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Edited assignment in research

- 1. Recently, many researchers use both methods, thereby the era of using mixed methods in research arose as a more desirable and encompassing approach in understanding phenomena. Qualitative methods may be used to explore a phenomenon and identify factors for a quantitative study. Or, a quantitative study may identify research areas that require the application of qualitative methods to provide an in-depth understanding of the phenomenon at hand or when the use of quantitative methods is insufficient to answer questions that relate to human behaviour such as feelings, values, and beliefs. Quantitative Research Quantitative data are pieces of information that can be counted and which are usually gathered by surveys from large numbers of respondents randomly selected for inclusion. Secondary data such as census data, government statistics, health system metrics, etc. are often included in quantitative research. Quantitative data is analysed using statistical methods. Quantitative approaches are best used to answer what, when and who questions and are not well suited to how and why questions. Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality. Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner]. The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed. Characteristics The data is usually gathered using structured research instruments. The results are based on larger sample sizes that are representative of the population. The research study can usually be replicated or repeated, given its high reliability. Researcher has a clearly defined research question to which objective answers are sought. All aspects of the study are carefully designed before data is collected. Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms. Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships. Researcher uses tools, such as questionnaires or computer software, to collect numerical data.
- 2. 7 Characteristics of Quantitative Methods Seven characteristics discriminate qualitative methods of research from qualitative ones. 1. Data gathering instruments contain items that solicit measurable characteristics of the population (e.g. age, the number of children, educational status, economic status). 2. Standardized, pre-tested instruments guide data collection thus ensuring the accuracy, reliability and validity of data. 3. For more reliable data analysis, a normal population distribution curve is preferred over a non- normal distribution. This requires a large population, the numbers of which depend on how the characteristics of the population vary. This requires adherence to the principle of random sampling to avoid researcher’s bias in interpreting the results that defeat the purpose of research. 4. The data obtained using quantitative methods are organized using tables, graphs, or figures that consolidate large numbers of data to show trends, relationships, or differences among variables. This fosters understanding to the readers or clients of the research investigation. 5. Researchers can repeat the quantitative method to verify or confirm the findings in another setting. This reinforces the validity of groundbreaking discoveries or findings thus eliminating the possibility of spurious or erroneous conclusions. 6. Quantitative models or formula derived from data analysis can predict outcomes. If-then scenarios can be constructed using complex mathematical computations with the aid of computers. 7. Advanced digital or electronic instruments are used to measure or gather data from the field. Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified. Strengths Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results; Allows for greater objectivity and accuracy of results. Generally, quantitativemethodsare designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability; Applying well establshed standards means that the research can be replicated, and then analyzed and compared with similar studies; You can summarize vast sources of information and make comparisons across categories and over time; and, Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques. Quantitativemethodspresumetohave anobjective approachtostudying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.
- 3. Weaknesses Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail; Uses a static and rigid approach and so employs an inflexible process of discovery; The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject; Results provide less detail on behavior, attitudes, and motivation; Researcher may collect a much narrower and sometimes superficial dataset; Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception; The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and, Preset answers will not necessarily reflect how peoplereally feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis. Limitations and weakness of quantitative research methods Improper representation of the target population As mentioned in the article, improper representation of the target population might hinder the researcher for achieving its desired aims and objectives. Despite of applying appropriate sampling plan representation of the subjects is dependent on the probability distribution of observed data. This may led Lack of resources for data collection Quantitative research methodology usually requires a large sample size. However due to the lack of resources this large-scale research becomes impossible. In many developing countries, interested parties (e.g., government or non-government organisations, public service providers, educational institutions, etc.) may lack knowledge and especially the resources needed to conduct a thorough quantitative research (Science 2001). Inability to control the environment Sometimes researchers face problems to control the environment where the respondents provide answers to the questions in the survey (Baxter 2008). Responses often depend on particular time which again is dependent on the conditions occurring during that particular time frame. Limited outcomes in a quantitative research Quantitative research method involves structured questionnaire with close ended questions. It leads to limited outcomes outlined in the research proposal. So the results cannot always represent the actual occurring, in a generalised form. Also, the respondents have limited options of responses, based on the selection made by the researcher. Expensive and time consuming Quantitative research is difficult, expensive and requires a lot of time to be perform the analysis. This typeof research is planned carefully in order toensure completerandomization and correct designation of control groups (Morgan 1980). A large proportion of respondents is appropriate for the representation of the target population. So, as to achieve in-depth responses on an issue, data collection in quantitative research methodology is often too expensive as against qualitative approach.
