The population also has specific characteristics that are taken into consideration though out the study, and might include age, gender, employment statues, or education and are known as variables; variable meaning that the specific characteristics within the population may vary from unit to unit. Two different types of variables include dependent and independent variables. Dependent variables represent the output or effect that the population within the experimental unit undergoes. In contrast, independent variables represent the inputs or the cause of observed from the population. In research both types of variables not only are used but they have to be used.
The alternative hypothesis is a statement that is accepted only if the data proves evidence is true. This typically represents the values of a population parameter in which the researcher wants to gather evidence to support the hypothesis (McClave, Benson, & Sincich, 2011). The next steps are to select the appropriate test statistic and level of significance. The z-test is typically used when testing a hypothesis of a proportion and a t-test is used when testing a hypothesis of a mean. The test statistic is used to determine whether the researcher should use the null or alternative hypothesis.
General Guidelines for Evaluating Supportive Evidence * In determining the rigor of the science supporting a particular policy, procedure, or practice, most professional organizations have recommendations and guidelines for evaluating research study adequacy. * Greater scientific credence has often been assigned to findings based on quantitative research approaches. Randomized experimental group designs are considered “gold standard”. * Internal validity: the extent to which the research design controls for extraneous or confounding variables, variables that could support an alternative explanation for the findings. * External validity/selection bias: the number of study participants and how participants are selected, with greater external validity assigned to studies with a larger number or participants who are randomly assigned to experimental conditions.
In the systemic review, both data extraction and analysis will be performed in a collaborative manner. During the extraction process, the design of the study will be given great attention by focusing on randomized controlled trials. The review will also consider the number of articles with abstracts and methodologies that address the research question. In addition, the systemic review will take into account the sample characteristics by focusing on parameters as age, gender, size sample, and activity profile of the subjects. The research will also focus on the outcome measured by individual articles, results at baseline, the post-intervention practices carried out, and as well as any reported follow-up periods, and mean differences form the baseline, coupled with their statistical significance.
Quantitative psychological research is where the research findings result from mathematical modeling and statistical estimation or statistical inference. Since qualitative information can be handled as such statistically, the distinction relates to method, rather than the topic studied. There are three main types of psychological research: 1. Correlational research In statistics, dependence is any statistical relationship between two random variables or two sets of data. Correlation refers to any of a broad class of statistical relationships involving dependence.
What are the similarities and differences between qualitative and quantitative research? Qualitative research is deals with collecting descriptive information that cannot be definitely measured on an exact scale, often things that are observed. Examples would be emotions or feelings, attitudes, behavior, etc. It is a research method for exploring issues and topics in an attempt to understand them better and obtain answers and in some cases find similarities. Qualitative research is used in business research, market research and even scientific research.
One way to do this is by narrowly defining the sampling criteria to make the sample as homogeneous (or similar) as possible to control for extraneous variables. Other methods include randomization or random assignment of subjects to groups; matching subjects on extraneous variables and then assigning them randomly to groups; application of statistical techniques of analysis of covariance; and balancing means and standard deviations of groups (Mcleod, 2008). The amount of control that the researcher has over the variables being studied varies, from very little in exploratory studies to a great deal in experimental design, but the limitations on control must be addressed in any research proposal (Silverstein,
Is the evidence “tinted” by the way the writer presents or discusses it (An article in a scientific journal might list raw numbers along with formulas, ideally providing objective information . Informal essays, cast in the writer’s voice, may intentionally or unintentionally suggest an attitude towards the information that sways the reader.) Clearly, some of these subjects may overlap. If you see connections between the questions, you are doing well. Focus, however, on responding to one prompt.
Bias occurs when there is a systematic error within the specific research and their findings. Bias can come from several sources, and it can also be classified into two categories: non-sampling error and sampling error. The non-sampling error includes non-response bias, sample selection bias, and systematic measurement error. Sample selection bias can be encountered within real property studies, which are often relied on observational samples like the occurrence of a comparable sale that cannot be assumed to have been a random event. Sampling error stems from the fact that a random sample can differ from the underlying population simply by
Caroline Cashion Question #1 * A hypothesis is a proposition to be tested, or a statement of a relationship between two variables. Hypothesis testing uses quantitative research and is concerned with describing cause and effect relationships. The ultimate goal for researchers in hypothesis testing is to provide evidence that a particular independent variable has a causal relationship with a particular dependent variable. The independent variable is a concept or construct that is believed to produce some measurable response or outcome (Jensen 211). A hypothetical theoretical construct would make a prediction about the links between two variables, and then set out to discover if their prediction holds true.