_L2-PR2-12-1.pdf

PRACTICAL RESEARCH LESSON THREE: SOURCES OF RELATED LITERATURE AND STUDIES LESSON THREE:  ANALYZE THIS! A Variable is anything that has a quantity or quality that varies. EXAMPLE: For instance, during the quarantine period, your mother planted tomato seedlings in pots. Now common understand...

PRACTICAL RESEARCH LESSON THREE: SOURCES OF RELATED LITERATURE AND STUDIES LESSON THREE:  ANALYZE THIS! A Variable is anything that has a quantity or quality that varies. EXAMPLE: For instance, during the quarantine period, your mother planted tomato seedlings in pots. Now common understanding from science tells you that several factors are affecting the growth of tomatoes: sunlight, water, kind of soil, and nutrients in soil. How fast the tomato seedlings will grow and bear fruits will depend on these factors.  ANALYZE THEREFORE, The growth of tomatoes and the number of fruits produced are examples of the Dependent Variables. The amount of sunlight, water, and nutrients in the soil are the Independent Variables Let’s analyze this If there is an existing relationship between the independent and dependent variables, then the value of the dependent variable varies in response to the manipulation done on the independent variable. The independent variable is also identified as the presumed cause while the dependent variable is the presumed effect. In an experimental quantitative design, the independent variable is pre-defined and manipulated by the researcher while the dependent variable is observed and measured. It is important to note other factors that may influence the outcome (dependent variable) not manipulated or pre-defined by the researcher. These factors are called Extraneous Variables. In our example above, the presence of pests and environmental stressors (e.g. pets, extreme weather) are the extraneous variables. Since extraneous variables may affect the result of the experiment, it is crucial for the researcher to identify them prior to conducting the experiment and control them in such a way that they do not threaten the internal validity (i.e. accurate conclusion) of the result. Controlling the extraneous variable can be done by holding it constant or distribute its effect across the treatment. When the researcher fails to control the extraneous variable that it caused considerable effect to the outcome, the extraneous variable becomes a Confounding Variable. For example, if the tomato had been infested by pests (confounding variable) then you cannot conclude that manipulations in sunlight, water, and soil nutrients (independent variable) are the only contributing factors for the stunted growth and poor yield (dependent variable) of the plant or is it the result of both the independent variables and the confounding variable. The variables can also be classified according to their nature. The diagram on the next slide will shows the different classifications: QUATITATIVE VARIABLES also called numerical variables, are the type of variables used in quantitative research because they are numeric and can be measured. Under this category discrete and continuous variables. QUATITATIVE VARIABLES Discrete variables are countable whole numbers. It does not take negative values or values between fixed points. For example: number of students in a class, group size and frequency. Continuous variables take fractional (non- whole number) values that can either be a positive or a negative. Example: height, temperature. QAULITATIVE VARIABLES also referred to as Categorical Variables are not expressed in numbers but are descriptions or categories. It can be further divided into dichotomous, nominal or ordinal.  QUATITATIVE VARIABLES Dichotomous variable consists of only two distinct categories or values, for example, a response to a question either be a yes or no.  QUATITATIVE VARIABLES Nominal variable simply defines groups of subjects. In here, you may have more than 2 categories of equivalent magnitude. For example, a basketball player's number is used to distinguish him from other players. It certainly does not follow that player 10 is better than player 8. Other examples are blood type, hair color and mode of transportation.  QUATITATIVE VARIABLES Ordinal variable from the name itself, denotes that a variable is ranked in a certain order. This variable can have a qualitative or quantitative attribute. For example, a survey questionnaire may have a numerical rating as choices like 1, 2, 3, 4, 5ranked accordingly (5=highest, 1=lowest) or categorical rating like strongly agree, agree, neutral, disagree and strongly disagree. Other examples or ordinal variable: cancer stage (Stage I, Stage II, Stage III), Spotify Top 20 hits, academic honors (with highest, with high, with honors).