- 4. Difficultly in data analysis Quantitative study requires extensive statistical analysis, which can be difficult to perform for researchers from non- statistical backgrounds. Statistical analysis is based on scientific discipline and hence difficult for non-mathematicians to perform. Quantitativeresearchis a lot more complex for social sciences, education, anthropology andpsychology. Effective response should depend on the research problem rather than just a simple yes or no response. Requirement of extra resources to analyse the results The requirements for the successful statistical confirmation of result is very tough in a quantitative research. Hypothesis is proven with few experiments due to which there is ambiguity in the results. Results are retested and refined several times for an unambiguous conclusion (Ong 2003). So it requires extra time, investment and resources to refine the results. Types There are four main types of quantitative research designs: descriptive, correlational, quasi- experimental and experimental. The differences between the four types primarily relates to the degree the researcher designs for control of the variables in the experiment. Following is a brief description of each type of quantitative research design, as well as chart comparing and contrasting the approaches. A DescriptiveDesign seeks to describe the current status of a variable or phenomenon. The researcher does not begin with a hypothesis, but typically develops one after the data is collected. Data collection is mostly observational in nature. A Correlational Design explores the relationship between variables using statistical analyses. However, it does not look for cause and effect and therefore, is also mostly observational in terms of data collection. A Quasi-Experimental Design (often referred to as Causal-Comparative) seeks to establish a cause- effect relationship between two or more variables. The researcher does not assign groups and does not manipulate the independent variable. Control groups are identified and exposed to the variable. Results are compared with results from groups not exposed to the variable. Experimental Designs, often called true experimentation, use the scientific method to establish cause- effect relationship among a group of variables in a research study. Researchers make an effort to control for all variables except the one being manipulated (the independent variable). The effects of the independent variable on the dependent variable are collected and analyzed for a relationship. Survey Research Survey research uses interviews, questionnaires, and sampling polls to get a sense of behavior with intense precision. It allows researchers to judge behavior and then present the findings in an accurate way. This is usually expressed in a percentage. Survey research can be conducted around one group specifically or used to compare several groups. When conducting survey research it is important that the people questioned are sampled at random. This allows for more accurate findings across a greater spectrum of respondents.
- 5. It is very important when conducting survey research that you work with statisticians and field service agents who are reputable. Since there is a high level of personal interaction in survey scenarios as well as a greater chance for unexpected circumstances to occur, it is possible for the data to be affected. This can heavily influence the outcome of the survey. There are several ways to conduct survey research. They can be done in person, over the phone, or through mail or email. In the last instance they can be self-administered. When conducted on a single group survey research is its own category. However survey research can be applied to the other types of research listed below. Correlational Research Correlational research tests for the relationships between two variables. Performing correlational research is done to establish what the affect of one on the other might be and how that affects the relationship. Correlational research is conducted in order to explain a noticed occurrence. In correlational research the survey is conducted on a minimum of two groups. In most correlational research there is a level of manipulation involved with the specific variables being researched. Once the information is compiled it is then analyzed mathematically to draw conclusions about the effect that one has on the other. Remember, correlation does not always mean causation. For example, just because two data points sync doesn’t mean that there is a direct cause and effect relationship. Typically, you should not make assumptions from correlational research alone. Causal-Comparative Research Causal-comparative research looks to uncover a cause and effect relationship. This research is not conducted between the two groups on each other. Rather than look solely for a statistical relationship between two variables it tries to identify, specifically, how the different groups are affected by the same circumstance. Causal-comparative research involves ‘comparison.’ In causal-comparative research the study of two or more groups is done without focusing on their relationship. As always the use of statistical analysis is engaged to synthesize the data in a clear method for presentation. Experimental Research Though questions may be posed in the other forms of research, experimental research is guided specifically by a hypothesis.Sometimesexperimental research canhaveseveral hypotheses.Ahypothesis is a statement to be proven or disproved. Once that statement is made experiments are begun to find out whether the statement is true or not. This type of research is the bedrock of most sciences, in particular the natural sciences.
