HYPOTHESIS & VARIABLES

VARIABLE - property or quantity that can be taken on different values

  • INDEPENDENT VARIABLE - manipulated such that its

    values are chosen and set by the researcher.

  • DEPENDENT VARIABLE - pertains to behavior or

    change in characteristic of the subject during experimentation

TWO KINDS OF GROUP IN AN EXPERIMENT

  • EXPERIMENTAL GROUP - group of subjects that is exposed to a

    certain treatment.

  • CONTROL GROUP - exposed to the absence of the treatment.

How to turn non testable questions to testable questions?

  • read the non testable question carefully.

  • determine the cause (IV) and effect (DV) in the question.

  • recast the question by following the cause and effect format.

How can we Formulate Hypothesis?

  • Gathered information and objectives of the study must be the main basis.

  • Since there are many possible solutions to a problem, more than one hypothesis from a single information can be formed.

  • Check if the hypothesis you formulated is testable.

  • Make observations and do experiments that will support your hypothesis.

HOW TO STATE YOUR HYPOTHESIS

  • null statement - indicates the value of population parameter to be tested. This is known as hypothesis of "no difference". Usually, this type of hypothesis is formulated for the sole purpose of being rejected

  • alternative statement - operational statement of the experimenter's research hypothesis or the prediction derived from the theory being tested

  • cause and effect statement - reflects the connection of two variables of the main subject in the study

QUANTITATIVE VARIABLE/ NUMERIC VARIABLE- can take values corresponding

to the points on a real line

QUALITATIVE VARIABLE/ CATEGORICAL VARIABLE- are variables that are not numerical

LEVELS OF MEASUREMENT

  • NOMINAL SCALE - groups of the subjects when the values of the variables differ by category; counting and classification

  • ORDINAL SCALE - categories or labels, but have a natural order or ranking; intervals between categories are not necessarily equal or meaningful

  • INTERVAL SCALE - have a natural order but have equal intervals between values; lack a true zero point

  • RATIO SCALE - have all the characteristics of interval data, plus they have a true zero point (complete absence of the quantity being measured)