# Mass Media Research An Introduction Ninth Edition Roger D. Wimmer and Jospeh R. Dominic Chapter 04

## By: Kishwer Toor # MASS MEDIA RESEARCH AND INTRODUCTION NINTH EDITION ROGER D. WIMMER  JOSEPH R. DOMINIC CHAPTER 04SAMPLING

Sampling is the technique of choosing a representative subset of the population called sample. Sampling makes research more precise and efficient. It’s the sampling method which actually regulates the generalizability of the research findings. In simple words, the process of selecting a sample of the population to study is called

Sampling. A researcher should be explain population before to design research methodology, population may be a group and class etc. The process of examine every member in a population is called a census.

In sampling process researcher mainly face to two types errors which is sampling error and nonsampling error .Sampling error occurs during the selection of sample  while non sampling error is created by another aspect of research such as measurement error and data analysis error. Non sampling error is further divided in to systematic error  which Consistently produce invalid results in the same direction or same context, while random error  are caused by unknown and unpredictable variables the results lean in one direction when study repeated the results lean in opposite direction.

Probability Sampling used in mathematics where each unit have equal chance for selection .while nonprobability sampling does not follow the mathematical guidelines .The most significant difference is probability sampling allows researcher to calculate the amount of sampling error present in research study while nonprobability sampling does not.

Types of Probability Sampling

Simple Random Sampling: In which each element, subject and unit of population have equal chance of selection .Widely used in mass media research.

Systematic sampling: In a large population samples are selected according to a random starting point and a fixed periodic interval .This interval is called the sampling interval is calculated by dividing the population size by desired sample size.

Stratified Sampling: Researcher divide the entire population in to separate groups called strata then randomly select the group proportionally from the different strata. This approach used to get adequate representation of a sub sample( strata).

Multistage and Cluster Sampling: similar to the stratified sampling but the difference is that in stratifies sampling strata is randomly selected while in cluster sampling only the selected cluster are selected for sample.

Types of Nonprobability Sampling

Convenience Sampling: selection of sample from an entire population which are easy to approach.

Snow ball sampling:

In which existing subjects provide referrals to recruit samples required for a research study also known as chain and referral sampling. Mostly used in academic research in private sectors.

Quota Sampling: sample has the same proportions of individuals as the entire population with respect to known characteristics, traits or focused phenomenon.

Theoretical/ Purposive sampling: Sample is selected on the base of specific qualities and characteristics.

An accurate and adequate sample size is an important aspect of sampling .Selection of sample size depends at least one or seven factors which are *Project type*Purpose*Project complexity*amount of errors*time constraints *financial constraints and previous research. Due to these factors there are some general principals and guidelines for selection of sample size. If the scientific procedure provide valid and useful results researcher mast pay close attention to  the method they use in selecting sample .Sampling error (confidence level and confidence interval ) related to selection of sample ,,while standard error (computing) included statistical measurement etc. Sampling process must not be considered lightly in the purpose of scientific investigation. 