- 6. Kinds of Variables We will distinguish three different kinds of variables. Many more are possible, but for our purposes this will be sufficient. 1. Interval Variables: A measurement of something where the relationship among values is clearly defined in a quantitative manner. Can say that one level is a mathematically defined function of another. Height, Income, Percent time spent studying. An income of $10000 is twice that of an income of $5000. 2. Ordinal Variables: A qualitative assessment of something where the relationship among levels of the variable is known but only in a qualitative manner. Ranks of things like preferences for physical attributes in possible mates. 3. Nominal Variables: Qualitative assessment of something where relationship among values of the variable is unknown. Sex, ethnicity, religious affiliation. All levels of the variable must be mutually exclusive. That is, a person cannot belong to more than one level of income or to more than one sex. Latent vs. Manifest Variables: Latent Variables: These are the theoretical dependent and independent variables that you wish to measure. Manifest Variables: These are the things that you actually measure. The goal is to achieve convergence between your manifest and your latent variables to the degree that it is possible. Types of Variable All experiments examine some kind of variable(s). A variable is not only something that we measure, but also something that we can manipulate and something we can control for. To understand the characteristics of variables and how we use them in research, this guide is divided into three main sections. First, we illustrate the role of dependent and independent variables. Second, we discuss the difference between experimental and non-experimental research. Finally, we explain how variables can be characterised as either categorical or continuous.
- 7. Dependentand Independent Variables An independent variable, sometimes called an experimental or predictor variable, is a variable that is being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes called an outcome variable. Imagine that a tutor asks 100 students to complete a maths test. The tutor wants to know why some students perform better than others. Whilst the tutor does not know the answer to this, she thinks that it might be because of two reasons: (1) some students spend more time revising for their test; and (2) some students are naturally more intelligent than others. As such, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. The dependent and independent variables for the study are: Dependent Variable: Test Mark (measured from 0 to 100) Independent Variables: Revision time (measured in hours) Intelligence (measured using IQ score) The dependent variable is simply that, a variable that is dependent on an independent variable(s). For example, in our case the test mark that a student achieves is dependent on revision time and intelligence. Whilst revision time and intelligence (the independent variables) may (or may not) cause a change in the test mark (the dependent variable), the reverse is implausible; in other words, whilst the number of hours a student spends revising and the higher a student's IQ score may (or may not) change the test mark that a student achieves, a change in a student's test mark has no bearing on whether a student revises more or is more intelligent (this simply doesn't make sense). Categorical and ContinuousVariables Categorical variables are also known as discrete or qualitative variables. Categorical variables can be further categorized as either nominal, ordinal or dichotomous. o Nominal variables are variables that have two or more categories, but which do not have an intrinsic order. For example, a real estate agent could classify their types of property into distinct categories such as houses, condos, co-ops or bungalows. So "type of property" is a nominal variable with 4 categories called houses, condos, co-ops and bungalows. Of note, the different categories of a nominal variable can also be referred to as groups or levels of the nominal variable. Another example of a nominal variable would be classifying where people live in the USA by state. In this case there will be many more levels of the nominal variable (50 in fact). o Dichotomous variables are nominal variables which have only two categories or levels. For example, if we were looking at gender, we would most probably categorize somebody as either "male" or "female". This is an example of a dichotomous variable (and also a nominal variable). Another example might be if we asked a person if they owned a mobile phone. Here, we may categorise mobile phone ownership as either "Yes" or "No". In the real estate agent example, if type of property had been classified as either residential or commercial then "type of property" would be a dichotomous variable. o Ordinal variables are variables that have two or more categories just like nominal variables only the categories can also be ordered or ranked. So if you asked someone if they liked the policies of the Democratic Party and they could answer either "Not very much", "They are OK" or "Yes, a lot" then you have an ordinal variable. Why? Because you have 3 categories, namely
