Chapter 14 - Quantitative Data Analysis
Introduction
After quantitative data have been collected from a representative sample of the population,
the next step is to analyze them to answer our research questions. First, we need to ensure
the data is accurate, complete, and suitable for further analysis.
Getting the data ready for analysis
After data are obtained through questionnaires, they need to be coded, keyed in, and edited.
Then, outliers, inconsistencies, and blank responses have to be handled in some way.
Coding and data entry
Data coding involves assigning a number to the participants’ responses so they can be
entered into a database. If for whatever reason, this cannot be done, then it is perhaps a
good idea to use a coding sheet first to transcribe the data from the questionnaires and then
key in the data.
Coding the responses
Human error can occur while coding. At least 10% of the coded questionnaires should,
therefore, be checked for coding accuracy. Their selection may follow a systematic sampling
procedure. That is, every nth for coded could be verified for accuracy.
Data Entry
After responses have been coded, they can be entered into a database. Raw data can be
entered through any software program. Each row represents a case or observation and each
column represents a variable.
Editing Data
After the data are keyed in, they need to be edited. Data editing deals with detecting and
correcting illogical, inconsistent, or illegal data and omissions in the information returned by
the participants of the study. An example of an illogical response is an outlier response.
Inconsistent responses are reponses that are not in harmony with other information. Illegal
codes are values that are not specified in the coding instructions. The best way to check for
an illegal code is to have the computer produce a frequency distribution and check it for
illegal codes. Omission may occur because respondents did not understand the question,
did not know the answer, or were not willing to answer the question.
Data Transformation
Data transformation, a variation of data coding, is the process of changing the original
numerical representation of a quantitative value to another value. Data are typically changed
to avoid problems in the next stage of the data analysis process. Data transformation is also
necessary when several questions have been used to measure a single concept.
Getting a feel for the data
We can acquire a feel for the data by obtaining a visual summary or by checking the central
tendency and the dispersion of a variable. We can also get to know our data by examining
the relationship between two variables. Descriptive statistics for a single variable are
provided by frequencies, measures of central tendency, and dispersion.
Introduction
After quantitative data have been collected from a representative sample of the population,
the next step is to analyze them to answer our research questions. First, we need to ensure
the data is accurate, complete, and suitable for further analysis.
Getting the data ready for analysis
After data are obtained through questionnaires, they need to be coded, keyed in, and edited.
Then, outliers, inconsistencies, and blank responses have to be handled in some way.
Coding and data entry
Data coding involves assigning a number to the participants’ responses so they can be
entered into a database. If for whatever reason, this cannot be done, then it is perhaps a
good idea to use a coding sheet first to transcribe the data from the questionnaires and then
key in the data.
Coding the responses
Human error can occur while coding. At least 10% of the coded questionnaires should,
therefore, be checked for coding accuracy. Their selection may follow a systematic sampling
procedure. That is, every nth for coded could be verified for accuracy.
Data Entry
After responses have been coded, they can be entered into a database. Raw data can be
entered through any software program. Each row represents a case or observation and each
column represents a variable.
Editing Data
After the data are keyed in, they need to be edited. Data editing deals with detecting and
correcting illogical, inconsistent, or illegal data and omissions in the information returned by
the participants of the study. An example of an illogical response is an outlier response.
Inconsistent responses are reponses that are not in harmony with other information. Illegal
codes are values that are not specified in the coding instructions. The best way to check for
an illegal code is to have the computer produce a frequency distribution and check it for
illegal codes. Omission may occur because respondents did not understand the question,
did not know the answer, or were not willing to answer the question.
Data Transformation
Data transformation, a variation of data coding, is the process of changing the original
numerical representation of a quantitative value to another value. Data are typically changed
to avoid problems in the next stage of the data analysis process. Data transformation is also
necessary when several questions have been used to measure a single concept.
Getting a feel for the data
We can acquire a feel for the data by obtaining a visual summary or by checking the central
tendency and the dispersion of a variable. We can also get to know our data by examining
the relationship between two variables. Descriptive statistics for a single variable are
provided by frequencies, measures of central tendency, and dispersion.
