More Related Content
What's hot
What's hot (20)
Similar to Lecture no 03 mspt 1st semester research methods in rehabilitation by abdul ghafoor sajjad
Similar to Lecture no 03 mspt 1st semester research methods in rehabilitation by abdul ghafoor sajjad (20)
Recently uploaded
Recently uploaded (20)
Lecture no 03 mspt 1st semester research methods in rehabilitation by abdul ghafoor sajjad
- 4. Thinking like a researcher Barriers to rehabilitation research Obstacles to rehabilitation research Overcoming barriers Research publication vehicles Theory in rehabilitation research Criteria for evaluating research problems Evidence-based practice Finding research literature Criteria for evaluating research problems Types of information Some common rehabilitation databases In this Lecture
- 5. 1st Semester MS-PT(NMPT) RRTM 6052 (02CH) Research Methods in Rehabilitation LECTURE NO 03
- 6. Variables, and Definitions Problems and Hypotheses Defining the research problem, Formulation of the research hypotheses, The importance of problems and hypotheses In this Lecture
- 7. Dr Abdul Ghafoor Sajjad HOD/Associate Professor Department of Rehabilitation Sciences STMU
- 10. One of the most important functions of clinical science—and science in general—is to determine cause and effect. VARIABLES
- 11. “When there is a change in ‘A,’ does a change in ‘B’ result?” We can suppose that the change in “A” was the cause of the change in “B.” We are constantly applying empirical methods. In looking for cause-and- effect relationships. VARIABLES
- 12. To understand cause and effect, the concept of variables must be understood Variables change within the course of an experiment, whether clinical treatment or not. VARIABLES
- 13. If there is no change to an aspect, that aspect is a constant, not a variable. A Physical therapist tries two different treatments for low back pain on a series of 70-year-old patients. Because treatment varied (i.e., there were two), treatment is a variable. Because age did not vary (i.e., all patients were 70), age was not a variable, but a constant VARIABLES
- 14. Variables are things you measure, manipulate and control in statistics and research. All studies analyze a variable, which can describe a person, place, thing or idea. A variable's value can change between groups or over time. VARIABLES
- 16. •Discrete •Continuous Quantitative variables •Binary •Nominal •Ordinal Qualitative variables Confounding variables Composite variables TYPES OF VARIABLES
- 19. An independent variable is a singular characteristic that the other variables in your experiment cannot change. Age is an example of an independent variable. Where someone lives, what they eat or how much they exercise are not going to change their age. Independent variables can, however, change other variables. INDEPENDENT VARIABLES
- 21. Dependent variables are the outcomes of a study, whether clinical intervention or not. They are dependent because their values depend on changes in the independent variable. DEPENDENT VARIABLES
- 22. Dependent variables are the outcomes of a study, whether clinical intervention or not. They are dependent because their values depend on changes in the independent variable. DEPENDENT VARIABLES
- 23. In physical therapy, some common dependent variables are: Range of motion, Pain rating, Functional Independence Measure. DEPENDENT VARIABLES
- 24. A dependent variable must be operationally defined. That is, the researcher must decide how to measure the outcome. It is not enough to say that range of motion is being measured; the metric used (degrees, in this case) must be specified. DEPENDENT VARIABLES
- 25. Hinman and colleagues3 examined the effects of aquatic physical therapy on hip or knee osteoarthritis. Outcome included measures of pain, physical function, physical activity levels, quality of life, and muscle strength. DEPENDENT VARIABLES
- 27. Difference Between VAS & NPRS ? DEPENDENT VARIABLES
- 29. An intervening variable, sometimes called a mediator variable, is a theoretical variable the researcher uses to explain a cause or connection between other study variables Usually dependent and independent ones. They are associations instead of observations. For example, if wealth is the independent variable, and a long life span is a dependent variable, the researcher might hypothesize that access to quality healthcare is the intervening variable that links wealth and life span. INTERVENING VARIABLES
- 31. A moderating or moderator variable changes the relationship between dependent and independent variables by strengthening or weakening the intervening variable's effect. For example, in a study looking at the relationship between economic status (independent variable) and how frequently people get physical exams from a doctor (dependent variable), age is a moderating variable. That relationship might be weaker in younger individuals and stronger in older individuals. MODERATING VARIABLES
- 33. Control or controlling variables are characteristics that are constant and do not change during a study. They have no effect on other variables. Researchers might intentionally keep a control variable the same throughout an experiment to prevent bias. For example, in an experiment about plant development, control variables might include the amounts of fertilizer and water each plant gets. These amounts are always the same so that they do not affect the plants' growth. CONTROL VARIABLES
- 35. Extraneous variables are factors that affect the dependent variable but that the researcher did not originally consider when designing the experiment. These unwanted variables can unintentionally change a study's results or how a researcher interprets those results. Take, for example, a study assessing whether private tutoring or online courses are more effective at improving students' Spanish test scores. Extraneous variables that might unintentionally influence the outcome include parental support, prior knowledge of a foreign language or socioeconomic status. EXTRANEOUS VARIABLE
- 37. Quantitative variables are any data sets that involve numbers or amounts. Examples might include height, distance or number of items. Researchers can further categorize quantitative variables into two types: Discrete Continuous QUANTITATIVE VARIABLES
- 38. QUANTITATIVE VARIABLES Discrete Any numerical variables you can realistically count, such as the coins in your wallet or the money in your savings account. Continuous Numerical variables that you could never finish counting, such as time, Temperature etc
- 40. Qualitative, or categorical, variables are non-numerical values or groupings. Examples might include eye or hair color. Researchers can further categorize qualitative variables into three types: Binary Nominal Ordinal QUALITATIVE VARIABLES
- 41. Binary Variables with only two categories, such as male or female, red or blue. Nominal Variables you can organize in more than two categories that do not follow a particular order. Take, for example, housing types: Single-family home, condominium, tiny home. Ordinal Variables you can organize in more than two categories that follow a particular order. Take, for example, level of satisfaction: Unsatisfied, neutral, satisfied. QUALITATIVE VARIABLES
- 43. A confounding variable is one you did not account for that can disguise another variable's effects. Confounding variables can invalidate your experiment results by making them biased or suggesting a relationship between variables exists when it does not. For example, if you are studying the relationship between exercise level (independent variable) and body mass index (dependent variable) but do not consider age's effect on these factors, it becomes a confounding variable that changes your results. CONFOUNDING VARIABLES
- 45. A composite variable is two or more variables combined to make a more complex variable. Overall health is an example of a composite variable if you use other variables, such as weight, blood pressure and chronic pain, to determine overall health in your experiment. COMPOSITE VARIABLES