How to Collect and Analyze Survey Data

How to Collect Survey Data

One of the key sources of quantitative data in sociology is surveys. Try online survey maker. There are three main survey types based on their format:

Telephone survey
Street survey
Online survey

Telephone Survey

This type of survey is essentially a phone interview. It can be conducted by calling both landlines and mobile phones. Such surveys require a high level of phone penetration among population. Their time frames are quite tight, so the interviewer faces an increased workload. There is specialized software in the market that automatically determines the sample and dials chosen respondents.

Advantages of a telephone survey:

Quick and cheap to carry out (less time and interviewers needed)
Reduced impact of the interviewer on the respondent, no third-party presence effect
Easy interviewer monitoring
Specialized software available

Limitations of a telephone survey:

Visual materials (images, audio, videos) cannot be displayed
Behavioral reaction cannot be recorded
The survey has to be short (no more than 10 minutes) and include simple questions with few hints
The interviewers need to be experienced, as they only have their voice to keep the respondents focused
Unrepresentative samples due to a high non-response rate
The optimal call time is limited: Tuesday, Wednesday, Thursday 5:30 p.m. to 9:00 p.m.
Lack of up-to-date phone directories for sampling

Online Survey

With this type of survey, respondents answer questions on their personal device — a computer, tablet, or smartphone. Survey invitations can be sent out to an existing contact list or to participants of a specifically curated survey panel.

Advantages of an online survey

Low costs, low labor intensity required
Prompt results: more than 60% of responses come within the first two days
Wide audience coverage
No interviewer influence
Visual materials (images, audio, videos) can be displayed
No time restraints to surveying
To conduct a survey, a preset survey panel can be used based on specific criteria
Sensitive topics (alcohol consumption, socially-objectionable behavior) can be studied
The incoming data can be processed automatically

Limitations of an online survey

Low response rate
No random sampling available
Sampling correction tools need to be used
Sampling mostly covers only specific categories of respondents
Low control over responses
Poor response quality due to respondent anonymity

Street Survey

It is a type of personal interview conducted in crowded areas: on busy streets, in malls, etc. Respondents are recruited from those passing by.

Advantages of a street survey

Fast to conduct
Easy to set precise respondents’ location
Visual materials (images, audio, videos) can be displayed
Behavioral reaction can be recorded

Limitations of a street survey

Random sampling
Interview lasts no more than 10 minutes
Hard to control interviewers’ work
Influence of weather conditions
Difficult to get a permit to conduct a survey in a mall

How to Analyze Survey Results

The scale to measure a variable determines the procedures available. In general, there are three types of scales used in sociology:

Nominal
Ordinal
Interval

Non-Quantitative Scales

Nominal Scale

The nominal scale identifies whether an object has a certain attribute. On this scale, different attributes cannot be ranked or put in order, as they cannot be compared in terms of “bigger–smaller” or “better–worse”.

Statistical procedures

Frequency
Contingency table

Ordinal Scale

The ordinal scale helps to identify whether an object has one of ordered attributes.

Statistical procedures:

Frequency
Mode
Median

Quantitative Scales

Interval Scale

On the interval scale, attribute values are ordered with equal intervals between them, and have an assigned measurement unit.

Interval scales do not have a true zero. The zero value is arbitrary and does not mean that the object does not have a measured attribute. Examples include calendars with different starting points, or temperature scales with different zero values (Celsius, Fahrenheit, Kelvin).

Since the attribute is measured in certain units, its values can be added or subtracted. Multiplication and division operations, however, do not make sense due to the lack of a true zero that would represent the absence of the attribute. For instance, we can say that it is 7 degrees hotter today than yesterday, but not X times hotter.

Ratio Scale

The ratio scale has all the same properties as the other scales, plus a true zero that represents complete absence of an attribute. Thanks to this feature, ratio scales can be used to compare different objects. For example, a respondent can be 1.5 times older, go to malls 2 times less often, or make several times more expensive purchases than other respondents.

Intervals between values do not have to be equal, but they need to have distinct boundaries. For instance, if one interval on an age scale ends on 30 years, the next one has to start with 31 years.

