lesson 2

• VARIABLES


  • a characteristic or attribute of persons or objects which assume different values for different objects under consideration

  factors that can be manipulated and measured
• CLASSIFICATION OF VARIABLES


  Discrete and Continuous Variables

  Qualitative and Quantitative Variables

  Dependent and Independent Variables

  Univariable, Bivariable and Multivariable Distribution
• Discrete and Continuous Variables

  Discrete

  • can assume finite or at most countable infinite numbers of values
  • usually measured by counting or enumeration
  • ex. students, professors, psychologists, parents

  Continuous

  • those that cannot be counted because of their distinct division
  • “abstract variables”
  • can assume values corresponding to a line of interval
  • ex. intelligence, beauty, effectiveness, cleanliness
• Qualitative and Quantitative Variables

  Qualitative

  • can give categorical responses
  • ex. occupation, gender, civil status, religious

  Quantitative

  • takes on numerical values representing an amount or quantity
  • ex. height, salary, number of children, weight
• Dependent and Independent Variables

  Dependent

  • measures based on the effect of the independent
  • “outcome variable”

  Independent

  • variables that the researcher controls or manipulate in accordance with the purpose of the investigation
• • Univariable

  • there is only one variable involved
  • ex. age of grade 7 pupils
• Bivariable


  • data are classified on the basis of two variables
  • ex. an ice cream shop keeps track of how much ice cream they sell versus the temperature of the day
• Multivariable


  • each datum belongs to three or more variables
  • ex. the teacher would like to keep track the enrolment in the college in terms of program, year level, and gender
• MEASUREMENT


  • the process of determining the value or label of a particular variable for a particular individual or object on which variable is measured
• LEVELS OF MEASUREMENT


  Nominal Scale

  Ordinal Scale

  Interval Scale

  Ratio Scale
• Nominal Scale


  • has no numerical value
  • “categorical scales”
• Ordinal Scale


  • classifies subjects 

  also ranks them in terms of the degree to which they posses a characteristics of interest
• Interval Scale


  • has all the characteristics of a nominal and an ordinal scale but it is based upon predetermined equal intervals

  does not have true zero point
• Ratio Scale


  • represents the highest, most precise level of measurement
  • has a meaningful true zero point
• SAMPLE


  • a portion or subset of the population used to gather information from the population
  • truly represents the unique qualities or characteristics of the population
• POPULATION

  • the total or entire group of individuals, events, objects, observations, reactions to a certain stimuli that have unique patterns of qualities and from which information is desired by the researcher
  • “the universe”
• Probability Sampling

  • a sampling process where each unit in the population has known nonzero probability of being included in the sample
  • most unbiased but difficult method
• Simple Random Sampling

  • sample will be chosen randomly 

  each member in the population will have an equal chance of being selected
• Stratified Random Sampling

  samples are randomly selected from the different groups or sections of the population used in the study
• Systematic Random Sampling

  the method where every kth name in the list of the population members can be selected as part of the sample
• Cluster Sampling

  • the researcher identifies convenient, naturally occurring group units
  • unlike strata, it is advisable to form clusters with heterogeneous components
• Multi Stage Sampling

  • used when the respondents of the study are scattered all over a big geographical area such as for national, regional, provincial or country level studies
  • involves several stages in drawing the samples from the population
  • define the population
  • cluster the population
  • randomly select clusters
  • randomly sample units from within the selected clusters
• Non-Probability Sampling

  • a sampling process wherein probabilities of selection are not specified for the individual units in the population
  • when the researcher is not after generalizing the results of the study to the population or universe
• Purposive Sampling

  • the researcher selects those who can best help explain or give information based on his judgment
  • “judgmental selects”
• Convenience Sampling

  • the researcher selects respondents who are available at the time and place the data is to be collected
  • “haphazard or incidental sampling”
• Quota Sampling

  • to come up with the desired number of samples no matter how they are selected
• Snowball Sampling

  • used when respondents are difficult to identify and best located through referral networks
• PARAMETERS

  • measures of the population or numerical characteristics of the population

  “μ“
• Action Research

  when the researcher is interested in finding out whether something will work or problem solving in local setting
• Descriptive Research

  used when the researcher’s concern is to understand the nature, characteristics, components or aspects of a situation or phenomenon
• Explanatory Research


  • utilized when the researcher seeks to explain the relationship between two or more variables and predict relationships between and among these factors
• Exploratory Research


  • when the researcher is after uncovering data on a phenomenon little is known about
• Correlational Research

  investigates relationships between variables without researcher controlling or manipulating any of them
• Evaluation Research


  appropriate when the researcher plans to assess the impact, effect, result, or outcome of operations, policies and programs
• Policy Research

  • when the researcher is concerned about generating information relevant to the development and formulation of policy and the assessment of the effect of such policy
• EX-Post Facto Researcher

  • when the research is after observing existing conditions and looking back through the data for plausible causal factors

  “causal-comparative research”
• Historical Research


  when the researcher is attempting to solve certain problems arising out of historical context through the gathering and examining relevant data from the past
• Ethnographic Research

  to come up with a holistic description of phenomenon or situation with the use of multiple data collection techniques
• Phenomenological Research

  interprets an experience or fact, by listening to the different stories of the participants