Volume merely indicates how much buy-side interest exists in a stock. For liquidity, the sell-side interest is more relevant, which implies the quote characteristics (the limit-order book).
In addition to the bid-ask spread, I look at the top-of-book quote size. Here's an example from BATS:
sym | bid ask bidsize asksize
AAPL| 325.12 325.21 100 100
MSFT| 24.70 24.71 3900 5900
I can only buy \$32,521 worth of Apple without impacting price, as opposed to \$145,789 of Microsoft. So the slippage is smaller.
There are more sophisticated measurements for order book entries. I could look at the full book ("level II data") to see the depth of the order chain. I could look across multiple exchanges, which is what a smart order router must do anyway. I could even look at related asset classes if the investor's goal is merely to gain exposure to general risk.
To be really swanky, I could investigate dark pools, though that's harder since the quotes aren't displayed. For this, a quant would need historical data regarding how much has been executed in the past. That's one reason why the big banks have a competitive advantage in dark-pool aggregator algorithms: they have enough client flow to record execution patterns.