Statistical procedures for quantitative scales:

Frequency
Mode
Median
Mean
Variance
Standard deviation

Descriptive Statistics

The point of descriptive statistics is to describe the external features of the data.

Key indicators of descriptive statistics

Absolute frequency shows how many times a particular answer option has been chosen.

Relative frequency shows the share of a particular value in the total sample results.

PeakPoll survey builder automatically calculates absolute and relative frequencies for each answer option and presents information as pie charts and bar diagrams.

Cumulative frequency shows the share of answer options that do not exceed a certain value.

A mean is the sum of all answers divided by their quantity. It is used to determine the center or the midpoint of a row of data. It does not always objectively reflect the data structure, as it is easily influenced by outliers (uncharacteristically small or big values).

A median is a value that divides into halves a sequence of data presented in the ascending order. A median is another way to find the center of a data series which is not as sensitive to outliers as the mean.

A mode is the most frequent answer or the most typical value in a data set.

Variance shows the degree of answers’ deviation from the average value.

Standard deviation is a method of measuring the degree of deviation in a data set. In other words, it is the average interval from the average value. It is an important statistical indicator that should be analyzed along with the mean and the median. For instance, an average salary may not reflect the real situation at a company if the pay disparity is too high. That’s when standard deviation can come in handy.

Contingency Tables

With contingency tables, you can visualize the distribution of two variables and explore the relations between them. It is the most effective way to establish links between two nominal variables (for example, the gender and the consumption indicators for a certain product).

Correlation

When changes in the values of one variable coincide with changes in another one, it shows a correlation between them. However, it’s important to keep in mind that correlation does not mean causation.

When changes in one variable are directly proportional to changes in another one, it indicates linear correlation.

Linear correlation can be:

Positive (both variables increase or decrease)
Negative (one variable increases, and the other decreases)
Strong (correlation coefficient over 0.7)
Weak (correlation coefficient below 0.5)

The correlation coefficient range varies from -1 to +1. A zero correlation coefficient means that both variables are linear uncorrelated.

Correlation strength types

Very weak (correlation coefficient below 0.2)
Weak (correlation coefficient below 0.5)
Average (correlation coefficient below 0.7)
Strong (correlation coefficient below 0.9)
Very strong (correlation coefficient over 0.9)

To prove correlation or lack of it between two attributes, different statistical criteria are used depending on the variable type. They are:

Chi-squared test
Contingency coefficient
Kolmogorov–Smirnov (Lambda) test
Spearman’s Rho
Pearson correlation coefficient

Multidimensional Analysis

Multidimensional analysis enables researchers to explore correlation between two and more variables and test causation hypotheses.

In marketing, factor and cluster analysis procedures are the most common.

Factor Analysis

The key idea behind factor analysis is to boil down a large number of known variables to a fewer number of factors that define the differences between these variables.

The main objectives of factor analysis

Reduce the number of variables
Classify the data

For instance, you need to examine an employer’s brand. To do that, you ask respondents to rate the company based on more than 10 criteria. Then, using the factor analysis procedure, you group criteria into several key factors, such as “remuneration”, “labor conditions”, and “corporate culture”.

Another example is when you need to create a psychographic profile of a client. For that, you ask respondents to rate how much they agree with a set of statements on lifestyle. Then, you can apply the factor analysis procedure to find different client types — “innovative”, “progressive”, “conservative”.

Cluster Analysis

The main goal of cluster analysis is to group objects into several clusters. The objects within one cluster should be as similar as possible, while the clusters themselves should be as different as possible.

Cluster analysis outline

Select variables for clustering
Calculate the similarities and differences between answers
Select a clustering method (the rule for grouping objects)
Determine the optimal number of clusters
Start a cluster analysis procedure
Based on the produced results, define the profiles of representatives of each cluster

Areas of cluster analysis application

Sociology: building socio-demographic respondent groups
Marketing: segmentation of the target audience, grouping of competitors based on their competitive factors
HR: grouping employees based on their motivation drivers