Type of data and sampling Techniques

Type of data
1. Primary data are data collected directly by the researcher.
Example:
a. Actual observation or measurement
b. Interviews with questionnaires
c. E-mailing
d. Experimentation
e. Registrations
2. Secondary data are data collected through information taken from published or unpublished materials previously gathered by other researchers or agencies.

Sampling Techniques
There are two type of sampling techniques. These are probability and non-probability. Probability sampling deals with the selection of a sample from a population, based on the principle of randomization or chance. Non-probability sampling has no equal chance of selected.

1. Simple Random sampling. The member of the population has an equal chance to be included in the sample gathered. (Lottery or fishbowl techniques)

2. Systematic Random Sampling. A random starting point is selected and then every kth member will be succeeding sample. (Every 10th member is selected, sample can be 10, 20, 30 40 and etc)

3. Stratified Random Sampling. In this type of planning a population is first divided into subsets based on homogeneity called strata. Thus, in stratified random sampling, the strata are internally homogeneous as possible and at the same time each stratum is different from one another as much as possible. Then samples are selected proportionally from each stratum which can be done through simple or systematic random sampling. This type has an advantage in some cases to reflect more accurately the characteristics of the population. (groupings of similar nature, uniform appearance or composition)

4. Cluster Sampling. This can be done by subdividing the population into smaller units and then selecting only at random some primary units where the study would then be concentrated.

Categories of Quantitative Variables

Categories of Quantitative Variables
1. Discrete Variable refers primarily on the counting of integral values such as numbers of students, drop-outs, number of graduates, total employees, number of subjects and etc. (You simply count the data)
2. Continuous Variable deals with any numerical values over an interval/s that are assume. Examples are height, weight, age, and etc. (You put this on interval)

Scale of Measurement of Data
1. Nominal data deals with the use of numbers for the purpose of identifying name or membership in a group or category.
2. Ordinal data deals with ranking and inequalities.
3. Interval scales indicate an actual amount and there is equal unit of measurement separating each score, specifically equal intervals.
4. Ratio Data are similar to interval data, but has an absolute zero and multiplies are meaningful.

1.2. Variables and Data

The statistical data or informational data are accomplished with the used of questionnaires and checklist. The gathering of this data can be done in the following manner interviewing people, observing, or inspecting items. The studied characteristic is called variable. A variable is a characteristic that takes two or more values which varies across individuals.

The usual of variables used in psychological research are age, race, gender, intelligence, personality type, attitudes, or political or religion affiliation. Other examples of variables for people are height, weight, marital status, eye color and etc. Other variables measure how loud a voice is, how much you are paid to do a kind of work, how potent a drug you consume, and friendly someone acts toward you.

Two types of Variable or Data

1. Qualitative variables stand for differences in quantity, character, or kind but not in amount. This means that the variables deal primarily on the non-numerical form of data. Examples are gender, birthplace, geographical location and etc.

2. Quantitative Variables refers to numerical values. It can be in order of ranked. Examples are weight, height, age, grades, achievement test results and etc.

Introduction to Statistics

Statistics is the science of conducting studies to collect, organized, summarize, analyze, and draw conclusions from the data.

Classification fo statistics
1. Descriptive Statistics deals with the collecting, organizing, presenting, and analyzing numerical data,
2. Inferential Statistics or inductive analyze the organized data leading to prediction or inferences.