To be complete the hypothesis must include three components:
A hypothesis should be:
Examples of a hypothesis are:
Types of hypothesis
Simple hypothesis - predicts the relationship between a single independent variable (IV) and a single dependent variable (DV).
For example: Lower levels of exercise postpartum (IV) is associated with greater weight retention (DV).
Complex hypothesis - predicts the relationship between two or more independent variables, and two or more dependent variables.
For example: The implementation of an evidence based protocol for urinary incontinence (IV) will result in (DV):
Used when the researcher believes there is no relationship between two variables, or when there is inadequate theoretical or empirical information to state a research hypothesis.
Methodology -- a theory or analysis of how research does and should proceed.
Method -- a systematic approach to a process. It includes steps of procedure, application of techniques, systems of reasoning or analysis, and the modes of inquiry employed by a discipline.
Mixed-Methods -- a research approach that uses two or more methods from both the quantitative and qualitative research categories.
Peer-Review -- the process in which the author of a book, article, or other type of publication submits his or her work to experts in the field for critical evaluation, usually prior to publication.
Population -- the target group under investigation. Samples are drawn from populations.
the chance that a phenomenon will occur randomly.
Elements of a Research Article
Research articles are a specific type of scholarly, peer-reviewed article. They typically follow a particular format and include specific elements that show how the research was designed, how the data was gathered, how it was analyzed, and what the conclusions are. Sometimes these sections may be labeled a bit differently, but these basic elements are consistent:
Abstract: A brief, comprehensive summary of the article, written by the author(s) of the article.This abstract must be part of the article, not a summary in the database. Abstracts can appear in secondary source articles as well as primary source.
Introduction: This introduces the problem, tells you why it’s important, and outlines the background, purpose, and hypotheses the authors are trying to test. The introduction comes first, just after the abstract, and is usually not labeled.
Review of Literature: Summarizes and analyzes previous research related to the problem being investigated.
Specific Question or Hypothesis: Often (but not always) in quantitative and mixed methods studies, specific questions or hypothesis are stated just before the methodology.
Method and Design: Researchers indicate who or what was studied (source of data), the methods used to gather information, and a procedures summary.
Results (findings): Summarizes the data and describes how it was analyzed. It should be sufficiently detailed to justify the conclusions.
Discussion: The authors explain how the data fits their original hypothesis, state their conclusions, and look at the theoretical and practical implications of their research. Sometimes called "Analysis."
Conclusions: A summary statement that reflects the overall answers to the resarch questions. Implications and recommendations are also included in this section.
References: A listing of the sources cited in the report.
A Brief Introduction to Research Designs: Quantitative, Qualitative, and Mix Methods (5:40 min)
by Christopher Smallwood
The goal in conducting a 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 associations between variables; an experimental study establishes causality (cause and effect).
Qualitative research looks at meaning, perspectives and motivations, rather than cause and effect. It typically has smaller sample sizes, uses focus groups, interviews, and/or observation, and the interviewer often plays an integral role in the investigation.
Approaches to qualitative research:
Mixed Methods Research
Combines elements of qualitative and quantitative design for the purpose of breadth and depth of understanding and corroboration. Mixed methods designs allow one design to strengthen the other, and addresses any inherent weaknesses by using either one alone.
There are three approaches:
by the Statistics Learning Centre
There are two main types of sampling: probability (random) and non-probability (non-random) sampling. The difference between the two is whether or not the sampling selection involves randomization. Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study. See the below video for sampling techniques, random and non-random that are commonly used in clinical research.
Random sampling techniques
|Simple Random||Every numbered population element has an equal probability of being selected||
In a study by Pimenta et al, researchers obtained a list of all elderly enrolled in the Family Health Strategy and, by simple random sampling, selected a sample of 449 participants.
|Systematic Sampling||A list of members of the population is used so that each nth element has an equal probability of being selected.||
In the study of Kelbore et al, children who were assisted at the Pediatric Dermatology Service were selected to evaluate factors associated with atopic dermatitis, selecting always the second child by consulting order.
|Stratified Random||Elements are selected randomly from strata that divide the population.||
A South Australia study investigated factors associated with vitamin D deficiency in preschool children. Using the national census as the sample frame, households were randomly selected in each stratum and all children in the age group of interest identified in the selected houses were investigated.
|Complex (Cluster) Sampling||Equal groups are identified and selected randomly, and participants in each group selected are used as the sample.||Five of 25 city blocks, each containing a high percentage of low socioeconomic status families, is selected randomly and parents in each selected block are surveyed.|
Non-Random Sampling Techniques
|Convenience||Sample of participants are convenient for researchers to recruit.||Study participants are randomly allocated to the intervention or control group.|
|Purposeful||Participants are selected by researchers based on specific criteria in order to fulfill the study's objective.||Women between the ages of 40 and 60, diagnosed with rheumatoid arthritis and Sjogren's syndrome, were selected to participate in the study.|
A population is first segmented into mutually exclusive sub-groups. Researcher judgment is then used to select study participants from each sub-group, based on a specified proportion.