- 8. "Not very much", "They are OK" and "Yes, a lot" and you can rank them from the most positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not very much). However, whilst we can rank the levels, we cannot place a "value" to them; we cannot say that "They are OK" is twice as positive as "Not very much" for example. Continuous variables are also known as quantitative variables. Continuous variables can be further categorized as either interval or ratio variables. o Interval variables are variables for which their central characteristic is that they can be measured along a continuum and they have a numerical value (for example, temperature measured in degrees Celsius or Fahrenheit). So the difference between 20C and 30C is the same as 30C to 40C. However, temperature measured in degrees Celsius or Fahrenheit is NOT a ratio variable. o Ratio variables are interval variables, but with the added condition that 0 (zero) of the measurement indicates that there is none of that variable. So, temperature measured in degrees Celsius or Fahrenheit is not a ratio variable because 0C does not mean there is no temperature. However, temperature measured in Kelvin is a ratio variable as 0 Kelvin (often called absolute zero) indicates that there is no temperature whatsoever. Other examples of ratio variables include height, mass, distance and many more. The name "ratio" reflects the fact that you can use the ratio of measurements. So, for example, a distance of ten metres is twice the distance of 5 metres. Types of Variables in Statistics and Research While a “variable” in algebra really just means one thing–an unknown value–you’ll come across dozens of types of variables in statistics. Some are used more than others. For example, you’ll be much more likely to come across continuous variables than you would dummy variables. Click on a variable name to learn more about that particular type. Common Types of Variables Categorical variable: variables than can be put into categories. For example, the category “Toothpaste Brands” might contain the variables Colgate and Aquafresh. Confounding variable: extra variables that have a hidden effect on your experimental results. Continuousvariable: a variable with infinite number of values, like “time” or “weight”. Control variable: a factor in an experiment which must be held constant. For example, in an experiment to determine whether light makes plants grow faster, you would have to control for soil quality and water. Dependentvariable: the outcome of an experiment. As you change the independent variable, you watch what happens to the dependent variable. Discrete variable: a variable that can only take on a certain number of values. For example, “number of cars in a parking lot” is discrete because a car park can only hold so many cars. Independent variable: a variable that is not affected by anything that you, the researcher, does. Usually plotted on the x-axis. A measurement variable has a number associated with it. It’s an “amount” of something, or a”number” of something. Nominal variable: another name for categorical variable. Ordinal variable: similar to a categorical variable, but there is a clear order. For example, income levels of low, middle, and high could be considered ordinal.
- 9. Qualitative variable: a broad category for any variable that can’t be counted (i.e. has no numerical value). Nominal and ordinal variables fall under this umbrella term. Quantitative variable: A broad category that includes any variable that can be counted, or has a numerical value associated with it. Examples of variables that fall into this category include discrete variables and ratio variables. Ratio variables: similar to interval variables, but has a meaningful zero. Less Common Types of Variables Attribute variable: another name for a categorical variable (in statistical software) or a variable that isn’t manipulated (in design of experiments). Binary variable: a variable that can only take on two values, usually 0/1. Could also be yes/no, tall/short or some other two-variable combination. Collider Variable: a variable represented by a node on a causal graph that has paths pointing in as well as out. Covariate variable: similar to an independent variable, it has an effect on the dependent variable but is usually not the variable of interest. Criterionvariable: another name for a dependent variable, when the variable is used in non- experimental situations. Dichotomous variable: Another name for a binary variable. Dummy Variables: used in regression analysis when you want to assign relationships to unconnected categorical variables. For example, if you had the categories “has dogs” and “owns a car” you might assign a 1 to mean “has dogs” and 0 to mean “owns a car.” Endogenous variable: similar to dependent variables, they are affected by other variables in the system. Used almost exclusively in econometrics. Exogenous variable: variables that affect others in the system. Identifier Variables: variables used to uniquely identify situations. Indicator variable: another name for a dummy variable. Interval variable: a meaningful measurement between two variables. Also sometimes used as another name for a continuous variable. Intervening variable: a variable that is used to explain the relationship between variables. LatentVariable: a hidden variable that can’t be measured or observed directly. Manifestvariable: a variable that can be directly observed or measured. Manipulated variable: another name for independent variable. Mediating variable: variables that explain how the relationship between variables happens. For example, it could explain the difference between the predictor and criterion. Moderating variable: changes the strength of an effect between independent and dependent variables. For example, psychotherapy may reduce stress levels for women more than men, so sex moderates the effect between psychotherapy and stress levels. Nuisance Variable: an extraneous variable that increase variability overall. Observed Variable: a measured variable (usually used in SEM). Outcome variable: similar in meaning to a dependent variable, but used in a non-experimental study. Polychotomous variables: variables that can have more than two values. Predictor variable: similar in meaning to the independent variable, but used in regression and in non-experimental studies. Test Variable: another name for the Dependent Variable. Treatment variable: another name for independent variable.
