) are the conditions within a study the experimenter directly manipulates.
For example, in Osborn's (1996) "Makeup causes attractiveness? " study he assigned participants to see a woman with or without makeup. So makeup was the independent variable with two levels, makeup versus no-makeup. By comparing the average rating of attractiveness of the woman with makeup to the average rating by a different group of participants who saw the woman without makeup, he could estimate how important a cause makeup (independent variable) is in effecting attractiveness ratings (dependent variable). So the cause is makeup and the effect is attractiveness ratings. Variables. Reporting Statistics - Reporting_Statistics.pdf. Biostatistics - Populations. Statistical Studies in Populations In order for a study to be performed, the population must be defined.
When one studies the extent of diabetes mellitus amongst the Hispanic population of Los Angeles, does that mean both males and females, adults only, persons who have emigrated from Latin America, persons who live just in the city of Los Angeles, etc? One must be very careful about defining the population to be studied. Since it is not practical to perform tests or measures on all members of a population, then one must obtain a sample of that population. There are methods available to randomize the sampling of the population. 3. Populations and samples. Populations In statistics the term "population" has a slightly different meaning from the one given to it in ordinary speech.
It need not refer only to people or to animate creatures - the population of Britain, for instance or the dog population of London. Statisticians also speak of a population of objects, or events, or procedures, or observations, including such things as the quantity of lead in urine, visits to the doctor, or surgical operations. A population is thus an aggregate of creatures, things, cases and so on. Although a statistician should clearly define the population he or she is dealing with, they may not be able to enumerate it exactly. Samples. Sample Size. Volume 11, No. 3, Art. 8 – September 2010 Sample Size and Saturation in PhD Studies Using Qualitative Interviews Mark Mason Abstract: A number of issues can affect sample size in qualitative research; however, the guiding principle should be the concept of saturation.
This has been explored in detail by a number of authors but is still hotly debated, and some say little understood. A sample of PhD studies using qualitative approaches, and qualitative interviews as the method of data collection was taken from theses.com and contents analysed for their sample sizes. Key words: saturation; sample size; interviews. Starting Statistical Analysis. Statistics might sound daunting, or it might not.
Either way, after using PANDA the use of statistics should seem more practical and the basic concepts should be ready at your fingertips for future use. In order to make a smooth transition, this section will provide some statistics background that will be useful for PANDA modules such as the Two-way and Multi-way Analysis. Hope this makes things a lot easier. Reporting Statistics in APA Style. Dr.
Jeffrey Kahn, Illinois State University The following examples illustrate how to report statistics in the text of a research report. You will note that significance levels in journal articles--especially in tables--are often reported as either "p > .05," "p < .05," "p < .01," or "p < .001. " APA style dictates reporting the exact p value within the text of a manuscript (unless the p value is less than .001).
Please pay attention to issues of italics and spacing. Mean and Standard Deviation are most clearly presented in parentheses: The sample as a whole was relatively young (M = 19.22, SD = 3.45). Percentages are also most clearly displayed in parentheses with no decimal places: Nearly half (49%) of the sample was married. Chi-Square statistics are reported with degrees of freedom and sample size in parentheses, the Pearson chi-square value (rounded to two decimal places), and the significance level: Statistical Data Types. Statistical hypothesis testing and some pitfalls. Lessons in biostatistics Department of Biophysics, Medical Statistics and Medical Informatics, J.J.Strossmayer University of Osijek, School of Medicine, Osijek, Croatia Abstract Data analysis for research purposes usually aims to use the information gained from a sample of individuals in order to make inferences about the relevant population.
Statistical hypothesis testing is a widely used method of statistical inference. It is important to a reader of scientific or expert journals, as well as to a researcher, to understand the basic concepts of the testing procedure, in order to make sound decision and opinion on presented results. This article gives an overview of basic steps in the general procedure for statistical hypothesis testing and points out some common pitfalls and misconceptions.
Key words: statistical hypothesis testing; P value; significance level; multiplicity problem.