A combination of vemurafenib and cobimetinib versus placebo was tested in patients with locally-advanced melanoma. The study recruited 495 patients from 135 health centers located in several countries.
|Snowball||The researcher selects an initial group of individuals. Then these participants indicate other potential members with similar characteristics to take part in the study.||
Frequently used in studies investigating special populations, for example, those including illicit drugs users, as was the case of the study by Gonçalves et al, which assessed 27 users of cocaine and crack in combination with marijuana.
Types of Data: Nominal, Ordinal, Interval/Ratio (6:19 min)
by the Statistics Learning Centre
One of the most commonly used terms in quantitative research is variable. A variable is a measurable or quantifiable characteristic of a concept, person, object or phenomenon that can take different values, numerically or categorically.
Examples: Values for variables can be a measurable quantity e.g. height, age, weight, blood pressure, or it may be a qualitative factor, e.g. color, sex, behavior.
Types of variables
Independent variable - The variable that is used to describe or measure the factors that are assuming to cause or at least influence the other variable is called independent variable. The independent variable is given to the participants, usually for some specified time period. It is often manipulated and controlled by the investigator who sets its values by specifying how it will be used in the study.
Example: if we study role of cholesterol in genesis of hypertension and atherosclerosis, cholesterol is independent variable, and hypertension and atherosclerosis are dependent variables.
Dependent Variable - The variable that gets modified under the influence of some other (independent) variable is called the dependent variable.
Measurement is assigning numbers to indicate different values of a trait, characteristic, or other unit that is being investigated as a variable. The purpose is to quantitatively describe the variables and units of study that are being investigated.
Measurement requires that variables be differentiated and there are four ways to achieve this, dependent on the nature of the data. These four methods are referred to as scales of measurement.
1st Method - nominal variables - the values assigned to each category are simply labels rather than meaningful numbers.
Nominal Time of Day - categories; no additional information
2nd Method - ordinal variables - values are placed in meaningful order (categories are rank ordered) but the distances between each unit are not equal.
Ordinal Time of Day - indicates direction or order of occurrence; spacing between is uneven
3rd Method - interval scale variables - values are placed in meaningful order and the distances between each unit are equal.
Interval Time of Day - equal intervals; analog (12-hr.) clock, difference between 1 and 2 pm is same as difference between 11 and 12 am
4th Method - ratio variables -- In addition to possessing the qualities of nominal, ordinal, and interval scales, a ratio scale has an absolute zero (a point where none of the quality being measured exists).
Ratio - 24-hr. time has an absolute 0 (midnight); 14 o'clock is twice as long from midnight as 7 o'clock
Descriptive statistics, a subset of statistics, helps researchers and readers understand the information of data collected through organization, summarization, and visualization. It allows readers, patients, and healthcare providers to interpret and make sense of data derived through research, and if appropriate, implement findings into practice.
Descriptive statistics are broken down into measures of central tendency, and measures of variability (spread). Measures of central tendency include the mean, median, and mode, while measures of variability include the range (the difference between the maximum and minimum observations), variance, standard deviation, kurtosis and skewness. Below is a summarized explanation of the most commonly used descriptive statistics in health publications.
Shape and Normality
Kurtosis: the extent to which a frequency distribution is peaked or flat.
Skew: a measure of symmetry. If one tail is longer than another, the distribution is skewed. Also called asymmetric or asymmetrical distributions.
Finding Mean, Median, and Mode: Descriptive Statistics: Probability and Statistics (3:54)
by Khan Academy
Dispersion or Variation
by Khan Academy
Validity is the extent to which the scores from a measure represent the variable they are intended to. When a measure has good test-retest reliability and internal consistency, researchers should be more confident that the scores represent what they are supposed to. There has to be more to it, however, because a measure can be extremely reliable but have no validity whatsoever. As an absurd example, imagine someone who believes that people’s index finger length reflects their self-esteem and therefore tries to measure self-esteem by holding a ruler up to people’s index fingers. Although this measure would have extremely good test-retest reliability, it would have absolutely no validity. The fact that one person’s index finger is a centimeter longer than another’s would indicate nothing about which one had higher self-esteem.
Three types of validity tests are:
Reliability and Validity (8:18 min)
Sensitivity and specificity are statistical measures of test sensitivity; test sensitivity is the ability of a test to correctly identify those with the disease (true positive rate), whereas test specificity is the ability of the test to correctly identify those without the disease (true negative rate).
Sensitivity is essentially how good a test is at finding something if it's there. It is a measure of how often the test will correctly identify a positive among all positive by the gold standard test. For example, a blood test for a virus may have sensitivity as high as 99% or more — meaning that for every 100 infected people testing, 99 or more of them will be correctly identified. This is a good figure to take note of, but doesn't necessarily reflect a test's true effectiveness, as will become apparent.
Specificity is a measure of how accurate a test is against false positives. A sniffer dog looking for drugs would have a low specificity if it is often lead astray by things that aren't drugs — cosmetics or food, for example. Specificity can be considered as the percentage of times a test will correctly identify a negative result. Again, this can be 99% or more for good tests, although a particularly unruly and easily distracted sniffer dog would be much, much lower.
Sensitivity and Specificity (4:43 min)
by Medmastery - The clinical skills academy