- 10. Variable Types Variables are often specified according to their type and intended use. Be careful when referring to a variable; don't confuse the variable with values it may take on. In the examples below I'll attempt to describe this common error. Different types of variables Quantitative Variable A quantitative variable is naturally measured as a number for which meaningful arithmetic operations make sense. Examples: Height, age, crop yield, GPA, salary, temperature, area, air pollution index (measured in parts per million), etc. Categorical Variable Any variable that is not quantitative is categorical. Categorical variables take a value that is one of several possible categories. As naturally measured, categorical variables have no numerical meaning. Examples: Hair color, gender, field of study, college attended, political affiliation, status of disease infection. In a study asking respondents to identify themselves as Republican, Democrat or Independent or Other, each respondent will answer with exactly one of these. These are the values the variable takes. Republican is not a variable (it does not vary from person to person) but party affiliation is (it does vary from person to person). Often categorical variables are disguised as quantitative variables. For example, one might record gender information coded as 0 = Male, 1 = Female. (Data is generally easier to manipulate in an analysis spreadsheet when it's coded quantitatively.) Still---the variable is categorical; it is not naturally measured as a number. In some cases it's tougher to make the distinction. A psychologist may collect survey data of the following nature How do you feel abouttheinformationon this page? (Circle one.) 1 2 3 4 5 Awful Poor OK Good Great It's a toss up. Technically the numbers are artificial. But, the psychologist will work with these numbers as though they had meaning. For instance, two people might respond "Awful" and "OK." The psychologist would record 1 and 3 and, perhaps, compute an average of 2.0. From my point of view it's meaningless to have the average of Awful and OK being Poor! Nevertheless, this sort of scale (called a "Likert Scale") is often used in social science research. I'd
- 11. classify this variable as categorical; it would not be entirely incorrect to classify it as quantitative. You can see how any categorical variable may be coded to look like a quantitative variable--- simply by arbitrarily assigning numbers to categories. Ordinal Variable On ordinal variable is a special type of categorical variable for which the levels can be naturally ordered. The example above provides a good illustration of an ordinal variable. Even if we ignore the numbers, we still may order the responses. Awful is "worse than" Poor; Poor is worse than OK; OK is worse than Good; Good is worse than Great. A natural ordering exists for these categories. Contrast this with a categorical variable such as hair color. There is no natural ordering for the various colors of hair.. Math 158 students do not worry much about ordinal variables. They treat them as if they were categorical variables. However, in advance statistics ordinal variables are treated differently to make use of the added structure they give to a variable. Different uses of variables In many studies more than one variable is recorded per case or individual. It is often the purpose of the study to determine if and/or how one or more variables affect another. This is a basic paradigm in statistical analysis; the distinctions that are made here are integral to the way a problem is stated and analyzed. ResponseVariable The outcome of a study. A variable you would be interested in predicting or forecasting. Often called a dependent variable or predicted variable. Explanatory Variable Any variable that explains the response variable. Often called an independent variable or predictor variable. In general identifying these amounts to deciding which variable(s) would be used to predict another. Here follow a few examples. Example Consider a study performed by a medical center to determine which of two heart surgeries is most effective: angioplasty (running rubber tubes through the arteries) or bypass (rerouting arteries). The purpose of either procedure is to prolong the life of the patient. The study will certainly record the survival time of each patient (measured from the time of the surgery). This really is the outcome of the study; survival time is the response variable. Now, each patient will get one of the two types of operations; this is a second variable. . .let's call it the "procedure" variable; it takes one of two possible values, Angioplasty and Bypass. The entire purpose of the study is to determine how, if at all, the
- 12. procedure affects survival time. Type of surgery is an explanatory variable. We would use type of operation (explanatory variable or predictor) to predict survival time (response or predicted variable). Survival time may well depend on procedure; survival time is the dependent variable and procedure is the independent variable. Note that the response is measured after the explanatory. This is often---but not always---the case. The response variable is quantitative, the explanatory variable is categorical. In a true clinical trial many more explanatory variables would be recorded: gender, age at the time of surgery, state of health pre-surgery (how would this be measured?), numerous physiological indicators and so forth. There would be but one response variable, survival time! (Actually, there would be others. Quality of life after the operation is important, as is an analysis of the side-effects attributable to the two procedures.) Importance across fields Quantitative research designis the standard experimental method of most scientific disciplines. These experiments are sometimes referred to as true science, and use traditional mathematical and statistical means to me asure results conclusively. They are most commonly used by physical scientists, although social sciences, education and economics have been known to use this type of research. It is the opposite of qualitative research. Quantitative experiments all use a standard format, with a few minor inter-disciplinary differences, of generating a hypothesis to be proved or disproved. This hypothesis must be provable by mathematical and statistical means, and is the basis around which the whole experiment is designed. Randomization of any study groups is essential, and a control group should be included, wherever possible. A sound quantitative design should only manipulate one variable at a time, or statistical analysis becomes cumbersome and open to question. Ideally, the research should be constructed in a manner that allows others to repeat the experiment and obtain similar results.